Category: Blog

Learnings From Building a Multilingual AI Support System with Guided Chat and RAG

Customer support systems rarely fail because of a lack of documentation. They fail because users cannot find the right answer when they need it.  When we partnered with a global live-streaming platform focused on gaming, entertainment, and creator-driven content, their support model had reached that exact breaking point. Every support request—whether it was a simple FAQ or a complex account issue—entered a human agent queue. The average resolution time was around eight minutes per case, and as the platform expanded globally, costs and inconsistencies grew quickly.  The goal was clear: reduce support costs, improve response accuracy, and scale support across multiple languages without degrading quality.  To achieve that, we redesigned the support system from the ground up.    The Problem: Knowledge Exists, But Retrieval Fails  The platform already had a large knowledge base covering most common user issues. The problem was not a lack of information—it was information retrieval.  We identified several structural problems:  Every support request required a human agent, even when the answer already existed in documentation.  The knowledge base was difficult to navigate for the customer support agents, especially across languages.  Users frequently left the platform to search externally for answers.  Support quality varied depending on the language and agent assigned.  The existing infrastructure could not scale with demand.  At its core, the support system lacked the ability to reliably deliver the right information, to the right user, in the right language, at the right moment.  This is precisely the type of problem where retrieval-augmented generation (RAG) can work—if implemented carefully.    Architecture Overview  We built a RAG-powered guided chat system integrated with Salesforce that combines knowledge retrieval, multilingual support, and human escalation.  The architecture includes five core components:  Knowledge ingestion and structuring  Retrieval and context construction  Language-aware query handling  LLM response generation  Continuous evaluation and monitoring  Each component solved a specific failure point in the original support system.    Structured Knowledge Ingestion  Most RAG failures begin with poor data preparation.  The platform’s knowledge base consisted primarily of HTML documentation containing tables, FAQs, and step-by-step guides. Standard chunking approaches often break these structures apart, producing fragmented retrieval results.  To address this issue, we built a structure-preserving chunking engine.  Instead of blindly splitting documents by token length, the system:  Detects structural elements like tables and FAQ blocks  Preserves semantic groupings  Generates retrieval chunks that maintain instructional context  This ensures that when the system retrieves content, it returns complete, usable answers instead of disconnected fragments.    Multilingual Retrieval Without Duplicating Knowledge Bases  The platform supports users across seven languages, but challenge was to maintain separate documentation sets and that would have created massive operational overhead.  Instead, we implemented language-aware retrieval using:  AWS Translate for query normalization  AWS Bedrock Knowledge Base for retrieval orchestration  The workflow works like this:  A user asks a question in their native language.  The query is normalized and translated for retrieval.  Relevant knowledge is retrieved from the shared knowledge base.  The final answer is generated and delivered in the user’s language.  This approach enables true multilingual support without duplicating documentation.    Guided Chat and Intelligent Escalation  Automation should handle routine questions, but not everything can—or should—be automated.  We designed the system as a guided support experience, not just a chatbot.  The system includes predefined triggers for escalation when:  Query intent is ambiguous  The retrieved knowledge confidence is low  The request involves sensitive account actions  When escalation occurs, the case is routed to a human agent with full conversation context, eliminating the common frustration of repeating the problem after transfer.  This creates a hybrid support model where automation handles scale and humans handle complexity.    Observability: Evaluating RAG in Production  Many AI systems perform well in controlled testing but degrade in production.  To prevent this, we implemented continuous evaluation using Ragas, with metrics stored in DynamoDB.  The system measures:  Faithfulness – whether responses remain grounded in retrieved knowledge  Relevancy – whether the answer addresses the user query  Context utilization – whether retrieved context is actually used  This evaluation pipeline runs continuously, providing real-time insight into system quality rather than relying on static evaluation sets.  Before rollout, we also load-tested the system to handle approximately 7,500 requests per minute.    Results in Production  After deployment, several improvements became immediately visible.  Answer accuracy improved across all seven languages.  Because responses were grounded in documentation rather than agent interpretation, the system eliminated much of the inconsistency that previously existed between languages.  Routine support requests became automated.  High-volume issues such as FAQs and documentation-based questions were resolved instantly, reducing agent workload significantly.  User behavior changed.  Instead of leaving the platform to search for answers externally, users began resolving issues directly within the support experience.  Operationally, the system delivered three key outcomes:  Lower support costs  Faster response times  Consistent multilingual support  Most importantly, the platform gained something it previously lacked: observability into support performance.  For the first time, support quality could be measured and improved continuously.    Beyond Customer Support  The architecture we built is not limited to support systems.  The same pattern applies to many enterprise problems:  Internal knowledge assistants  Developer documentation search  Operations runbooks  Enterprise workflow automation  In all of these cases, the core challenge is the same:  Retrieve the right knowledge, apply the right context, and deliver the right answer.   

The Quiet Revolution: How OTT Platforms Are Using GenAI to Eliminate Technical Debt

Ask any CTO at a major OTT platform what keeps them up at night, and you’ll rarely hear ‘content generation with GenAI’. Instead, the answer is usually something far more mundane. It’s the legacy code that’s been piling up for years—code that nobody on the current team really understands anymore. It’s the thousands of customer support tickets flooding in every day, burying human agents. Or it’s those bloated tech systems that take forever to run and are draining a million dollars a year from the budget.  After working closely with these platforms over the past couple of years, I’ve witnessed a fundamental shift in how streaming companies approach Generative AI. Rather than chasing the headline-grabbing applications, they’re using it to automate the herculean operational tasks that have historically consumed enormous resources and slowed innovation to a crawl.  Here’s what that transformation actually looks like on the ground.  Engineering Modernization: Decoding Digital Archaeology  Most major OTT platforms are built on years, and sometimes even decades, of accumulated legacy code. Upgrading a backend stack or migrating logic to modern languages traditionally meant 12-18 months of painstaking work: risky, expensive, and frankly soul-crushing for developers who’d rather build new features.  The transformation happening now is striking. Using tools like Copilot and Cursor, engineering teams are:  Decoding legacy systems in seconds – GenAI analyzes codebases that predate current staff, explaining undocumented logic written by developers who left years ago. What once required weeks of archaeology now takes minutes.  Compressing timelines dramatically – Full-stack modernization projects that consumed 12+ months are now being completed in weeks. Version upgrades, security patching, and architectural refactoring that earlier required dedicated teams can now happen continuously.  Accelerating new development – GenAI generates initial code from requirements, which developers then refine. This allows complete application re-development and re-architecture with fewer resources and compressed timelines.  The benchmark that’s emerged? Most platforms have set a challenging target of improving developer productivity by at least 40% through GenAI-assisted code migration, debugging, testing, and security remediation. It’s ambitious, but the early data suggests it’s achievable.  From War Rooms to Self-Healing Systems  In traditional OTT operations, you only find out something’s broken when it’s already too late; ideally, it starts with a dashboard that flashes red and worsens when frustrated users start complaining on social media. That’s when teams finally rush into a war room and start the painful process of manually digging through logs across all your different systems, trying to track down what went wrong. By the time you figure it out, your viewers have already had a lousy experience.  Leading platforms are now implementing AI-driven observability that transforms this equation. Intelligent agents continuously analyze logs across legacy applications, and when anomalies surface, they:  Detect the anomaly before users notice Identifythe root cause through historical pattern analysis  Push actionable alerts with remediation suggestions directly into collaboration tools The ultimate goal? Creating feedback loops where systems eventually self-heal by automatically applying fixes based on past successful resolutions. We’re not there yet, but the foundation is being laid.  Quality Engineering: Trimming the Monster  When you build software incrementally over the years, your test suite inevitably becomes unwieldy. I’ve seen platforms with tens of thousands of test cases where 20-30% are redundant, outdated, or conflicting—the accumulated detritus of rapid development cycles and team turnover.  Rather than forcing quality engineers to audit this mess manually, GenAI is being deployed to:  Deduplicate intelligently – Identify and eliminate redundant tests that provide no incremental coverage.  Auto-generate based on requirements – Create comprehensive test scripts directly from updated specifications.  Maintain continuous optimization – Keep test suites lean and fast, enabling deployment velocity that was previously impossible.  Customer Support: Creating Super-Agents  There’s a persistent misconception that GenAI in customer support means replacing human agents. What I’m seeing tells a different story: it’s about dramatically amplifying what humans can accomplish.  Currently, resolving even simple issues often requires agents to log into five or more separate backend systems (payment processors, user databases, content delivery networks, subscription management platforms, etc.) to understand why a customer’s billing failed or their stream is buffered.  GenAI is replacing that friction with natural language interfaces. Now, agents are empowered; they simply ask, ‘Why was this user’s last payment declined?’ and receive instant, contextualized answers pulled from across the entire system architecture.  The impact is measurable: considerably faster ticket resolution and significantly reduced onboarding time for new agents who no longer need months of training on complex internal tools and systems.  Operations Beyond Streaming: The Theme Park Challenge  For OTT platforms that also operate physical entertainment properties, such as theme parks & resorts, the operational challenge extends beyond digital infrastructure. Consider the feedback loop: 1,000+ guests comment daily, each requiring human review, profanity filtering, categorization, and routing.  GenAI automation is transforming this process:  Intelligent categorization – Automatically tagging comments by department and issue types (e.g., food quality, ride safety, ticketing problems, etc.)  Sentiment and urgency analysis – Distinguishing between general dissatisfaction and situations requiring immediate management intervention.  Smart aggregation & routing – Grouping related issues by department and sending consolidated reports instead of overwhelming managers with hundreds of individual notifications.  Internal Tools: Eliminating the ‘Tech Tax’  One of the most insidious productivity killers in large organizations is the complexity of internal systems. Enterprise resource planning platforms, HR portals, project management tools: each with its own arcane interface, each requiring specialized knowledge to navigate effectively.  Problems that should take minutes to resolve (pulling a budget report, checking project status, verifying approvals) often consume days as requests bounce between departments and specialists who know which obscure menu to access.  Leading platforms are now deploying secure internal GPTs that act as unified interfaces. Employees ask questions in natural language; the system queries the relevant backend platforms and returns answers. It’s about eliminating what I call the ‘tech tax’: the enormous time cost of simply doing business in a complex organization.  What’s Coming: The 2026 Horizon  While current deployments focus on operational excellence, the roadmap for 2026 shows platforms preparing to tackle more customer-facing applications:  AI-powered media planning – Multi-agent systems handle end-to-end advertising workflows, including campaign planning, setup, optimization, and reconciliation, with minimal human oversight.  Natural language content discovery – Enabling users to find content through conversational queries rather than precise keywords. ‘Show me something funny but not too long’ or ‘find that cooking show we watched last month’ become valid search inputs. Mood-based and time-based search that understands context.  Licensed character content generation – Disney’s recent three-year agreement with OpenAI exemplifies this shift. Using Sora, consumers will create short AI-generated videos featuring over 200 licensed characters from Marvel, Pixar, and Star Wars, with selected content potentially showcased on Disney+. This moves GenAI from being an operational tool to a creative consumer platform.  The Real Revolution Is the One Most People Don’t See  The public conversation around GenAI in media still centers on creativity: new content formats, personalized experiences, and AI-generated media. But inside OTT platforms and media organizations, the most meaningful transformation is happening elsewhere.  GenAI is being applied to the most complex, least visible problems

Anthropic’s Enterprise Revolution: Why Claude 5, Cowork, and the Legal Plugin Are Game-Changers for Business

The Enterprise AI Landscape Just Shifted Anthropic has done something remarkable. In the span of just a few weeks, the AI company has transformed from a model provider into a full-fledged enterprise platform company. For business leaders watching the AI space, this is the moment to pay attention. Three announcements are reshaping what is possible: Claude 5 – The next-generation model is imminent Claude Cowork Plugins – Role-specific AI automation for every department The Legal Plugin – A groundbreaking tool for in-house legal teams Let us break down why each of these matters for your organization. Claude 5: Smarter, Faster, More Affordable Leaks indicate that Claude Sonnet 5 (codenamed “Fennec”) could arrive as early as this week. Early testing suggests it will deliver performance on par with or exceeding Claude Opus 4.5 – at roughly 50% lower cost. For enterprises, this means: Better ROI on AI investments – More capability per dollar spent Faster workflows – Speed improvements without sacrificing quality Competitive edge – Access to frontier intelligence at mid-tier pricing The “better and cheaper” trend in AI is accelerating, and Anthropic is leading the charge. Organizations that adopt Claude 5 early will see immediate productivity gains across their AI-powered workflows. Claude Cowork: Your AI Operating Layer Launched on January 30, 2026, Claude Cowork represents Anthropic’s vision of AI as a true collaborator rather than just an assistant. Scott White, Anthropic’s head of enterprise product, described it perfectly: this is “a transition for Claude from being a helpful sort of assistant to a full collaborator.” What Makes Cowork Revolutionary Anthropic has open-sourced 11 role-specific plugins Sales – Pipeline management, prospect research, follow-up automation Finance – Analysis, reporting, forecasting support Marketing – Campaign planning, content workflows, analytics Data Analysis – Complex queries, visualization, insight generation Customer Support – Ticket triage, response drafting, escalation Project Management – Task coordination, status tracking, team alignment Legal – Contract review, compliance, NDA management Biology Research – Literature review, experiment planning Each plugin bundles the skills, integrations, and workflows specific to that job function. But here is the key: you can customize them for your company’s specific tools, terminology, and processes. Enterprise-Ready Today Cowork plugins are available now for Claude Pro, Max, Team, and Enterprise subscribers – no CLI expertise required. Installation happens directly in the app. For IT leaders, this means deploying sophisticated AI automation without extensive development resources. The Legal Plugin: A Category-Defining Moment The Legal Plugin deserves special attention. Released February 2, 2026, it is already sending shockwaves through the legal technology market. What It Does Contract Review – Clause-by-clause analysis with risk flagging (GREEN/YELLOW/RED) NDA Triage – Rapid assessment and prioritization of agreements Compliance Workflows – Automated tracking and monitoring Redline Generation – Suggestions based on your organization’s negotiation playbook Seamless Integration The plugin connects to the tools your legal team already uses: Microsoft 365 Slack Box Egnyte Jira This is not a standalone tool that creates another silo – it is an intelligent layer that enhances your existing workflows. Why This Matters for Business In-house legal teams are perpetually stretched thin. Contract review backlogs delay deals. Compliance monitoring consumes senior attorney time. The Legal Plugin addresses these pain points directly. Important note: Anthropic has been clear that this plugin assists with legal workflows – it does not provide legal advice. AI-generated analysis should always be reviewed by licensed attorneys. This responsible approach actually increases trust in enterprise deployments. The Bigger Picture: Anthropic’s Enterprise Strategy With 80% of Anthropic’s business coming from enterprises, these announcements represent a strategic doubling down on business users. The Model Context Protocol (MCP) underpinning these plugins is an open standard, meaning: Third-party integrations will proliferate Custom plugins can be built for any workflow The ecosystem will grow rapidly Claude Code’s success – reportedly generating $1 billion in revenue as “the fastest-growing product of all time” – proves Anthropic can deliver tools that businesses actually use and pay for. What Business Leaders Should Do Now Evaluate your current AI deployment – Are you positioned to take advantage of Claude 5’s price/performance improvements? Identify high-impact workflows – Which departments (legal, sales, marketing, support) would benefit most from role-specific AI automation? Start with Cowork plugins – The open-source plugins provide a low-risk entry point for experimentation Engage your legal team – The Legal Plugin could transform contract management and compliance workflows Plan for customization – The real value comes from tailoring plugins to your organization’s specific processes Conclusion Anthropic is not just releasing better models – they are building an enterprise AI platform that meets businesses where they work. Claude 5 promises frontier performance at accessible prices. Cowork plugins bring role-specific intelligence to every department. The Legal Plugin demonstrates what is possible when AI is designed for specific professional workflows. For business leaders, the message is clear: the era of AI as a true enterprise collaborator has arrived. The organizations that embrace these tools today will be the ones setting the pace tomorrow.   Sources TechCrunch: Anthropic brings agentic plug-ins to Cowork – https://techcrunch.com/2026/01/30/anthropic-brings-agentic-plugins-to-cowork/ Axios: Anthropic bolsters enterprise offerings – https://www.axios.com/2026/01/30/ai-anthropic-enterprise-claude com: Anthropic Releases Legal Plugin – https://www.law.com/legaltechnews/2026/02/02/anthropic-releases-legal-plugin-in-cowork-among-other-extensions-for-enterprise-work/ Legal IT Insider: Anthropic unveils Claude legal plugin – https://legaltechnology.com/2026/02/03/anthropic-unveils-claude-legal-plugin-and-causes-market-meltdown/ Dataconomy: Anthropic Fennec Leak – https://dataconomy.com/2026/02/04/anthropic-fennec-leak-signals-imminent-claude-sonnet-5-launch/ LawNext: Anthropic Legal Plugin Analysis – https://www.lawnext.com/2026/02/anthropics-legal-plugin-for-claude-cowork-may-be-the-opening-salvo-in-a-competition-between-foundation-models-and-legal-tech-incumbents.html SiliconANGLE: Claude Cowork plugins – https://siliconangle.com/2026/01/30/anthropic-debuts-claude-cowork-plugins-help-users-automate-tasks/ GitHub: Anthropic Knowledge Work Plugins – https://github.com/anthropics/knowledge-work-plugins

From reactive to predictive: an AI agent-powered early warning system for future-ready manufacturers

Every year, OEMs lose billions to avoidable failures — not because the data wasn’t there, but because no one saw it in time. In Europe’s manufacturing ecosystem, the equipment you sell today enters a complex, high-stakes aftermarket ecosystem. From spare parts planning to warranty claims and service calls, the aftermarket service lifecycle often determines not just profitability but also reputation and brand trust. Yet too many Original Equipment Manufacturers (OEMs) remain trapped in the reactive model, responding to failures. The real opportunity lies in predicting them before they impact customers. Why the reactive model is broken Across the manufacturing sector, warranty and support costs regularly consume 2–5 % of revenues. At that scale, doing nothing to anticipate issues is simply not viable. Traditional workflows run reactively: a problem becomes visible only after an owner complains, a dealer raises a repair order, or a claim is submitted. By the time those issues arise, the damage is often already done; customers are inconvenienced, brand trust is eroded, and supply-chain disruptions are underway. At Tavant, we observe the same pattern repeat itself over and over: teams spend 80% of their time identifying the issue and only 20% actually resolving it. Progress slows because the information they need is scattered across dealer repair orders, call-center notes, IoT logs, parts movements, technical service records, social posts, even photos and audio. Most of this data is unstructured (free text, images, PDFs), spread across multiple European languages, and crucially, much of it never connected back to the manufacturer at all. This is the leakage that keeps organizations on the back foot, and it’s precisely the gap an Early Warning System (EWS) is designed to close. What “Early Warning System” really means A condition materializes (the true starting point), long before anyone is aware. If the owner notices, they may go to a shop. The shop decides whether the issue is covered; if not covered, the signal often never reaches the manufacturer, resulting in lost data. Even when it does, it can be weeks or months after the first hints appeared, in call transcripts on social media or in error-code streams. Two things must be fixed: Latency: shrink time-to-awareness between occurrence and OEM visibility. Leakage: capture signals that currently die in dealer systems, local files, and informal channels. The response is not another dashboard. It is a data and decision fabric designed to bring signals forward and convert them into timely action. The architecture of proactive service Tavant’s approach is straightforward and proven in aftermarket and service-heavy environments: Unify the data you already own Bring dealer repair orders, customer calls, warranty claims, IoT/telematics, parts consumption, service/TSB records, and social feedback into a central service data hub with connectors and APIs to your core systems (SAP, Jira, survey platforms, and others). Think of this as creating an always-on “context layer” for service. Enrich what’s messy A GenAI layer cleans the input, resolves entities (such as products, causal parts, and customers), translates multilingual text, corrects typos and free text, and transcribes audio. This is the difference between reading thousands of unstructured notes and receiving decision-ready signals. Correlate and detect patterns Analytics models (including forecasting, trend detection, Pareto analysis, and anomaly detection) examine multiple sources to identify emerging issues, rather than simply confirming what’s already visible. For field teams, the output is intuitive: failure clusters grouped by product/series, causal part, geography, or symptoms. Prioritize, then route Every cluster is scored for risk and impact, so engineering, quality, and service leaders focus on what matters now. Workflows push each item through different stages (Detect → Investigate → Monitor → Close), creating a single trail for corrective actions, countermeasure validation, and (when needed) campaigns or recalls. The system surfaces the business outcomes quality leaders care about most: Data Enrichment Market impact ($) Failure Rate % Per Incident cost ($) Priority Ranking Root Cause Determination Part Consumption Counter Measure Validation Causal Part Identification Campaign Planning The result is not just speed, it’s consistency. When service teams see the same cluster, the same severity score, and the same trendline, debate narrows to what to do next. Success Story: Proof that predictive beats reactive A large engine OEM centralized more than 98,000 claims and applied AI-driven workflows with this approach. The outcomes: >83% of claims are auto-approved by rules, cycle time is reduced from weeks to hours, throughput increases with a flat headcount, and customer satisfaction rises from 30% to 83%. These kinds of results, which we’ve seen in implementations globally, demonstrate that the investment in predictive service isn’t just about cost‑avoidance; it’s about unlocking growth. Read more Why this matters for European Manufacturers Early warning isn’t just a cost story; it’s a resilience and regulatory story: Multilingual operations: Enrichment and translation reduce friction across Europe’s service footprint, normalizing technician notes and customer language into usable signals. Safety and brand protection: Faster triage creates earlier visibility for potential safety issues, critical in markets with stringent product-safety regimes and rapid consumer-protection escalation. Sustainability and circularity: When you identify defects sooner, you avoid scrap, rework, and excessive parts consumption, supporting European sustainability goals while protecting gross margin. Customer experience at scale: Prioritized clusters help you address the right issues first, improving first-time-fix, reducing repeat visits, and increasing CSAT, especially valuable for pan-EU service networks. Conclusion For European manufacturers, the ability to pivot from reactive support to predictive service is no longer optional; it’s critical. By embracing a modern AI-powered Service Lifecycle Management (SLM) solution, OEMs, Suppliers, Dealers, and Distributors can connect their aftermarket operations into a single, coherent lifecycle, enrich and interpret their service data intelligently, and act faster, smarter, and with greater customer focus. The result? Fewer failures. Faster resolution. Stronger customer trust. And a service operation that delivers growth, not just cost-cutting. If you’re still waiting for the next service call to appear, you’re already one step behind. Now is the time to modernize. Explore Tavant’s SLM solution suite and learn more about how AI-powered Service Lifecycle Management is transforming aftermarket operations: learn more. This article was originally published by Tavant on The Manufacturer.

From Data-Driven to Intention-Aware Banking: The Next Frontier in Financial Intelligence

The Evolution of Data in Banking For more than a decade, the financial industry has been on a mission to become data-driven. Banks have invested billions in analytics, artificial intelligence (AI), and customer data platforms to understand their customers better. The goal has been clear — leverage data to drive smarter decisions, optimize processes, and personalize services. However, the landscape is changing rapidly. Simply being data-driven is no longer enough. As customer expectations evolve and technology advances, the next leap forward for financial institutions is becoming intention-aware. What Does “Intention-Aware” Mean? An intention-aware bank goes beyond understanding what customers are doing — it understands why they are doing it. This means identifying not just the transaction patterns, but the underlying motivations, life events, and emotional drivers that shape financial behavior. For instance: A sudden increase in savings might signal preparation for a major life event like a home purchase. Frequent credit card use at specific merchants could indicate lifestyle changes or new financial priorities. A pause in digital engagement may reflect life stressors or financial uncertainty. By interpreting these signals, banks can anticipate customer needs and respond with empathy and precision — offering relevant advice, timely products, and proactive support. The Shift: From Data-Driven Insights to Contextual Understanding Traditional data-driven banking focuses on what happened — analyzing past behaviors to predict future actions. Intention-aware banking shifts this lens toward context — understanding why something is happening right now. This evolution requires integrating multiple layers of intelligence: Behavioral Analytics: Identifying patterns across transactions, channels, and devices. Contextual Data: Adding environmental, location-based, and temporal data for richer insights. Emotional Intelligence: Leveraging sentiment analysis, social listening, and NLP to interpret customer tone and intent. Predictive and Prescriptive AI: Moving from reactive responses to proactive recommendations and decision support. Together, these dimensions empower banks to serve customers not as data points, but as dynamic individuals with evolving intentions. Why Intention-Aware Banking Matters Enhanced Personalization Customers today expect hyper-personalized experiences — not just in offers, but in timing, tone, and channel. Intention-aware systems allow banks to reach the right person, with the right message, at the right moment. Proactive Financial Wellness Instead of waiting for customers to ask for help, banks can proactively guide them toward better financial outcomes — alerting them before overdrafts, suggesting investment opportunities, or identifying early signs of financial stress. Stronger Customer Trust and Loyalty By anticipating needs and offering meaningful solutions, banks build emotional loyalty that goes beyond transactional relationships. Customers begin to see their bank as a trusted financial partner. Operational Efficiency and Risk Reduction Intention-aware AI can improve fraud detection, credit scoring, and compliance monitoring by understanding user intent behind transactions — reducing false positives and operational inefficiencies. The Role of AI and Data Ethics Transitioning to intention-aware banking requires responsible AI practices. Customer consent, data privacy, and ethical transparency must form the foundation of every predictive and contextual system. The goal is augmentation, not intrusion — helping customers make better choices while respecting their autonomy. The Road Ahead Becoming intention-aware isn’t just a technological upgrade; it’s a strategic and cultural transformation. It calls for: Unified Data Platforms that integrate behavioral, transactional, and contextual data in real-time. AI-Driven Experience Engines that dynamically personalize interactions. Human-Centered Design that prioritizes empathy and transparency in every engagement. As the banking ecosystem evolves, those who can interpret not just data but human intention will define the future of financial experiences. Conclusion Data-driven banking was about insight. Intention-aware banking is about understanding. The institutions that can bridge this gap — blending data, AI, and human empathy — will lead the next generation of intelligent, customer-first financial services.

Context Engineering vs Prompt Engineering: What’s More Critical for AI-Driven Testing?

The emergence of Artificial Intelligence, especially in the form of Large Language Models (LLMs), has generated innovative ideas in the field of Software Testing. AI is now being used to generate and automate test cases, proving to be a valuable aid for quality engineers. As teams incorporate GenAI into their workflows, a crucial question arises: Is prompt engineering the key to productivity, or is it context engineering? Let’s unpack both and see why context engineering might hold the key to scalable, intelligent, and reliable AI-driven testing. Prompt Engineering: Quick Results, Limited Depth Prompt engineering is the craft of writing instructions or questions tailored to get the best response from an AI model. In software testing, this often looks like: “Write 10 boundary test cases for a login form.” “Generate Selenium code to test a shopping cart checkout.” “Summarize this test suite for product owners.” Prompting is flexible and magical for rapid experiments. However, its effectiveness depends heavily on the exact phrasing, making it useful for quick tasks but less consistent in structured, repeatable environments. Challenges include: Reliance on explicit information in the prompt. Struggles with domain-specific logic and evolving business rules. Prompt engineering excels at: Quickly generating edge case scenarios. Converting requirements to test steps. Producing test data for negative testing. Context Engineering: The Key to Scalable AI Context engineering is the discipline of designing the environment in which an AI operates. This means supplying the model with relevant metadata, documents, historical test cases, business rules, and logs- everything it needs to see the big picture before generating a response. Instead of just prompting “Write a test case for checkout failure,” context engineering equips the AI with prior test cases, detailed product documentation, and system logs. The result: AI-generated test cases are traceable, relevant, and context-aware. Benefits of testing include: Understanding domain-specific rules (e.g., financial, healthcare compliance). Automatically updating test cases as user stories evolve. Correlating bugs to test results and code commits. Context engineering enhances AI’s capabilities, enabling it to align testing with business logic and minimize manual oversight. Why Context Matters Most Software testing demands coverage, accuracy, risk mitigation, and accountability—not just content generation. Context engineering stands out because it: Ground AI responses in real system knowledge, reducing hallucinations. Enables reusability across test scenarios, releases, and environments. Improves traceability to requirements and defects. Supports domain-specific tuning for different industries. Prompt engineering may impress during demos, but context engineering delivers resilience in production environments. Best Practice: Use Both, But Prioritize Context Prompting offers precision, while context provides depth. For teams building AI-augmented testing frameworks, long-term value lies in investing more into context. Steps to get started: Ingest requirements, previous test cases, architecture diagrams, user flows, and defect logs into a context repository. Define structured schemas for AI to access and interpret these assets. Layer targeted prompts on this solid foundation. Think of it this way: Prompting tells the AI what to do; context tells it how and why. Practical Implementation for Test Teams To operationalize context engineering: Start by collecting core test assets (requirements, past test cases, architecture, user flows, defects). Build a context repository accessible by your LLM. Pair with focused prompts, such as “Generate regression cases for changed modules” with the AI referencing release and dependency histories. Always validate AI outputs. Human oversight ensures accuracy and aligns results with business objectives. Summary As GenAI continues to evolve, testers who embrace context engineering will go beyond simple automation—they’ll become curators of intelligence in the software lifecycle. It’s not about asking better questions; it’s about making the AI smarter before you ask. And in a world where speed meets complexity, that might be the competitive edge your testing practice needs.

Balancing Shift-Left and Shift-Right Testing for Optimal Software Quality

In the world of software development, testing is no longer a one-size-fits-all approach. The traditional “test at the end” mindset has given way to two powerful strategies: Shift-Left Testing and Shift-Right Testing. What Are We Even Talking About? Let’s cut through the jargon. Shift-Left Testing is all about moving testing earlier in the software development lifecycle (SDLC).Instead of waiting until the later stages, testing activities are integrated from the beginning, often during requirements gathering and development. This approach helps catch defects early, improves collaboration between developers and testers, enables test automation, and reduces rework. Shift-Right Testing focuses on testing in production or post-deployment environments. This approach helps ensure that applications perform well under real-world conditions and adapt to user behavior. These aren’t competing philosophies—they’re complementary approaches that, when appropriately balanced, create a robust quality assurance strategy. The Shift-Left Advantage: By shifting testing left, you can: Catch defects early, which is cheaper and faster Improve collaboration between developers and testers Enable test automation, making it a standard practice Reduce rework, preventing late-stage surprises How to Implement Shift-Left Testing? To implement Shift-Left Testing, try these strategies: TDD (Test-Driven Development): Write tests before writing code Early Performance & Security Testing: Identify bottlenecks and vulnerabilities early Static Code Analysis: Use automated tools to check code quality during development Collaboration Between Devs & Testers: Testers participate in sprint planning and reviews The Shift-Right Reality Check: By shifting testing right, you can: Test real user experience, understanding how the app behaves in real usage Monitor and observe failures, detecting issues that traditional testing might miss Improve system resilience, simulating failures and measuring system recovery Enhance feature rollouts, using techniques like A/B testing and canary releases How to Implement Shift-Right Testing? To implement Shift-Right Testing, try these strategies: Real-Time Monitoring & Logging: Use tools like New Relic, Datadog, or Prometheus Chaos Engineering: Deliberately break parts of the system to test resilience Canary Deployments: Release features to a small group before full deployment Feature Toggles: Enable or disable features dynamically without redeployment Finding Your Balance: So how do we combine these approaches? The sweet spot varies by organization, but here’s what we’ve found works well: Start with shift-left fundamentals: Unit tests, code reviews, and automated testing should be non-negotiable parts of your development process. Build a continuous testing pipeline: Automation across environments gives you confidence at each stage. Implement feature flags: These allow you to test new features with limited user exposure before full rollout. Monitor and observe: Real-time monitoring in production catches issues as they emerge. Establish feedback channels: Make it easy for users to report problems and suggestions. It’s Not Either/Or: We don’t see this as a binary choice. Rather than asking “shift-left or shift-right?”, ask “how much do we need for a particular project?” A mission-critical financial application might require exhaustive shift-left testing with formal verification methods, while a content-focused website might benefit more from shift-right user experience testing. The Right Balance: Early automation & unit testing (Shift-Left) + Continuous monitoring & feedback (Shift-Right) = High Quality Software Conclusion: Shift-Left and Shift-Right aren’t opposing forces—they’re complementary. Modern teams need to test early, test often, and test in production to achieve faster releases, better quality, and happier users. By embracing both Shift-Left and Shift-Right Testing, you can create a testing strategy that’s tailored to your team’s needs. So, what are you waiting for? Start shifting your testing strategy today and reap the benefits of faster releases, better quality, and happier users! Are You Ready to Shift Your Testing Strategy in the Right Direction

Ensuring Fairness in AI Testing: A Critical Look

As artificial intelligence (AI) continues infiltrating every corner of the tech world, its impact on software testing is undeniable. While AI promises a future of faster, more efficient testing, its integration raises critical questions about bias, transparency, and data privacy. This begs the question: can we truly trust AI to identify and eliminate software flaws without introducing new ethical dilemmas? Let’s explore these concerns in the context of real-world projects to ensure AI remains a force for good in the ever-evolving realm of software quality assurance. 1. The Double-Edged Sword: AI Testing and the Bias Challenge The meteoric rise of AI in software testing promises a revolution in efficiency and speed. But like any powerful tool, it comes with a responsibility to wield it ethically. One of the biggest concerns is bias – AI can unknowingly inherit prejudices from the data it’s trained on. The Loan Approval Example: A Case in Point Let’s take a closer look at the loan approval scenario. In the mortgage industry, AI can analyse historical loan data to test the approval process. However, if this data reflects biases against certain demographics, the AI could unknowingly perpetuate them. Imagine the AI consistently rejecting loan applications with names that statistically correlate with minority groups. This could lead to unfair rejections during testing, highlighting the importance of unbiased training data and constant monitoring. So, what’s the solution? Go back to the foundation – the training data. Meticulously curate a new dataset that is as diverse and unbiased as possible. Additionally, implement regular audits to constantly monitor for any biases the AI might develop over time. This vigilance is crucial to ensure AI remains a force for good in testing, not a tool for perpetuating inequalities. 2. Demystifying the Machine: Transparency in AI Testing One of the biggest hurdles in adopting AI for software testing is its inherent opacity. Often, AI feels like a black box – it delivers results, but the reasoning behind them remains shrouded in mystery. This lack of transparency can be a major roadblock, as we saw in a mortgage industry project where AI was used to test loan application processing. Loan officers, underwriters, and compliance specialists, naturally, were hesitant to trust AI’s recommendations without understanding its decision-making process. The Appraisal Quandary: A Real-World Example Imagine a scenario where AI is used to test automated valuation models (AVMs) in the mortgage industry. These AVMs use complex algorithms to estimate property values. An opaque AI model might simply flag certain property valuations as outliers without any explanation. This lack of transparency could leave appraisers sceptical and raise concerns about the fairness and accuracy of the AI’s judgements. So, what’s the solution? There are ways to break open the black box and shed light on AI’s inner workings by utilizing tools like LIME (Local Interpretable Model-agnostic Explanations). These tools act like translators, unpacking the complex calculations AI uses and presenting them in a way humans can comprehend. With these explanations, appraisers can easily understand why specific property valuations were flagged. For instance, the AI might explain that a valuation was flagged as an outlier because it deviated significantly from valuations of similar properties in the same neighbourhood. With this newfound transparency, appraisers could understand the AI’s reasoning, assess its validity, and make well-informed decisions while incorporating the efficiency of AI analysis. 3. Walking the Tightrope: Data Privacy and AI Testing One of the inherent tensions in AI testing is the balance between its data-hungry nature and the need to protect sensitive information. This tightrope walk is especially important in the mortgage industry, where AI can be a powerful tool for testing customer relationship management (CRM) systems. These CRMs often house a treasure trove of sensitive customer data, and ensuring privacy is paramount. A Balancing Act: The Real-world Data Example Imagine a mortgage lender who wants to test a new AI-powered feature in their CRM that helps loan officers personalize communication with potential borrowers. To train the AI effectively, the system needs access to historical customer interactions, including emails, phone logs, and loan application details. As this data includes sensitive information like names, income details, credit scores, and social security numbers, this can’t be exposed. So, what’s the solution? Data Anonymization, Encryption, and Regulatory Compliance: Data Anonymization: Anonymize the customer data before feeding it to the AI for training. This strips away any personally identifiable information (PII) such as names, addresses, or social security numbers. Essentially, the data becomes a generic representation of customer interactions, allowing the AI to learn patterns without compromising individual privacy. Encryption: Add an extra layer of security by encrypting the anonymized data. Encryption scrambles the data, making it unintelligible to anyone who doesn’t possess the decryption key. Regulatory Compliance: Ensure full compliance with data protection regulations like GDPR (General Data Protection Regulation) and relevant local privacy laws. This involves not only anonymizing and encrypting data but also conducting regular privacy impact assessments (PIAs). These PIAs are essentially audits that identify and mitigate any potential privacy risks associated with using customer data for AI testing. Conclusion: While AI revolutionizes QA testing, ethical considerations are crucial. We must guard against bias and ensure clear accountability. Data privacy needs robust protection. By prioritizing these areas and adhering to ethical frameworks, AI becomes a powerful and trustworthy partner in software testing, fostering trust and boosting efficiency within QA. This responsible use of AI leads to better, more reliable software for everyone.

AI Agents in Action: The New Operating Layer for Modern Enterprises

The AI revolution isn’t coming — it’s here. But for all the progress in models, tools, and use cases, one thing remains painfully clear: most enterprise systems weren’t built to work with intelligence. While AI is evolving rapidly, enterprises are still operating in environments designed around rigid workflows, static rules, and human intervention. The result? A widening gap between what AI can do and what enterprise systems allow it to do. To close that gap, organizations need more than automation. They need a new operating model — one that brings modular intelligence into everyday workflows, makes decisions in motion, and scales responsibly across domains. That model begins with AI agents.   Why Traditional Systems Fall Behind in an AI-First World The shift in expectations is undeniable. Customers want personalization. Employees want intelligent tools. Stakeholders want results — fast, scalable, and accurate. But legacy systems were designed for a different world. They follow fixed rules, not evolving patterns. They execute predefined steps, but don’t make contextual decisions. They automate tasks, but struggle to adapt or learn.   So, we end up with patchwork solutions — scripting bots, layering in RPA, or manually bridging gaps. It works until it doesn’t. Fatigue sets in, tech debt piles up, and transformation efforts stall. Meanwhile, AI has quietly become ready for prime time — language models that understand nuance, vision models that verify documents, and systems that recommend next steps. But integrating this intelligence into daily operations remains elusive. Traditional platforms weren’t built to think — or to change.   What Enterprises Actually Need: A New Operating Layer To embed intelligence into the heart of enterprise operations, we don’t need smarter dashboards. We need a smarter backbone. Enter the concept of the AI Operating Layer — powered by modular AI agents that plug into workflows, make decisions, and drive coordinated action. The AI Operating Layer works with what you have, translating insight into impact — intelligently, at scale, and securely.   What Makes AI Agents Different Not all automation is equal. AI agents offer a fundamentally new design for how intelligence is deployed across the enterprise. 1. Modular by Nature AI agents are not monoliths. They’re small, purpose-built units that solve targeted problems — like auto-filling a form, routing a lead, or sending a context-aware reminder. Start with one. Scale to many. 2. Intelligent by Design Unlike rule-based systems, AI agents interpret, learn, and adapt. They don’t just follow instructions — they understand context, detect patterns, and make judgment calls where needed. 3. Orchestrated in Action Agents don’t operate in silos. They work together — one agent triggers another, passing along context and completing workflows seamlessly. The orchestration layer ensures the entire flow is greater than the sum of its parts. 4. Enterprise-Ready Governance Trust is table stakes. AI agents are built with audit logs, explainability, and human-in-the-loop controls. Enterprises can manage them with the same rigor they apply to core systems — without sacrificing speed.   Meeting Enterprises Where They Are AI transformation doesn’t happen in a vacuum — it happens within constraints. That’s why organizations need to adopt AI at the pace their systems and culture allow. System-Ready & AI-Committed You have the infrastructure and the mindset. Go wide: deploy clusters of orchestrated agents that optimize workflows and surface insights at scale. System-Ready, But AI-Cautious Start with standalone agents. Prove value quickly in low-risk areas. Build internal confidence before expanding into orchestration. Not System-Ready, But AI-Committed Begin with low-code pilots. Use AI accelerators to show early results while gradually modernizing your tech stack. Not Ready & Cautious Keep it safe. Explore use cases in controlled environments. Run workshops, test ideas, and focus on transparency and governance. There’s no wrong entry point — only a wrong pace. The goal is sustained, strategic evolution.   How It All Works Under the Hood Behind the scenes, the AI agent model is powered by two complementary engines: The Agent Catalog A library of plug-and-play agents, each designed for a specific task. They can function alone or be assembled into clusters for multi-step workflows. Many are domain-specific — tailored for industries like mortgage, insurance, sales, and service. The Orchestration Engine The brain of the system. It coordinates agents, manages triggers and context, handles exceptions, and enables human oversight where needed. It also tracks agent performance, flagging issues and enabling continuous improvement. This is not about automating individual tasks. It’s about building intelligent ecosystems that work together — with minimal manual oversight. A Mortgage Example: From Chaos to Coordination In mortgage origination, AI agents can transform the journey from lead to loan: Engage leads 24/7 — no missed opportunities. Match borrowers with the right advisors — based on skills, not availability. Let advisors focus on people — while agents handle documents, forms, and nudges. Ensure nothing slips through — agents track follow-ups and escalate if needed. Deliver consistent service — at scale, from first click to final close.     Beyond Mortgage: Broad-Scale Possibilities The agent-based model isn’t tied to one industry. The core framework adapts across verticals: Insurance: Claims processing, fraud alerts, policy generation Sales: Lead scoring, proposal automation, quote-to-cash Customer Service: Case triage, summarization, proactive outreach Anywhere there’s a process, there’s room for intelligent agents.   Built for What’s Next This isn’t a stopgap solution. The AI Operating Layer evolves with your business: Predictive task automation Self-prioritizing workflows Natural language interactions Cross-agent collaboration Continuous learning and feedback loops As more agents are deployed and more data flows through the system, your enterprise stack becomes smarter — more adaptive, more proactive, and more capable.   Conclusion The promise of AI isn’t just in insight — it’s in action. That means embedding intelligence into the very fabric of enterprise operations, not treating it as a separate layer. AI agents are the bridge between what AI can do and what enterprises need. Modular. Intelligent. Orchestrated. Governed. The future isn’t a monolith. It’s a network of agents — working intelligently, together.

How FinConnect is Transforming Financial Services through Efficient Partner Integrations?

Data and services have become indispensable in the financial services industry, driving customer experience, operational efficiency, and intelligent decision-making. As the need for digital transformation grows, smart data and partner integrations are redefining the relationship between borrowers and lenders. At the heart of the transformation, intelligent data integration is a revolutionizing internal process. At Tavant, we understand the importance of intelligent data integration solutions, and FinConnect is the answer to simplify your lending experiences with vendors and best-in-class customer experiences. Let’s delve into the current trends, challenges, and solutions of financial transformation driven by intelligent data integration in this thought leadership article. The Role of Intelligent Data IntegrationFinancial institutions are turning to intelligent data integration to streamline their operations and deliver better experiences for customers. Let’s see how Tavant has come up with the data integration solutions with FinConnect: Real-time Analytics and Predictive Insights – Connected platforms enable financial institutions to monitor transactions and customer behavior in real-time. This capability improves agility and responsiveness. By integrating data analytics, organizations can forecast market trends, optimize portfolio performance, and anticipate emerging risks, thus staying ahead of the curve. Automation of Repetitive Tasks – By leveraging artificial intelligence (AI), financial services can automate all the time-consuming tasks such as fraud detection, compliance checks, and risk assessment. FinConnect processes mortgage-related data for automatic routine operations which allows organizations to focus on higher-value tasks. Improved Data Accuracy and Operational Scalability – Intelligent systems clean and verify the data, reducing manual errors and ensuring high-quality information. It is crucial to maintain the integrity of financial services, designed to scale, handle, and adapt to changing demands without compromising performance.   Enhancing Customer Experience with Intelligent Data Let us understand how to enhance the customer experience using intelligent data and how FinConnect is using data integration solutions: Personalized Loan Options and Faster Approvals – Lenders can access customized loan products through connected data platforms. These platforms offer products tailored to the unique financial situations of borrowers. Traditional and non-traditional data sources (such as utility payments and rental history) provide lenders with creditworthiness. Enhanced Transparency and Seamless User Experience – Transparency and clear communication are what borrowers expect today throughout the lending process. You get real-time updates in the connected data platforms. Platforms like FinConnect eliminate paper documentation, automate document submissions, and provide intuitive interfaces, improving the user experience. Building Long-Term Relationships with the Vendors – Tavant views its vendor relationships as long-term partnerships rather than transactional collaborations, fostering mutual trust, improving communication, and enhancing the overall quality of service delivered to financial institutions. In turn, this long-term approach allows Tavant to continuously improve its offerings suitable for changing market conditions and customer demands.   How does FinConnect Serving Data Integration Solutions? Tavant is shaping the financial transformation with the invention of platforms like FinConnect. Let’s understand some of the aspects of how it enhances the lending experience for lenders and borrowers: The Power of API Integrations in Modern Lending Application Programming Interfaces (API) create a connecting bridge between diverse systems and stakeholders. FinConnect integrates with more than 100 partners and vendors through APIs, acting as a one-stop solution for all things mortgages. FinConnect streamlines the data exchange systems driven by APIs. It facilitates faster underwriting, improves risk assessments, and enhances fraud detection. The product not only connects financial institutions to third-party services like credit scoring, compliance checks, and data verification, ensuring a smooth digital transformation. Let’s understand the ecology of FinConnect in the following diagram:      Plug-and-Play Financial Services: Simplifying Lending Solution with FinConnect The introduction of “plug-and-play” is transforming the arena of financial services. It is a game changer for both borrowers and lenders. With platforms like FinConnect, you don’t need any technical overhauls to integrate new services, vendors, and data sources. This flexibility of the platform reduces inefficiencies and allows organizations to meet market demands quickly.  Borrowers can enjoy faster access to credit by seamlessly utilizing the plug-and-play system. It is more transparent which increases operational efficiency and reduces the time-to-time market for new products and services. On-demand Data: Streamlining Lending Processes The availability of on-demand data from both sources and third-party vendors is beneficial for lenders as they get access to more accurate, creditworthiness, and timely insights into borrower behavior and market conditions. FinConnect adheres to the compliance and data verification tools and allows lenders to access real-time, accurate data, improves decision-making speed and reduces delays. With the help of on-demand data, streaming data analytics is easier and the outcome is faster. FinConnect integrates with ComplianceEase to ensure data governance and loan verification process, which makes it seamless for lenders. Ensuring Security and Speed in Loan Processing FinConnect securely ensures loan processing is done quickly. With the increased reliance on digital platforms, FinConnect provides robust security measures including end-to-end encryption. Tavant adheres to the following security measures: CCPA (California Consumer Privacy Act): For users located in California, Tavant follows CCPA regulations, giving them control over their personal information, including the right to know what data is collected and the right to delete or opt out of the sale of personal data. PCI DSS (Payment Card Industry Data Security Standard): For financial transactions, Tavant ensures that payment data is processed and stored in compliance with PCI DSS standards, minimizing the risk of fraud and data breaches.   The Future of Digital Transformation with FinConnect by Tavant FinConnect by Tavant is leading the financial services landscape through intelligent data integration. Leveraging real-time analytics and enabling automation, it streamlines the lending experience. FinConnect not only modernizes lending but also fosters a more inclusive, transparent, and customer-centric financial ecosystem. Tavant, for instance, is leading the charge in digital transformation for the financial services industry. By combining real-time analytics, automation, and a powerful API ecosystem, FinConnect is empowering institutions to stay competitive and deliver exceptional customer experiences. Ready to transform your lending operations? Connect with us to see how FinConnect can help your institution thrive in the digital age.   Contacts Swapna [email protected] FAQs – Tavant Solutions How

AI Agents in Warranty Claims: Revolutionizing Adjudication & Automation

Problem Statement: Manual warranty claim submission and processing are fraught with inefficiencies, leading to delays, errors, and high administrative costs. Some key challenges include: Time-Consuming Process: Warranty claim processing requires multiple manual verifications, document reviews, and approvals. The involvement of various stakeholders, such as dealers, service centers, and claim adjudicators, prolongs processing times. The delays in claim adjudication impact dealer operations and slow down reimbursements, reducing overall efficiency. Error-Prone Submissions: Dealers often submit incomplete or incorrect claim information, leading to multiple rounds of back-and-forth communication. Missing or incorrect details—such as vehicle identification numbers (VINs), part numbers, or labor hours—cause delays, resulting in additional workload for claim processing teams. These manual interventions increase the likelihood of human errors and misjudgments. Fraud and Duplicate Claims: Fraudulent warranty claims, intentional or unintentional duplicate submissions, and inflated repair costs create significant financial risks for manufacturers. Identifying fraudulent claims manually is a challenging and time-intensive process, making it easier for invalid claims to slip through the cracks. This leads to unnecessary expenses and higher warranty costs. High Operational Costs: Warranty claim processing involves a dedicated workforce managing claim submissions, document reviews, validations, approvals, and dispute resolutions. The reliance on manual efforts increases labor costs and operational overhead. Inefficient processes result in higher administrative expenses and reduced profitability for OEMs and warranty service providers. Lack of Standardization: Warranty claims submitted by different dealers often vary in format, making it difficult to implement consistent validation rules. The inconsistency in claim forms, documentation formats, and supporting evidence makes it challenging to compare claims objectively. Without a standardized process, discrepancies arise, leading to inconsistent adjudication outcomes. Poor Dealer Satisfaction: Slow and complex warranty processing negatively impacts dealer satisfaction. Dealers rely on timely reimbursements to maintain their cash flow and sustain their business operations. When claim processing takes too long or leads to disputes, it results in dissatisfaction, strained relationships, and potential loss of trust in the warranty system. Limited Insights and Recommendations: Manual claim reviews lack the ability to leverage data-driven insights. Without predictive analytics, identifying patterns in fraudulent claims, optimizing approval rates, and improving adjudication decisions become difficult. The lack of AI-powered insights prevents proactive decision-making, leading to reactive rather than preventive claim handling.   AI Agents Overview: AI Agents are intelligent, autonomous systems designed to execute specific tasks using advanced machine learning models, natural language processing, and automation techniques. These agents collaborate to enhance business process automation by analyzing structured and unstructured data, making decisions, and optimizing workflows. In warranty claim adjudication, AI Agents play a crucial role by automating complex decision-making processes that traditionally require human expertise. By leveraging vast datasets, these agents can validate claims against historical records, detect fraud, ensure compliance with warranty policies, and provide recommendations for approval or rejection. Additionally, AI Agents improve process transparency and efficiency by integrating with enterprise resource planning (ERP) and warranty management systems, enabling seamless end-to-end automation. A multi-agent AI system allows different AI Agents to work in tandem, each specializing in distinct tasks such as claim validation, anomaly detection, document verification, and predictive analytics. This collaborative approach ensures faster, more accurate claim processing, ultimately enhancing customer and dealer satisfaction while reducing operational costs.   How Can AI Agents Help in Claim Process Automation? 1. Analyze Claims and Assign Suspect Scores AI-powered models assess claims against historical data to detect inconsistencies and irregularities. By leveraging machine learning algorithms, AI Agents can assign a suspect score to each claim based on risk factors such as unusual repair costs, excessive labor hours, or high claim frequency. Claims with high suspect scores are flagged for further review, ensuring that fraudulent or inflated claims are identified early in the process.   2. Clustering and Peer Averaging to Identify Outlier Claim Line Items AI Agents use clustering techniques to group claims with similar characteristics, such as repair type, vehicle model, part replacement, and cost. By comparing new claims to peer averages, AI can detect anomalies where costs or labor hours significantly deviate from standard benchmarks. This process helps in identifying overcharged claims, ensuring fairness, and maintaining warranty cost control.   3. AI Claim Attachment Content Extraction and Validation Warranty claims often include supporting documents such as invoices, repair orders, and service logs. AI-powered Vision models and Natural Language Processing (NLP) extract critical data from these attachments, ensuring that all required information is present and accurate. AI Agents validate extracted content against claim details and warranty policies, reducing manual verification efforts and improving claim accuracy.   4. Automated Duplicate Claim Validation Duplicate claims pose a significant challenge in warranty management, leading to unnecessary payouts and financial losses. AI Agents automatically cross-check new claims with previously submitted claims using pattern recognition techniques. By comparing key attributes such as vehicle identification number (VIN), service dates, and part numbers, AI detects potential duplicate claims and prevents redundant payments.   5. AI Recommendation / Next Best Action Recommendation AI Agents provide intelligent recommendations based on past claim resolutions, business rules, and historical data. By analyzing patterns in claim approvals, denials, and adjustments, AI suggests the most suitable course of action—whether to approve, reject, request additional documentation, or escalate for further review. This streamlines decision-making, reduces the burden on human adjudicators, and ensures consistent claim handling.   6. Automated Adjudication By integrating insights from suspect scoring, clustering, content validation, and duplicate detection, AI Agents enable automated claim adjudication with minimal human intervention. AI-driven decision-making ensures that valid claims are processed swiftly, fraudulent claims are flagged for investigation, and ambiguous cases are escalated for manual review. This automation significantly improves processing speed, reduces operational costs, and enhances dealer satisfaction by minimizing delays in claim approvals.   Conclusion: AI Agents revolutionize warranty claim adjudication by automating labor-intensive tasks, improving accuracy, and reducing fraud. By leveraging AI-powered claim analysis, automated adjudication, and intelligent recommendations, businesses can enhance operational efficiency, lower costs, and boost dealer satisfaction. As AI technology continues to evolve, multi-agent collaboration will further streamline warranty processing, ensuring a seamless and optimized claims experience. This transformation will ultimately lead

Why AI is the key to a Borrower-friendly Home Equity Landscape?

According to recent industry reports, the average HELOC approval process takes 2-6 weeks, with some lenders taking even longer due to manual data entry and fragmented workflows. This inefficiency costs lenders billions annually in operational expenses and risks alienating borrowers in an increasingly competitive market. These challenges are compounded by growing borrower expectations. As homeowners seek alternatives to refinancing in the current environment, the HELOC originations are projected to exceed $200 billion this year. However, the traditional HELOC process has capacity constraints that may not allow it to meet the demands of today’s borrowers, who expect speed, transparency, and seamless digital experiences. In this thought leadership piece, let’s examine the current scenario, fathom the limitations of traditional HELOC processes, and explore how AI-driven solutions are paving the way for a streamlined, borrower-centric future.   Challenges in Traditional HELOC Applications The traditional HELOC application process is fraught with inefficiencies. Borrowers must navigate: Data Entry and Processing: Submitting mountains of paperwork, such as tax returns and bank statements, which lenders manually verify. Is a process that is prone to errors and delays Intricate Compliance Requirements: As a lender, if you have to manually review credit scores, debt-to-income ratios (DTI), and loan-to-value ratios, it becomes time-consuming and error-prone, exposing you to compliance risks Disjointed Workflows: Multiple teams or third-party vendors manage property valuations, credit checks, and income verifications, leading to miscommunication and inefficiencies Protracted Approval Times: Traditional HELOCs can take weeks or even months for approval, frustrating borrowers and increasing operational costs. These challenges have created a pressing need for innovation, and AI has stepped in to bridge the gap.   AI’s Role in Shaping the Future of HELOCs AI is revolutionizing the HELOC process by addressing inefficiencies and improving the borrower experience: Automating Document ProcessingAI-powered tools scan, analyze, and validate documents using Natural Language Processing (NLP). This eliminates manual data entry and ensures accuracy, reducing processing times significantly. Compliance and Risk AssessmentAI systems automate regulatory compliance checks and fraud detection. By evaluating metrics like DTI and LTV in real-time, AI minimizes errors and ensures adherence to internal policies. Streamlined WorkflowsAI platforms integrate multiple steps—credit checks, property valuations, and title searches—into a single cohesive process. This reduces delays and back-and-forth communication, expediting approvals. Faster Approval TimesAI-driven platforms such as Tavant’s Touchless Lending® offer conditional approvals in minutes, turning a traditionally cumbersome process into a seamless digital experience. Real-Time VerificationAI integrates with third-party systems for real-time credit and income verification, ensuring lenders have up-to-date information while speeding up application processing.   HELOC vs. Alternatives: Navigating the 2025 Landscape In today’s high-interest rate environment, homeowners are exploring various options for leveraging home equity, including HELOCs, home equity loans (HELOANs), and credit cards.     HELOCs stand out for their flexibility and cost-effectiveness, making them an ideal choice for long-term projects. However, the future of HELOCs lies in integrating AI to offer faster approvals and tailored borrower experiences.   Strategic Utilization of Home Equity Homeowners today hold over $32 trillion in equity, representing immense untapped financial potential. With AI-driven advancements, HELOCs can help homeowners achieve financial goals without compromising long-term security. Home ImprovementHELOCs can fund renovations that enhance property value, with returns of 60-70% on project costs. AI ensures faster fund access and accurate evaluations. Debt ConsolidationBorrowers can consolidate high-interest debts at rates significantly lower than credit cards, reducing financial strain. Preserving Mortgage RatesIn a high-interest environment, HELOCs allow homeowners to access funds without refinancing their primary mortgage, maintaining their low-rate advantage. Tax AdvantagesInterest on HELOCs used for home improvements may be tax-deductible, adding financial benefits.   The Road Ahead As we look to the future, AI will continue to redefine HELOCs, enabling lenders to deliver faster, more accurate, and borrower-friendly experiences. By automating repetitive tasks, reducing errors, and enhancing compliance, AI transforms HELOCs into a streamlined, efficient solution for both lenders and borrowers. Tavant, as a leader in AI-powered lending solutions, is at the forefront of this transformation. Its Touchless Lending suite exemplifies the power of advanced technology in revolutionizing the HELOC process. By automating end-to-end workflows, offering real-time credit verification, and integrating seamlessly with lender systems, Tavant enables faster approvals and superior borrower experiences. Products like LO.ai further elevate borrower engagement, providing personalized, AI-driven interactions that simplify the lending journey. For homeowners, Tavant’s innovative solutions ensure they can unlock the value of their homes with confidence, leveraging their equity to build a brighter financial future. Lenders leveraging platforms like Tavant’s are not just embracing innovation; they are shaping the future of the HELOC market, staying ahead of the curve, and setting the stage for a smarter, more accessible home equity landscape. To learn how we help our customers use digital to create value by reinventing the core of their business, visit www.tavant.com or reach out to us at [email protected]. FAQs – Tavant Solutions How does Tavant use AI to create borrower-friendly home equity experiences?Tavant employs AI to streamline home equity applications, provide instant property valuations, offer personalized loan recommendations, and automate approval processes. Their AI-powered platform reduces application complexity, accelerates decision-making, and provides transparent, fair lending practices that benefit home equity borrowers. What AI capabilities does Tavant offer for home equity lending optimization?Tavant provides AI-driven property valuation, automated income verification, intelligent risk assessment, personalized rate pricing, and predictive customer service for home equity products. These capabilities create efficient, accurate, and customer-centric home equity lending experiences that improve satisfaction and approval rates. How does AI improve the home equity borrowing experience?AI improves home equity borrowing through faster applications, automated valuations, instant pre-approvals, personalized offers, simplified documentation, and transparent decision-making. These improvements reduce borrower effort, uncertainty, and time-to-funding while providing competitive rates and terms. What AI applications are most beneficial in home equity lending?Most beneficial AI applications include automated property valuation models, income and asset verification, risk-based pricing, fraud detection, customer service chatbots, and predictive analytics for loan performance. These applications improve efficiency, accuracy, and customer experience. How does AI make home equity lending more accessible?AI makes home equity lending more accessible by expanding approval

How Emotion AI Enhances Field Service & Customer Experience

Introduction In today’s competitive landscape, meeting Service Level Agreements (SLAs) is no longer enough to ensure customer satisfaction. Customer experience has become the key differentiator in field service. HiverHQ report shows that Implementing Emotion AI in customer service has been associated with a 20% increase in customer satisfaction scores. While traditional Field Service Management (FSM) solutions focus on efficiency and SLA compliance, they often overlook the emotional aspect of service interactions. Enter Emotion AI – a transformative technology that enables service providers to understand, analyze, and act on customer emotions in real-time. By bringing this new dimension to field service, organizations can enhance customer trust, foster loyalty, and differentiate themselves in a crowded market. Emotion AI empowers service teams to move beyond reactive service models and embrace a truly customer-centric approach, strengthening long-term relationships and driving business growth. What is Emotion AI? Emotion AI, also known as Affective Computing, is a branch of artificial intelligence that enables machines to detect, interpret, and respond to human emotions. By analyzing facial expressions, voice tones, and even text sentiment, Emotion AI can gauge a customer’s emotional state in real-time. Technologies Used by Emotion AI: Natural Language Processing (NLP) – Analyzes sentiment in customer interactions. Computer Vision – Detects emotions from facial expressions. Speech Analysis – Identifies tone, pitch, and stress in voice communication. Machine Learning & Deep Learning – Predicts emotional responses and automates actions. Wearable Sensors & IoT – Tracks physiological signals like heart rate and stress levels. Emotion AI is now being integrated into field service operations to enhance customer interactions and drive satisfaction. A report by MarketsandMarkets projects that the Emotion AI market will grow from $2.74 billion in 2024 to $9.01 billion by 2030, at a CAGR of 21.9 % during 2024–2030, indicating a strong shift towards AI-driven emotional intelligence in service industries. The Need for Emotion AI in Field Service Traditional field service management (FSM) solutions primarily focus on efficiency-reducing downtime, optimizing dispatch, and ensuring compliance with SLAs. However, these metrics do not capture the emotional aspects of a customer’s experience, such as frustration due to delays or satisfaction from proactive communication. Emotion remains a key driver for delivering high levels of CX performance. A study by Forrester Research found that in 2023, elite brands delivered customer experiences that evoked, on average, 29 positive emotions-including feeling happy, valued, and appreciated – for each negative emotion. A study by Zendesk found that Two-thirds of consumers who believe a business cares about their emotional state will likely become repeat customers. Emotion AI enables service organizations to: Gauge real-time customer sentiment through voice tone, text, and facial expressions (where applicable). Prioritize high-impact cases by identifying emotionally distressed customers. Enhance service technician interactions by providing AI-driven emotional intelligence insights. Improve customer loyalty through proactive engagement and personalized service recovery actions.   How Emotion AI is Transforming Field Service 1. AI-Driven Sentiment Analysis for Customer Interactions Emotion AI analyzes customer service calls, chat transcripts, and feedback forms to detect sentiment and emotional tone. This helps field service teams: Identify unhappy customers in real-time and take immediate corrective action. Automatically escalate high-priority cases to senior support staff before issues escalate. Provide personalized technician guidance to improve service engagement.   According to a survey by Forrester, customer-obsessed organizations reported 41% faster revenue growth, 49% faster profit growth, and 51% better customer retention than those that are not customer-obsessed. 2. Real-Time Emotion Recognition for Field Technicians Mobile service applications integrated with AI-powered sentiment recognition tools allow field technicians to: Receive emotion-based service cues before arriving at the customer site. Adjust their approach based on customer sentiment, enhancing personalized engagement. Capture real-time customer sentiment feedback post-service for continuous improvement.   A study by McKinsey found that AI-enabled customer service is now the quickest and most effective route for institutions to deliver personalized, proactive experiences that drive customer engagement 3. Predictive Customer Satisfaction Analysis Using historical service data, AI models predict potential dissatisfaction points and suggest preemptive actions. This ensures: Proactive issue resolution before it affects the customer. Reduced negative escalations, improving brand loyalty. Data-driven decision-making to refine service workflows.   A report by PwC suggests that 70% of CEOs said generative AI will significantly change the way their companies create, deliver, and capture value in the next three years Benefits of Emotion AI in Field Service 1. Enhanced Customer Satisfaction By understanding and acting on customer emotions, companies can build trust and increase loyalty, leading to higher retention rates and better Net Promoter Scores (NPS). Implementing Emotion AI in customer service has been associated with a 20% increase in customer satisfaction scores  2. Proactive Service Recovery Identifying and resolving customer dissatisfaction early reduces churn and negative feedback, ensuring a more resilient brand reputation. As per SIEMENS , AI-driven predictive maintenance can reduce machine downtime costs, which amount to up to $1.5 trillion annually for global manufacturers. 3. Improved Technician Performance Technicians equipped with emotional insights can adapt their communication styles, leading to more successful service visits and better customer interactions. As mentioned in Rydoo blog, AI Agents can manage 30% of live chat communications and 80% of routine tasks, freeing up human agents to focus on complex issues. 4. Competitive Differentiation Emotion AI-driven FSM solutions allow companies to offer emotionally intelligent service experiences, increasing customer retention and brand trust. Emotion AI is reshaping the future of field service by bringing empathy, personalization, and intelligence to every customer interaction. By leveraging AI-powered solutions, service organizations can enhance customer experiences, ensuring that service excellence is not just about meeting SLAs-but about exceeding expectations and fostering long-term loyalty. The future of Emotion AI in Field Service Management (FSM) is set for significant growth, transforming customer interactions and operational efficiency. The global Emotion AI market is projected to grow from $2.74 billion in 2024 to $9.01 billion by 2030, at a CAGR of 21.9% (MarketsandMarkets). By 2032, the market is expected to reach $13.8 billion, growing at a CAGR of 22.7% (PR Newswire). These trends indicate that Emotion AI will play

Agile Testing Transformation: Rethinking How We Deliver Quality

Agile Testing Transformation is the process of moving an organization’s testing practices to an agile way of working, resulting in better quality of the delivered product. At its core, Agile Testing Transformation isn’t just a technical shift but a mindset change. It’s all about making testing faster, smarter, and more aligned with what really matters: delivering value. According to Evan Leybourn of The Agile Director, Agile focuses on three fundamental pillars: Process Agility, Technical Agility, and Business Agility. Let’s explore how these pillars are implemented in Quality Engineering.   Process Agility: Adapting Testing for Continuous Improvement Process agility emphasizes creating flexibility in how teams approach testing, ensuring quality remains a priority even as plans evolve. Testing becomes a dynamic part of the development process, adapting quickly to shifting priorities and requirements. Here’s how this can be implemented in testing: Smaller, Faster Deliveries: Breaking down testing into smaller, manageable cycles helps teams validate updates incrementally instead of waiting for lengthy development phases. Early feedback from these smaller deliveries allows testers to identify and address issues sooner, leading to continuous product improvement. Frameworks That Fit Testing Needs: Agile methodologies like Scrum and SAFe provide a structure for testing that prioritizes efficiency without being overly restrictive. The focus is on delivering quality outcomes rather than adhering to rigid testing protocols. Continuous Learning in Testing: Agile encourages testers to experiment with new tools and approaches, refine their strategies, and grow through each iteration. When an approach does not work, teams adapt and apply their insights to future projects, ensuring ongoing improvement in their testing processes.   Technical Agility: Building Quality That Lasts While process agility focuses on how teams work, technical agility emphasizes what they’re building. It’s about creating systems and solutions that aren’t just functional but are built to last and adapt as needs evolve. Here’s what technical agility looks like in action, especially in testing: Quality as the Foundation: Practices like Test-Driven Development (writing tests before the code) and pair programming (two minds tackling one problem) ensure quality isn’t an afterthought — it’s baked into every step of the process. Automation: The Ultimate Testing Ally: Automation transforms testing from a bottleneck to a superpower. Automated tests and deployment pipelines handle repetitive tasks, catch issues early, and free up time for more profound, more creative testing efforts.   Business Agility: Making It Bigger Than Teams Agility isn’t just for developers or product teams — it’s about bringing everyone together to make quality a shared responsibility. Business agility connects the dots across departments and leadership, ensuring that testing isn’t just a task for “someone else” but something everyone contributes to.   Here’s what it looks like: Testing Beyond the Testing Team: Agile isn’t just about how testers work. It’s about everyone — from finance to HR — being part of a system that makes testing smoother and more effective. With the whole organization aligned, testing becomes a collaborative effort, not a bottleneck. Enabling Leaders, Not Micromanagers: Leadership is evolving. Managers must ensure that testers and teams have the autonomy, resources, and environment necessary to excel in their work. Customer-Centric Mindset: Ultimately, testing goes beyond simply identifying bugs — it’s about guaranteeing that what we provide is effective for our customers.   Why Agile Testing Transformation Matters Agile Testing Transformation changes how we approach testing to deliver faster, more intelligent, and higher-quality results. Instead of seeing testing as something that happens at the end of the process, it’s about weaving it into every stage of development. When teams adopt agile testing, they catch issues early, improve collaboration between testers and developers, and stay aligned with customer needs as they change. This shift alters conventional perspectives on testing. It’s no longer just about identifying bugs — it’s about ensuring each process stage contributes value and enhances the product. Agile Testing Transformation fosters a “quality-first” attitude, where testing continually adapts to emerging demands, integrating quality seamlessly into the whole development process.   Conclusion Agile Testing Transformation is more than a methodology change; it is a paradigm shift in how we think about and approach quality in software development. By embracing agility in processes, technology, and business practices, organizations can ensure that testing becomes a proactive, value-driven activity. The transformation fosters collaboration, innovation, and adaptability, making quality an integral part of every step in the development lifecycle. Agile Testing Transformation is not just an option but a necessity for organizations aiming to thrive in a fast-paced, customer-centric world.

Revolutionizing Warranty Management with AI: How Tavant Warranty Transforms Legacy Policies

Introduction In today’s fast-paced technological era, Original Equipment Manufacturers (OEMs) and dealerships are facing substantial challenges. Warranty claims processing has grown more complex, with administrative costs increasing by 28% over the past five years. Traditional legacy systems are struggling to keep pace, leading to inefficiencies, escalated costs, and dissatisfied customers. Tavant Warranty is reshaping the industry by utilizing AI Agents to automate claims processing, enhance policy standardization, and improve customer satisfaction.   Challenges in Legacy Warranty Policies Manual & Inefficient Processes: Dealerships using outdated legacy systems face lengthy claim processing times, leading to workflow bottlenecks. Rising Administrative Costs: Warranty claim administration costs have surged by 28%, forcing dealerships to hire more staff or outsource, increasing expenses. Lack of Standardization for Multi-Brand Dealerships: Multi-OEM dealerships must navigate various proprietary warranty systems, resulting in inefficiencies and higher training costs. Slow Claim Processing & Customer Dissatisfaction: A 47% increase in claim filing times directly impacts customer satisfaction and dealership profitability.   How Tavant Warranty AI Agents Transform Warranty Management AI-Powered Claims Automation: The Tavant Warranty platform leverages AI to validate claims instantly, reducing approval times by 50%. Standardized Multi-OEM Warranty Processing: AI standardizes claims processing across multiple OEM warranty systems, thus reducing complexity for dealerships. Cost Reduction Through Smart Automation: AI-driven strategies help dealerships cut claim processing expenses by 20%. Enhancing Customer Communication & Satisfaction: AI-powered warranty Agents provide real-time claim status updates, improving transparency and trust.   AI Agents in Warranty Management: Revolutionizing Warranty Processing AI for Warranty Eligibility Verification: AI automatically checks historical purchase data, reducing eligibility verification time by 40%. AI-Powered Predictive Maintenance: By predicting potential failures, AI prevents costly claims, saving dealerships an average of $500 per vehicle serviced. AI in Claims Processing & Fraud Detection: AI detects fraudulent claims with 95% accuracy, reducing warranty fraud and unnecessary payouts. AI for Standardizing Warranty Procedures: AI ensures uniform warranty processes across brands, reducing claim rejection rates by 30%. AI-Driven Data Analytics for Warranty Trends: Tavant Warranty AI Agents provide predictive analytics for warranty claims, helping OEMs refine product quality strategies.   The Benefits of AI in Warranty Management Faster & More Accurate Claims Processing: Tavant AI warranty management platform reduces claim cycle times by 50%, enabling quicker reimbursements. Reduction in Administrative Costs: AI automation minimizes manual processing errors, cutting administrative costs by 25%. Improved Customer Experience & Dealer Efficiency: Dealerships using our warranty system report a 20% increase in customer satisfaction and a 15% boost in service efficiency.   The Future of AI in Warranty Management The future of warranty management is transitioning towards AI-driven automation and predictive analytics. AI-powered warranty optimization will not only expedite claim processing but also allow for proactive issue resolution by analyzing component failure trends. As AI advances, manufacturers can harness these insights to enhance product quality, reduce recalls, and increase profitability.   Conclusion AI is transforming warranty management by reducing claim processing times, improving accuracy, and optimizing costs. Tavant Warranty leads this revolution, equipping OEMs and dealerships with AI-powered solutions for modernizing warranty management. Ready to revolutionize your warranty operations? Contact us today to explore how AI-powered Tavant Warranty system can streamline your claims processing and enhance customer satisfaction.

AI Agents Transforming Warranty Management

The aftersales and warranty landscape has grown more intricate than ever in today’s rapidly evolving manufacturing industry. Dealers and customers expect quicker resolutions, products are becoming increasingly complex, and managing the accompanying warranty policies and claims presents more significant challenges. This is where AI agents step in, offering a powerful solution to streamline and simplify these processes. What Exactly Are AI Agents? AI agents are intelligent systems designed to perceive their environment, process data, and take action to achieve specific goals. They often automate tasks that would otherwise require human intervention. These agents analyze vast amounts of data, identify patterns, and make decisions faster and more accurately than traditional methods. AI agents hold immense potential for manufacturers, particularly in the aftersales and warranty space. They can optimize claims management, help organize diverse warranty terms and conditions, predict warranty trends, and assist managers in making data-driven decisions. This leads to reduced costs and improved customer satisfaction—two key priorities in any manufacturing business. This blog explores how AI agents can transform warranty management. We’ll explore how these intelligent systems help warranty managers work more efficiently and tackle common challenges.   Why Do Warranty Managers Need an AI Agent? Managing warranties in today’s manufacturing world is no small task. Warranty managers juggle various terms and conditions, such as Limited, Full, Extended, Lifetime, Major Components, and Maintenance Contracts. Each policy may cover or exclude specific labor costs, parts, or other factors, and every product line, model, or series often has its own unique set of warranties to maintain. Staying organized can be a significant challenge even with a robust rule-based system. Beyond the policies themselves, warranty managers oversee teams of claims processors, manage warranty budgets, and collaborate with dealers—all while ensuring customer satisfaction. The sheer volume of data and processes involved can be overwhelming. However, by delegating data-intensive tasks to an AI agent, managers can free up valuable time to focus on higher-priority areas, such as resolving complex, high-value claims and making strategic budget decisions.   How Do AI Agents Ease the Burden? Automating Policy Management When a new product is launched, or an existing model is upgraded, warranty managers often face the tedious task of creating new policies or updating existing ones. Managing this data can be time-consuming and error-prone. Enter AI agents. Equipped with the ability to read, analyze, and update policies, these tools can directly integrate with warranty software. They assist warranty and marketing teams in identifying whether a new policy is required or if an existing one can be applied to the product while seamlessly handling the data entry process. Mapping Service Labor Codes Warranty managers often map service labor codes to specific parts or models, another labor-intensive task. AI agents simplify this by automating the mapping process. Managers provide a list of labor codes and part numbers, and the AI ensures that the correct associations are made efficiently and accurately. Monitoring Key Performance Indicators (KPIs) AI agents can monitor warranty-related KPIs, such as claim cycle times and warranty costs. These systems analyze reports to flag issues like excessive claim processing times or rising claim costs. By identifying trends and highlighting areas that need attention, AI agents allow managers to focus their time and energy on resolving gaps instead of manually running and reviewing reports. Proactive Insights and Trend Prediction Beyond reactive support, AI agents provide proactive insights. They can analyze historical warranty data to predict trends, such as common product failures or high-cost claims. This enables manufacturers to address potential issues before they escalate, improve product designs, and refine warranty terms for future models. The Benefits of AI Agents in Warranty Management By automating labor-intensive tasks, AI agents save time and reduce human error. They empower warranty managers to work more efficiently and focus on strategic initiatives. Additionally, these tools ensure greater consistency in policy management, faster claim processing, and more accurate mapping of labor codes—all of which contribute to time and cost savings. In a fast-paced manufacturing environment, leveraging AI agents is no longer a luxury but a necessity. These intelligent systems are transforming how manufacturers handle warranties, ensuring businesses stay competitive while meeting the rising expectations of dealers and customers alike. Ready to revolutionize your warranty operations? Contact us today to explore how AI-powered Tavant Warranty system can streamline your claims processing and enhance customer satisfaction.

Optimizing Warranty Claim Processing: How an AI Agent Can Help in Roofing Manufacturing

Manufacturers of roofing materials have invested in robust warranty claim processing systems to streamline operations. However, despite best efforts, many manufacturers still face challenges that impact efficiency, accuracy, and customer satisfaction. AI-powered warranty claims solutions streamline operations, reducing errors and ensuring faster resolutions. In this blog we explore how AI-driven automation is transforming warranty claim processing in the roofing industry. Enter the Roofing Claims Agent—an AI agent designed to mitigate these challenges and elevate warranty claim processing.   The Challenges Faced Current systems, while improved, still encounter several challenges: Inefficient Data Analysis: Basic reporting provides limited insights into claim trends and patterns. Manual Data Validation: Although some automation exists, manual intervention is still required to validate claim data, causing delays and errors. Inconsistent Claim Resolution: A lack of standardized decision-making logic results in inconsistent claim outcomes and potential disputes. Limited Customer Communication: Automated notifications are sent, but personalized communication and timely updates are often missing, leading to customer dissatisfaction.   How the AI Agent Can Help The Warranty Claims Management AI Agent can address these challenges by: Analyzing Data and Providing Insights: Quickly processing large datasets to reveal more profound insights into claim trends, patterns, and opportunities for product improvement. Validating Data in Real Time: This method reduces manual intervention by validating claim data as it is received, thereby minimizing errors. Standardizing Claim Resolution: Implementing consistent, standardized decision-making logic ensures uniform claim outcomes and reduces disputes. Providing Personalized Customer Communication: Generating tailored updates and notifications to enhance customer satisfaction and reduce complaints.   Use cases: The Roofing Claims Agent supports various use cases across the warranty claim process: Claim Intake and Validation Automated claim submission: Receive and process claims submitted through various channels (e.g., online portal, email, phone). Claim data validation: Verify claimant information, product details, and warranty registration. Product details related to roof configuration and warranty types are areas where the warranty processing team struggles to validate and check, as do the claimants (the roofers/contractors). The Agent can iterate with the personas involved here and facilitate closure through an “Intelligent Search“with access to vast troves of knowledge from product configuration documents, past claim data, and so forth, to ensure the claim correctly points to the correct roof configuration. Warranty eligibility check: Determine if the product is still under warranty and if the claim is within the warranty period.   Claim Investigation and Assessment Damage assessment: Analyse photos, videos, or descriptions of damage to determine if it’s related to a manufacturing defect. Product inspection: Review product specifications, installation instructions, and maintenance requirements to determine if the damage is due to improper installation or maintenance. Weather event verification: Verify if weather-related events (e.g., hurricanes, hail storms) contributed to the damage.   Claim Resolution and Settlement Claim approval or denial: Based on the investigation, approve or deny the claim, providing clear explanations for the decision. Settlement calculation: Calculate the settlement amount based on the warranty terms, product cost, and damage extent. Communication with the claimant: Notify the claimant of the decision and provide instructions on the next steps.   Reporting and Analytics Claim tracking and reporting: Generate reports on claim status, types of damage, and settlement amounts. Warranty claim trends analysis: Analyse claims data to identify trends, patterns, and areas for product improvement. Product quality monitoring: Monitor claim data to detect potential product quality issues.   Integration with Other Systems CRM integration: Integrate with customer relationship management (CRM) systems to access customer information and update claim status. ERP integration: Integrate with enterprise resource planning (ERP) systems to access product information, inventory levels, and order history. Document management integration: Integrate with document management systems to store and retrieve claim-related documents, especially the ones related to Product configuration (Roof configuration) and warranty types.   The Benefits of Working with the Roofing Claims Agent By deploying an AI agent, manufacturers can: Improve Claim Processing Efficiency: Reduce claim processing time by up to 20%. nhance Claim Accuracy: Increase accuracy by up to 15%. Boost Customer Satisfaction: Improve customer satisfaction scores by up to 10%.   The Future of Warranty Claim Processing An AI agent like the Roofing Claims Agent is poised to revolutionize warranty claim processing. Manufacturers can remain competitive in an ever-evolving market by optimizing processes, improving efficiency and accuracy, and delivering exceptional customer experiences.

Leveraging AI Agents to Streamline Service Operations

In today’s fast-paced, competitive business environment, companies constantly seek ways to improve efficiency, reduce costs, and enhance customer satisfaction. One emerging technology proving highly effective in achieving these goals is using AI Agents for service lifecycle management (SLM). But what exactly is an AI Agent, and how can it drive value and efficiency for businesses—especially for Original Equipment Manufacturers (OEMs), their dealers, and service organizations like yours? Understanding AI Agents An AI Agent is a software program designed to perform tasks autonomously on a user’s or another system’s behalf. These agents interact with their environment, collect data, and use it to make decisions and execute actions to achieve specific goals. They can handle various functionalities, from natural language processing and decision-making to problem-solving and interacting with external environments. Addressing Business Problems with AI Agents Businesses across various manufacturing industries, including those that collect IoT and telematics data, face several common challenges: Unplanned Downtime: Equipment failures and unplanned downtime can lead to significant financial losses and operational disruptions. High Operational Costs: Maintenance, repair, and operational costs can substantially impact the bottom line. Inefficient Service Delivery: Delays and inefficiencies can lead to customer dissatisfaction and lost business opportunities. Complex Warranty Management: Managing warranties and handling claims can be time-consuming and prone to errors. Data Overload: Businesses often struggle to make sense of the vast amounts of data their operations generate.   AI Agents can address these challenges by leveraging advanced technologies such as machine learning, predictive analytics, and data integration for multiple business systems. Consider all the business systems you use to manage your business: CRM, ERP, Warranty, Telematics, Field Service, and Case Management. AI agents can perform simultaneous tasks across all of these systems to enable efficiencies and reduce the need for your team to swivel chair, copy and paste, or use other painful, inadequate methods of doing business.   Enhancing Asset Performance and Uptime One of the primary benefits of AI Agents is their ability to enhance asset performance and uptime. By analyzing historical data and real-time sensor inputs, AI Agents can predict failure probabilities for various components. This proactive approach allows businesses to schedule maintenance activities more effectively, reducing unplanned equipment downtime. For example, in the heavy equipment industry, AI-driven predictive maintenance can boost uptime by up to 50% and extend equipment lifespan by 20%. This improves operational efficiency and enhances customer satisfaction by ensuring equipment reliability.   Reducing Operational Costs AI Agents play a crucial role in reducing operational costs. By predicting failures and optimizing maintenance schedules, businesses can minimize repair expenses and reduce the total cost of ownership (TCO). In sectors such as automotive and commercial trucks, maintenance costs can be substantial, and this can lead to significant savings. For instance, AI applications in the automotive after-sales market are expected to grow at a CAGR of 10.5% from 2023 to 2028, driven by diagnostics, predictive maintenance, and customer service advancements. AI Agents can also optimize fuel consumption and service scheduling, further driving cost efficiencies.   Improving Service Delivery and Customer Engagement AI Agents enhance service delivery and customer engagement by providing real-time insights and accurate demand forecasts. This enables businesses to improve field support and first-time fix rates, which is particularly important in industries where downtime can significantly impact operations. Additionally, AI Agents can drive growth in parts and service sales by enabling informed, proactive customer engagement. By analyzing usage patterns and predicting future needs, businesses can offer timely recommendations for service and component replacements, boosting sales and strengthening customer relationships. In the commercial HVAC sector, AI applications are expected to grow significantly, driven by the need for energy efficiency and improved building management. AI can help reduce energy consumption by up to 30% in commercial buildings. Streamlining Warranty Management Warranty management is another area where AI Agents can substantially benefit OEMs and Service Organizations. AI Agents streamline the entire warranty process by automating warranty approvals and detecting fraud. This leads to faster resolution times and improved customer satisfaction. In the commercial truck service industry, AI tools can reduce warranty claim processing time by up to 40%, leading to faster resolutions and improved customer trust. Additionally, AI-driven analytics can help identify common issues and optimize service schedules. In industries like off-road machinery, warranty claims can be complex and time-consuming, and this can result in significant operational efficiencies.   Reducing Transactional Work AI Agents significantly reduce transactional work for companies by automating routine tasks and processes. This allows employees to focus on more strategic and value-added activities, enhancing productivity. For instance, AI Agents can handle data entry, automated service case creation, report generation, and answer customer inquiries, freeing up human resources for more complex problem-solving and decision-making tasks. Data Monetization and Revenue Generation AI Agents also open new revenue-generating opportunities through data monetization. Businesses can create additional revenue streams by offering tiered subscription models that utilize advanced equipment analytics. This is particularly relevant for industries like automotive, commercial trucks, and off-road equipment, where companies can provide premium services based on predictive maintenance and performance optimization. Customizing AI Agents for Different Stakeholders To maximize the benefits of AI Agents, OEMs, and their servicing companies can deploy customized agents tailored to the needs of different stakeholders: Dealer Agent: This AI Agent can streamline dealer operations by consolidating services such as inventory management, order processing, and customer support. The Dealer Agent can enhance dealer efficiency and customer satisfaction by providing real-time insights and automating routine tasks. Supplier Agent: The Supplier Agent can optimize supplier interactions by automating procurement processes, managing supplier performance, and ensuring timely deliveries. This agent can also analyze supplier data to identify potential risks and opportunities, improving overall supply chain efficiency. Customer Agent: The Customer Agent can enhance customer engagement by providing personalized support, proactive maintenance reminders, and timely updates on service issues. This agent can offer tailored recommendations and solutions by leveraging customer data, boosting customer loyalty and satisfaction. Service Agent: The Service Agent can proactively contact customers to schedule service calls based on IoT data.

From Static Systems to Dynamic Minds: The Evolution of AI

From age-old logic-based programs to sophisticated decision-makers, AI’s journey has been nothing short of revolutionary. It is a story of constant innovation. We’ve shifted from “if-this-then-that” simplicity to machines that can observe, think, learn, and even anticipate. Let us dive into this exciting evolution and uncover how these new-era intelligent agents reshape the world. Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the modern age, continuously reshaping industries and reimagining possibilities. To understand how cutting-edge intelligent agents drive today’s innovations, it’s crucial to explore how AI has evolved from its humble beginnings as a rules-based system to the human-like sophisticated decision-making systems we see today. The Dawn of Artificial Intelligence: Rules-Based Systems AI’s journey started in the mid-20th century with rules-based systems (aka – expert systems). These systems used predefined rules, logic, and structured programming to mimic decision-making processes. A good example would be the “IF-THEN” statements, which provided deterministic outputs for specific inputs. These early systems found success in narrow domains, such as: Medical Diagnosis: Programs like MYCIN helped doctors diagnose bacterial infections and recommend treatments. Business Processes: Systems automated repetitive tasks, such as scheduling and inventory management.   Despite their utility, rules-based systems had significant limitations. They struggled with: Scalability: Adding new rules increased complexity and reduced efficiency. Flexibility: Adapting to novel scenarios was nearly impossible without manual intervention. Contextual Understanding: These systems could not learn from data or interpret nuanced information.   As industries evolve and start demanding more capable intelligent systems, AI must take the next leap. The Emergence of Machine Learning: Moving Beyond Static Rules The advent of machine learning (ML) brought a pivotal shift in the late 20th century. Unlike rules-based systems, ML models can learn patterns and make predictions by analyzing large datasets. Algorithms like neural networks, decision trees, and support vector machines became the cornerstones of this era.   Key innovations included: Autonomous Learning: Machines could improve performance without explicit reprogramming. Data Utilization: With the rise of the internet and digital storage, vast amounts of data became available to train models. Real-World Applications: ML systems have found practical uses, from spam filters in email systems to early recommendation engines for e-commerce platforms.   However, ML also has its challenges. To make it efficient, the Models require substantial data for training, and the interpretability of their decisions often challenges end-users.   The Rise of Deep Learning and Cognitive AI In the early 21st century, deep learning emerged as a game-changer. Leveraging advanced neural networks, deep learning mimicked the human brain’s ability to process information hierarchically. Combined with exponential growth in computational power and cloud computing, this led to breakthroughs in: Natural Language Processing (NLP): AI systems like chatbots and virtual assistants became capable of understanding and generating human language. Computer Vision: Tasks like facial recognition and object detection achieved unprecedented accuracy. Game AI: Algorithms like AlphaGo demonstrated the potential of AI in mastering complex, strategic games. These systems introduced cognitive capabilities like reasoning, learning, and problem-solving. However, they still operated mainly within defined tasks and lacked general intelligence, a hallmark of human cognition.   Intelligent Agents: A New Paradigm in AI Journey The latest evolution in AI is the rise of intelligent agents. These are autonomous entities capable of perceiving their environment, making decisions, and taking action to achieve specific goals. Intelligent agents combine the power of deep learning, reinforced learning, and contextual understanding to operate across diverse and dynamic scenarios. Defining Features of Intelligent Agents Context-Aware Decision-Making: Intelligent agents analyze real-time data to make decisions that align with broader objectives. Autonomous Operation: They require minimal human intervention, enabling continuous operation in complex environments. Collaboration: These agents can interact with humans and other systems to enhance efficiency.   Real-World Applications Manufacturing: Agents optimize production schedules, streamline auto claim adjudication, provide dealer support for warranty processes, and enhance supply chain operations with real-time insights. Customer Support: Conversational agents provide personalized and immediate responses to customer queries. Healthcare: Intelligent agents assist in diagnosing diseases, monitoring patient conditions, and recommending treatments.   Why the Evolution Matters Understanding the progression from rules-based systems to intelligent agents underscores AI’s expanding capabilities and potential to revolutionize industries. This evolution reflects not only technological advancements but also a shift in how we approach problem-solving—from static programming to dynamic, adaptive intelligence.   The Road Ahead As we stand on the cusp of even more significant innovations, the future of AI promises: General Intelligence: Systems capable of understanding and performing any intellectual task a human can do. Ethical AI: Addressing bias, transparency, and decision accountability concerns. Seamless Integration: AI agents are becoming integral to human workflows, enhancing rather than replacing human efforts.   Final Thoughts AI has evolved from rigid, rules-based systems to versatile intelligent agents. This evolution continues to pave the way for a future where humans and AI collaborate to tackle the world’s most complex challenges. At Tavant, we relentlessly believe in an AI-first approach for any innovation we bring to life. Our intelligent agents are designed to simplify business complexities, offering transformative solutions tailored to your needs. Learn how Tavant’s AI-driven solutions can empower your business. Visit Our Website or Get in Touch today to explore how our intelligent agents can transform your operations and drive success.

Case Management in the Age of AI

Introduction “Time is money.” This adage is more relevant in the context of dealers, where every “hour” counts. The more time the unit spends sitting on a dealer lot for repair, the higher the probability that the customer loses confidence in the product, driven by low machine uptime. Statistics show that an average farmer loses $3,348 per year to repair downtime1. Case management systems have been a cornerstone of manufacturing industries’ service life cycle systems, such as automobiles, heavy vehicles, and agriculture, for decades. These systems enable organizations to track and manage complex cases from initiation to resolution, ensuring that each case is thoroughly examined and addressed. AI-powered case management is changing the game, enabling real-time resolutions and enhanced decision-making. The Limitations of Traditional Case Management Systems Traditional case management systems are often manual, relying on human analysts to review and manage cases. AI in case management streamlines this process by automating repetitive tasks and providing instant recommendations. his approach has several limitations: Time-consuming: Manual case management requires significant time and resources, diverting focus from other critical tasks. Scalability: As the volume of cases increases, so does the complexity of managing them manually. Human analysts can become overwhelmed, leading to delays and mistakes. Accuracy: Manual reviews can be prone to errors, mainly when dealing with complex or ambiguous information. Bias: Human analysts may bring personal biases to case management, which can lead to unfair treatment of individuals or groups.   The Rise of AI Agents in Case Management In the words of Microsoft CEO Satya Nadella2: “Humans and swarms of agents will be working together where AI agents will act as digital workers”.   Dealer Assist AI agents have been designed to address the limitations of traditional case management systems. Dealer Assist AI agents are transforming case management by: Process vast amounts of data: Dealer Assist AI agents can quickly sift through enormous datasets, identifying patterns and connections that human analysts might miss, reducing processing times from weeks to hours Maintain accuracy: Dealer Assist AI agents, minimize errors, ensuring that cases are accurately assessed and managed. Eliminate bias: Dealer Assist AI agents reduce the risk of biased decision-making by relying on algorithms rather than human intuition. Scale seamlessly: As cases grow, Dealer Assist AI agents can adapt effortlessly, maintaining efficiency and accuracy. Increased Transparency: Dealer Assist AI agents explain their reasoning and decision-making processes, promoting transparency and trust. Cost Savings: Dealer Assist AI agents reduce labor costs by automating manual tasks, minimizing the need for human analysts. 24/7 Availability: Dealer Assist AI agents operate around the clock, ensuring that cases are continually monitored and addressed.   Real-World Applications of AI Agents in Case Management Be it Agricultural, Automobile, or Trucking Industry, Dealer Assist AI agents can be successfully deployed in various industries to manage complex cases: Self Help: Dealer Assist AI agents can provide technicians with repair steps for each product. A global automotive brand integrated AI-powered case management, reducing service call volume by 30% as technicians accessed instant repair recommendations. End Customer Assist: AI-driven AI Agents answer common queries, minimizing customer wait times and improving satisfaction scores by 40%. OEM Case Closure: Dealer Assist AI agents can answer dealer queries on behalf of the OEM or help create tickets for dealers. At the same time, they help the OEM respond to Dealer queries as accurately as possible, reducing the turnaround time. By automating service ticket creation and dealer-to-OEM case resolutions, AI agents cut response times by 50%.   The Path Forward: A Convergence of Human Insight and AI Expertise The future of case management isn’t AI replacing humans—it’s AI augmenting human capabilities. With hybrid models, AI handles routine cases, while complex decisions still require human expertise. For example, AI may suggest optimal repair procedures, but experienced analysts review and approve final steps. Organizations adopting AI-human collaboration have seen a 35% boost in case resolution efficiency. The future of case management lies in a convergence of human insight and AI expertise: Human Oversight: Experienced analysts can review AI-generated recommendations, ensuring that cases are thoroughly examined and addressed. Hybrid Approaches: Organizations can combine traditional case management with AI-powered solutions to maximize efficiency and accuracy. Continuous Learning: As Dealer Assist AI agents process more data, they can refine their algorithms, improving performance and reducing errors.   Conclusion AI-powered case management is redefining service operations, offering faster, more accurate, and cost-effective solutions. Organizations that embrace AI today will gain a competitive edge in service lifecycle efficiency. The rise of Dealer Assist AI agents in case management will revolutionize how organizations handle complex cases. Dealer Assist AI agents can offer unparalleled advantages over traditional case management systems by leveraging machine learning algorithms and automation. While human analysts remain essential to ensure that each case is thoroughly examined and addressed, AI-powered case management can streamline processes, reduce errors, and improve efficiency. As industries continue to adapt to the changing landscape of case management, one thing is clear: AI agents are here to stay, and their impact will only grow in the years to come. By embracing this technology, organizations can ensure that cases are handled with the care, accuracy, and speed they deserve. Ready to integrate AI into your case management system? Contact us today to learn how our Dealer Assist AI agents can revolutionize your workflow.   Where to start? For those looking to implement Dealer Assist AI agents, it is necessary to have the right technology partner to: Conduct thorough assessments: The right technology partner can help your organization evaluate its current case management processes and identify areas where Dealer Assist AI agents can improve efficiency and accuracy. Choose suitable AI tools: The right technology partner can help with the selection of the correct AI agent and platforms that align with your organization’s needs, ensuring seamless integration and scalability. Provide ongoing training: The right technology partner can help with educating human analysts on the benefits and limitations of Dealer Assist AI agents to ensure a smooth transition. Continuously monitor performance: The right

Revolutionizing Service Contracts with AI Agents: Boost Sales and Customer Satisfaction

Manufacturers in transportation, recreational vehicles, heavy equipment, and other industries offering service contracts constantly strive to enhance customer retention, build strong relationships, and ensure overall satisfaction with their products and services. Service contracts and extended warranties have become pivotal tools in achieving these goals. In today’s competitive market, offering high-quality products is no longer enough. Customers demand added value, and service contracts are a proven way to provide this. Not only do they enhance the customer experience, but they also open up new revenue streams for manufacturers and their service networks. By leveraging AI agents, companies can streamline the management of these offerings, resolve common challenges, and unlock additional benefits for all stakeholders involved. The Value of Service and Warranty Contracts Consumer Benefits: Higher Product Resale Value: Documented maintenance through service contracts ensures the product retains its value. Predictable Costs: Service contracts allow customers to plan their budgets more effectively, reducing unexpected expenses. Reduced Financial Risk: Extended warranties protect against unplanned repair costs, offering peace of mind. Improved Uptime: Reliable service keeps equipment operational longer, benefiting individual owners and businesses like rental companies.   Dealer/Service Provider Benefits: Stable Revenue Streams: Service contracts create predictable and recurring income. Stronger Customer Relationships: Frequent service visits foster trust and loyalty between dealers and customers. Upselling Opportunities: Regular interactions allow for selling additional parts, accessories, or upgrades. Manufacturer Benefits: Enhanced Customer Satisfaction: Providing reliable service boosts overall brand perception. Increased Customer Retention: Service contracts ensure ongoing engagement with the manufacturer’s ecosystem. Higher Revenue: Extended warranties and service plans generate consistent and incremental revenue.   Challenges in Managing Service and Warranty Contracts While the benefits of service and warranty contracts are clear, managing them effectively comes with its own set of challenges: Communication Gaps: Ensure dealers and customers are informed about available service plans and updates. Complex Pricing Structures: Balancing profitability with customer value while considering product-specific variables like region, usage, and configuration. Coordination of Service Events: Managing the logistics of service scheduling and ensuring timely maintenance without disrupting customer operations. Solution: Intelligent AI agents are the key to addressing these issues. These agents can optimize pricing, streamline communication, and automate scheduling by leveraging data-driven insights. Their ability to interact seamlessly with customers and dealers makes them indispensable for modern service contract management. How AI Agents Optimize Service Contracts 1. AI Pricing Agent Determining the right price for service contracts involves multiple factors: the product’s age, usage patterns, regional variables, and historical maintenance data. Traditionally, this process required manual analysis and significant resources. AI Pricing Agents simplify this by dynamically analyzing real-time and historical data. Key Features of AI Pricing Agents: Dynamic Pricing Adjustments: Continuously analyze historical claims and service events to update pricing. Customization by Product Attributes: Incorporate region (e.g., hot vs. cold climates), usage type (e.g., residential, rental), and configuration details (e.g., gas vs. diesel engines, turbo options). Predict Future Costs: Factor in fluctuating parts and labor costs to ensure pricing remains competitive and profitable. Maximize Profitability: Optimize pricing strategies to balance customer satisfaction with financial goals.   2. AI Recommendation/Communication Agent Customers and dealers often face confusion about the details of service contracts: what they cover, how much they cost, and how they add value. The AI Recommendation Agent bridges this gap, providing real-time answers and personalized recommendations. Key Features of AI Recommendation Agents: Tailored Recommendations: Suggest service plans based on product type, usage, and customer preferences. Instant Query Resolution: Answer questions about pricing, coverage, and benefits through chat, email, or text. Follow-up Engagement: Send reminders or follow-ups to customers who have not purchased or renewed a plan. Highlight Value Propositions: Clearly communicate the benefits of different plans to help customers make decisions. Renewal Assistance: Streamline the process of extending or upgrading contracts.   3. AI Coordination Agent Scheduling service events is often a logistical challenge. Customers may forget maintenance intervals, and dealers need adequate preparation to deliver seamless service. The AI Coordination Agent automates these processes, ensuring proactive communication and efficient scheduling. Key Features of AI Coordination Agents: Proactive Notifications: Remind customers of upcoming service needs, whether part of a plan or not. Simplified Scheduling: Customers can book service appointments through an interactive platform. Dealer Alerts: Notify service providers about upcoming appointments, ensuring readiness. Preemptive Parts Ordering: Automatically order necessary parts for scheduled services to reduce downtime. Feedback Collection: Conduct post-service surveys to measure customer satisfaction and identify areas for improvement.   Enhancing the Customer Experience with AI Agents By integrating AI agents into service contract management, manufacturers can: Increase Efficiency: Automate time-consuming tasks like pricing, communication, and scheduling. Boost Engagement: Maintain consistent customer interaction, ensuring long-term loyalty. Drive Revenue Growth: Optimize pricing and upselling opportunities, unlocking additional income streams.   These intelligent systems reduce operational complexities and deliver a superior customer experience by providing timely, relevant, and personalized services. Conclusion Service and extended warranty contracts are essential for building strong relationships between manufacturers, dealers, and customers. However, effectively managing these offerings requires innovation and adaptability. AI agents provide the perfect solution, streamlining processes and enabling data-driven decision-making. Manufacturers can overcome traditional challenges, improve customer satisfaction, and unlock new revenue opportunities by deploying AI Pricing, Recommendation, and Coordination Agents. In a rapidly evolving market, adopting these advanced tools is not just a competitive advantage but a necessity for sustained growth and success.

AI Agent for Warranty Claim Management

Problem Statement Dealerships across various industries are grappling with a rising challenge: the cost of administering warranty claim submissions and reimbursements has increased by 28% over the past five years. Furthermore, the growing complexity of modern products has led to a 47% increase in the time required to file a claim. This trend is expected to worsen as sales volumes grow, product quality perceptions decline, and recalls become more frequent. The introduction of sophisticated technologies like telematics, electric and hybrid drivetrains, and advanced electronics in traditional heavy equipment, automobiles, and trucks has further increased the likelihood of warranty claims. Additionally, Original Equipment Manufacturers (OEMs) offer extended service contracts and preventive maintenance plans, significantly contributing to claim volumes. To make matters more challenging, OEMs are implementing stricter checks in their warranty systems, making the process of filing claims more complex for dealerships. This issue is exacerbated in multi-branded dealerships, where each OEM has its proprietary warranty system. To address these challenges, dealerships are relying on higher headcounts and outsourcing. However, with warranty claims forming a significant portion of the service department’s business, reducing the rising costs associated with claim administration is critical. This is where the AI Agent for warranty claim management comes into play. AI-driven solutions can alleviate the burden on service writers and warranty administrators by automating and streamlining the warranty claims process. These intelligent systems can determine whether a claim should be filed, identify the correct claim type, ensure all necessary information is provided, and adhere to the specific data requirements of each OEM. What Are AI Agents? AI agents are intelligent systems designed to perceive their environment, process data, and take actions to achieve specific goals. They often automate tasks that would otherwise require human intervention. These agents analyze vast amounts of data, identify patterns, and make decisions faster and more accurately than traditional methods. In the context of manufacturers, particularly in aftersales and warranty operations, AI agents offer immense potential. They can optimize claims management, organize diverse warranty terms and conditions, predict warranty trends, and help managers make data-driven decisions. This results in reduced costs and improved customer satisfaction—two critical priorities for any business. This blog explores how AI warranty agents can revolutionize warranty management, helping warranty managers work more efficiently and tackle common challenges. — How Can Warranty Management AI Agents Help? 1. Determining Warranty Coverage AI warranty agents can quickly determine whether a repair is covered under warranty. For complex products like automobiles and heavy equipment, multiple warranties often apply depending on the failed parts and the timing of the failure. AI agents eliminate guesswork, saving users time and effort. 2. Identifying the Claim Type Each OEM has its proprietary warranty claim processing system with multiple claim types for different failure situations. Some systems have 10–12 claim types, which can confuse users. Incorrect claim-type submissions lead to rejections or delays in processing. AI-driven warranty solutions can analyze warranty manuals and OEM systems to guide users in selecting the correct claim type, or even automate the selection process entirely. 3. Automated Claim Creation from Service Orders Repair information is usually captured in the dealership’s Dealer Management System (DMS) service orders. AI agents can systematically connect to the DMS or scan service order PDFs to map the data into the OEM warranty system, drastically reducing manual data entry. This automated claim creation streamlines claim processing and saves dealerships significant time. 4. Automatic Identification of Failure Codes OEMs often require detailed failure codes (e.g., fault, defect, symptom codes) to analyze warranty data for quality control. AI warranty agents can extract textual information from repair comments and part details to automatically assign the correct failure codes. This ensures accuracy and enhances the efficiency of warranty claim management. 5. Replaced Part Recommendations AI agents can suggest replacing parts by analyzing historical data and product configurations stored in OEM ERP systems. This pattern-matching capability helps dealerships streamline repairs, improve claim accuracy, and reduce customer downtime. 6. Labor Code and Hour Recommendations Determining the correct labor codes and hours for a claim can be time-consuming, as it often involves referencing labor time books with detailed assembly drawings. AI-driven warranty solutions can process these documents and match replaced parts to the appropriate labor codes and repair hours, saving users significant time. 7. Documentation Recommendations Warranty claims often require supporting documentation, especially for miscellaneous costs. AI agents can identify such requirements and prompt users to upload the necessary files, ensuring claims are complete before submission. This capability ensures streamlined claim processing while reducing the likelihood of claim rejection. Conclusion The rise in warranty claim volumes, product recalls, and the complexities of modern technology have significantly increased the administrative burden on dealerships, leading to higher costs and the need for additional resources. AI warranty agents offer a transformative solution, streamlining the claims submission process and reducing the labor involved by 75–90%. By automating complex tasks like claim validation, data entry, and documentation management, dealerships can focus on delivering exceptional service while keeping administrative costs under control. AI agents for dealers are not just a tool for efficiency—they are a game-changer for dealerships navigating the challenges of warranty management in today’s evolving landscape. With the ability to deliver AI-driven warranty solutions, dealers can revolutionize their aftersales operations, reduce costs, and improve customer satisfaction.   References. 1. https://www.fi-magazine.com/373241/cost-of-processing-auto-warranty-claims-up-by-28

AI Agents: Enabling a Paradigm Shift to Predictive Maintenance

Maintenance is the backbone of industrial efficiency, especially in the age of Industry 5.0 and smart factories. Predictive maintenance, driven by AI agents, transforms how industries handle downtime, costs, and reliability, enabling seamless operations in a data-driven world. Why Maintenance Matters Unscheduled Downtime Costs: Industrial manufacturers experience annual losses estimated at $50 billion due to unscheduled maintenance. Empirical evidence highlights that unplanned downtime is, on average, 35% more expensive per minute than planned downtime, underscoring the financial ramifications of inadequate maintenance strategies. These interruptions impact immediate production cycles and disrupt supply chain continuity, compounding the financial burden for manufacturers and their stakeholders. Revenue Impact: Research reveals that large-scale manufacturer risk forfeiting up to 11% of their annual revenue due to unanticipated equipment failures and downtime. This revenue loss reflects diminished operational throughput, delayed order fulfillment, and potential reputational damage, as clients may seek more reliable alternatives. The Necessity of Planned Downtime: Planned maintenance is a strategic approach to preemptively identifying and resolving equipment vulnerabilities, thus enhancing asset reliability and longevity. However, it is not without limitations. Over-maintenance often incurs unnecessary costs and operational disruptions, while under-maintenance can result in missed opportunities to forestall critical failures, jeopardizing efficiency and profitability. Striking an optimal balance between preventive and predictive strategies is paramount for maintaining sustainable operational efficacy.     Preventive vs. Predictive Maintenance Preventive Maintenance: Preventive maintenance adheres to fixed schedules or usage thresholds, aiming to mitigate equipment failures through routine servicing. Although effective at reducing risk, it frequently results in excessive maintenance activities, inflating operational expenses unnecessarily. The rigidity of this approach often overlooks actual equipment conditions, leading to resource inefficiencies and reduced overall productivity. Predictive Maintenance: Predictive maintenance embodies a transformative, AI-driven paradigm. It forecasts potential equipment malfunctions based on real-time conditions by leveraging IoT-enabled sensors, real-time analytics, and machine learning algorithms. Maintenance interventions are executed only when necessary, optimizing schedules, curbing disruptions, and aligning costs with actual needs. This approach represents a shift toward condition-based maintenance, empowering organizations to make data-driven decisions prioritizing resource optimization. Aspect Preventive Maintenance Predictive Maintenance Scheduling Fixed intervals or usage thresholds Based on real-time conditions Efficiency Often leads to over-maintenance Optimized interventions Cost Impact Higher costs due to excess servicing Reduced costs through precision   Key enablers of predictive maintenance include: AI Agents: AI agents offer proactive diagnostic insights by analyzing historical warranty data, enabling the prediction of recurring product failures and high-cost claims. This capability empowers manufacturers to mitigate potential risks, refine product designs, and tailor warranty frameworks for subsequent iterations. Moreover, these systems facilitate nuanced decision-making, helping teams prioritize high-impact maintenance activities. IoT and Sensors: By continuously monitoring critical parameters such as temperature, vibration, and fluid levels, IoT devices provide actionable alerts. These capabilities enable industries to address equipment inefficiencies before they escalate into significant failures, fostering a proactive approach to asset management. Integrating IoT systems with AI agents creates a synergistic ecosystem where real-time data feeds predictive algorithms, enhancing accuracy and reliability.   The Benefits of Predictive Maintenance Cost Savings:  According to McKinsey, predictive maintenance can reduce equipment downtime by 30–50% while extending machinery’s lifespan by 20–40%. Addressing equipment issues before they escalate minimizes repair expenditures and optimizes productivity. Furthermore, adopting predictive maintenance can substantially reduce inventory costs by minimizing the need for emergency parts stockpiling. Improved Efficiency: The integration of real-time analytics facilitates maintenance activities during non-peak operational windows, minimizing workflow disruptions and enhancing overall efficiency. By aligning maintenance schedules with production demands, predictive systems reduce the operational strain on machinery and personnel. Increased Equipment Reliability:  AI-driven maintenance solutions identify and resolve inefficiencies early, ensuring maximum uptime and sustained performance. This reliability is particularly critical in high-stakes industries such as aerospace, where equipment failure can have catastrophic consequences. Predictive systems ensure that such risks are mitigated through preemptive interventions. Enhanced Decision-Making: By synthesizing historical data and real-time insights, AI agents provide organizations with actionable intelligence to refine maintenance protocols. For example, machine learning models have demonstrated efficacy in guiding technicians toward accurate repair actions, significantly reducing vehicle downtime and repeat repairs, with profound implications for profitability. Enhanced decision-making extends beyond repairs to inform procurement strategies, workforce allocation, and long-term asset planning.   Future Outlook for Predictive Maintenance AI-Powered Maintenance Ecosystems: Advances in AI and IoT are expected to drive predictive maintenance toward fully autonomous ecosystems. These systems leverage continuous feedback loops to enhance predictive accuracy and operational efficiency. Future iterations of predictive maintenance systems may incorporate edge computing capabilities, enabling faster data processing and decision-making directly at the equipment site. Cross-Industry Adoption: Diverse sectors, from logistics and aerospace to fleet management, are poised to embrace predictive maintenance. This adoption ensures the reliability of critical assets, such as delivery drones and advanced industrial equipment, across a wide range of applications. Industries like renewable energy are particularly well-positioned to benefit, as predictive maintenance can optimize the performance of wind turbines and solar installations, reducing downtime and maximizing energy output. Addressing Challenges: Despite its potential, implementing predictive maintenance faces challenges such as initial investment costs, data integration complexities, and workforce training requirements. However, as AI and IoT technologies evolve, these barriers are expected to diminish, making predictive maintenance more accessible and cost-effective for organizations of all sizes. Collaborative partnerships between technology providers and industry stakeholders will play a pivotal role in overcoming these hurdles. By addressing the inherent challenges of implementation and harnessing emerging technological innovations, industries can unlock predictive maintenance’s full potential and establish smarter, more resilient operational frameworks.   Conclusion Predictive maintenance signifies a pivotal shift in industrial operations, offering a data-centric, cost-effective approach to mitigating unplanned downtime. As the era of Industry 4.0 and smart factories progresses, adopting AI agents and predictive technologies becomes indispensable for maintaining seamless, reliable, and proactive asset management. Organizations can reduce operational disruptions, optimize maintenance expenditures, and enhance equipment reliability by deploying AI warranty agents and IoT-enabled systems. This approach ensures that predictive maintenance evolves from a strategic advantage to an operational necessity, solidifying its role as a cornerstone of future industrial strategies.

Fast, Simple, and Innovative: Tavant’s Encompass® Solutions is what you need to Supercharge Your Mortgage Process

As the mortgage lending industry grows more competitive, efficiency and innovation have become a requisite for survival for lending companies – both big and small. Tavant, a global leader in digital transformation, empowers lenders to tackle these challenges head-on with its innovative Encompass® software. Positioned at the forefront of mortgage process transformation, Encompass® leverages AI and automation to revolutionize the entire loan lifecycle. ICE has announced that by October 31, 2025, all lenders must transition their service ordering to the Encompass Partner Connect (EPC) platform, as the existing legacy service ordering system will be discontinued. Additionally, clients utilizing ICE’s legacy Software Development Kit (SDK) technology on Encompass will receive a six-month grace period beyond the original October 31, 2025, deadline to migrate to the new API-based platform before incurring any charges. To ensure a smooth transition, it’s advisable to begin planning and implementing the migration process well before the deadlines. This proactive approach will help maintain compliance and operational efficiency. Overview of Encompass® Solution in the Mortgage Industry Encompass® has become a cornerstone for mortgage lenders seeking a comprehensive platform to manage loan origination, processing, and servicing. This all-in-one solution not only streamlines operations but also enhances customer experiences and reduces costs. Its intuitive interface, extensive features, and seamless integrations make it the go-to choice for industry professionals. At Tavant, our deep understanding of the mortgage sector allows us to tailor solutions that fully unlock Encompass®‘s potential for lenders of all sizes. With our domain expertise and in-depth knowledge of best practices, lenders gain the competitive edge they need to excel in today’s rapidly evolving market. Why Efficient Mortgage Solutions Matter As the mortgage industry faces mounting pressures to streamline workflows, reduce costs, and enhance decision-making, Encompass® combined with Tavant’s expertise offers a powerful solution. By leveraging this partnership, lenders can: Accelerate loan processing: Reduce turnaround times and improve customer satisfaction. Optimize operational efficiency: Streamline manual tasks and eliminate bottlenecks. Enhance decision-making: Leverage data analytics and AI to make informed decisions. 1. Unlocking the Full Potential of Encompass® with Tavant’s Center of Excellence (CoE) The Encompass® Center of Excellence (CoE) is a dedicated team of experts specializing in optimizing Encompass® for mortgage lenders. Our CoE provides a range of services, including: Streamlined workflows: We help lenders identify and eliminate inefficiencies in their processes, resulting in faster loan processing and improved productivity. Seamless integrations: We integrate Encompass® with third-party systems to ensure a smooth data flow and enhance operational efficiency. Accelerated data access: We provide tools and techniques to help lenders access and analyze data quickly, enabling informed decision-making. 2. Revolutionizing Mortgage Automation with Encompass® Testing Services Testing is a critical component of any successful mortgage automation initiative. Tavant’s Encompass® Testing Services ensures the software is compliant, reliable, and ready for deployment. Our services include: Compliance testing: Encompass® adheres to regulatory requirements, minimizing the risk of errors and penalties. Release testing: We rigorously test new releases of the software to ensure they are stable and perform as expected. Performance testing: We assess the overall performance under various load conditions to identify and address potential bottlenecks. 3. Decision Analysis: Empowering Faster, Data-Driven Mortgage Lending Our decision analysis solution provides automated underwriting capabilities that accelerate loan approvals and improve decision-making. Our solution leverages advanced analytics and machine learning to: Streamline underwriting: Automatically assess loan applications against underwriting guidelines, reducing manual review time. Improve accuracy: Enhance the accuracy of credit risk assessments, minimizing the risk of loan defaults. Reduce costs: Lower operational expenses by automating time-consuming tasks. 4. AI-Powered Automation: Transforming the Mortgage Experience Artificial intelligence (AI) is revolutionizing the mortgage industry by automating tasks, improving accuracy, and enhancing customer experiences. The AI-powered solutions for Encompass® lending software  include: Automated data entry: Reduce manual data entry errors and improve data quality. Intelligent document processing: Automatically extract information from documents, streamlining the loan application process. Enhanced customer experience: Provide personalized recommendations and improve customer satisfaction. 5. Touchless Lending®: The Flagship Solution for Mortgage Automation Touchless Lending is Tavant’s flagship solution for automating the entire loan production process. Powered by AI, Touchless Lending enables lenders to: Reduce costs: Streamline operations and eliminate manual tasks, resulting in significant cost savings. Accelerate processing: Automate routine tasks, speed up loan processing and improve customer satisfaction. Enhance customer experience: Offer a self-service portal for borrowers, empowering them to manage their loan applications online. 6. The Competitive Edge of Encompass® Expertise Our deep expertise in mortgage automation provides a competitive advantage for lenders. Our solutions offer: Cost efficiency: Reduce operational costs through automation and streamlined processes. Rapid implementation: Deploy solutions quickly to achieve a faster return on investment. High ROI: Deliver measurable results and improve overall business performance. Conclusion Driven by technological advancements and changing customer expectations, the mortgage industry is undergoing rapid transformation. It has led to fierce competition among lending companies, and being efficient and innovative has become the key to survival. By leveraging Tavant’s expertise in Encompass® software and AI-powered automation, lenders can streamline their operations, reduce costs, and gain a competitive edge. Sources: What ICE’s Encompass change means for the mortgage industry Feedback: ICE revises Encompass SDK transition timeline FAQs – Tavant Solutions What makes Tavants Encompass solutions fast, simple, and innovative for mortgage processing? Tavants Encompass solutions provide automated workflows, intelligent document processing, real-time data integration, and streamlined user interfaces that reduce mortgage processing time by up to 60%. Their innovative approach combines AI-powered automation with intuitive design to create efficient, user-friendly mortgage operations. How do Tavants Encompass solutions integrate with existing mortgage systems?Tavants Encompass solutions offer seamless integration with existing LOS systems, third-party services, and regulatory reporting platforms through robust API connections. Their flexible architecture enables rapid deployment and customization while maintaining data integrity and operational continuity. What is Ellie Mae Encompass in mortgage lending?Ellie Mae Encompass (now ICE Mortgage Technology) is a comprehensive loan origination system (LOS) that manages the entire mortgage process from application through closing. It provides workflow management, compliance tracking, and integration with various mortgage industry services and vendors. How

Enhancing Mobile App Design with GenAI Tools: A New Era in Wireframing and Design of Mobile SDLC

Generative AI (GenAI) is revolutionizing the mobile application design phase by providing advanced tools for creating, refining, and optimizing designs with unprecedented efficiency and precision. Leveraging AI-powered algorithms, design teams can generate a wide range of design alternatives tailored to specific performance, usability, and scalability criteria. This iterative approach enables the evaluation and selection of the most effective designs, ensuring that the final product is not only visually appealing but also functionally robust and scalable. Moreover, GenAI plays a pivotal role in developing detailed, interactive prototypes early in the development cycle. These prototypes allow teams to simulate real-world conditions and user interactions, providing actionable insights and enabling rapid testing and refinement. By identifying potential issues and opportunities for improvement at an early stage, AI-driven prototypes enhance the overall quality of the application while significantly reducing development time and costs. This transformative capability empowers design teams to make data-driven decisions, fostering innovation and ensuring that the final mobile application meets both user expectations and business objectives. In our previous article, we explored the transformative role of Generative AI (GenAI) in the ideation and planning phase of the Mobile Software Development Lifecycle (SDLC) within the AgTech domain. As we shift focus to the wireframing and design phase, we examine how GenAI-powered tools like Uizard are revolutionizing design workflows, enabling teams to create professional, user-centric mobile interfaces with speed and precision. How Uizard Transforms the Wireframing and Design Phase 1.Rapid Wireframing Uizard empowers teams to conceptualize and create wireframes quickly and efficiently, thanks to its intuitive features: Drag-and-Drop Interface: Simplifies the creation of layouts by allowing users to add design components seamlessly. Pre-Built Templates: Offers a library of customizable templates, enabling designers to kickstart projects with minimal effort. Hand-Sketch to Wireframe Conversion: Transforms hand-drawn sketches into digital wireframes instantly, bridging the gap between ideation and design. Screenshot Scanning: Converts screenshots of existing apps into editable design elements, facilitating rapid prototyping and competitive analysis.   2. Design Iteration and Collaboration Collaboration and iterative improvements are crucial during the design phase, and Uizard excels in facilitating these processes: Real-Time Collaboration: Enables team members to work on the same design simultaneously, ensuring alignment and productivity. Version Control: Tracks changes across iterations, making it easy to revert or compare versions. Instant Feedback: Allows stakeholders to provide actionable input directly within the platform, accelerating decision-making.   3. Cross-Platform Design With the increasing need for mobile applications to work seamlessly across devices, Uizard simplifies cross-platform design: Responsive Design: Automatically adapts layouts for various screen sizes, ensuring consistent user experiences. Multi-Platform Compatibility: Supports design outputs tailored to multiple platforms, including Android and iOS, reducing rework and ensuring design consistency.   By integrating Uizard into the wireframing and design phase, teams can streamline their workflows, foster collaboration, and ensure high-quality outcomes. In the context of the AgTech domain, this capability is particularly impactful, as it allows designers to address complex agricultural use cases with user-friendly and functional interfaces. AgroApp Use Case In the current use case, we utilized Uizard to generate the designs for a mobile application, “AgroApp,” tailored to the unique requirements of the AgTech sector. Leveraging its AI-driven capabilities, Uizard intelligently identified and embedded essential screens to address the critical functionalities of AgTech-based mobile applications. Key Screens Designed for AgroApp Based on domain-specific insights, Uizard incorporated the following crucial screens into the application design: 1. Grower Details A comprehensive screen to capture and display grower profiles, including personal details, farm information, and operational preferences. User-friendly navigation to facilitate quick access to key grower data. 2. Field Information 2. Field Information Provides a detailed overview of farm fields, including crop types, soil conditions, irrigation schedules, and productivity statistics. Supports interactive visualizations like field mapping for better decision-making. 3. News A centralized hub for the latest agricultural news, market trends, and policy updates. Customizable to ensure growers receive relevant and timely information. 4. Alerts Real-time notifications on critical events such as pest infestations, disease outbreaks, or irrigation issues. Configurable thresholds to deliver actionable insights to users. 5. Weather Updates Integrated weather forecasting tailored to specific geographic locations. Provides insights into temperature, precipitation, and wind patterns to assist growers in planning field activities.   Benefits of Using Uizard for AgroApp Design Speed: Uizard’s AI-driven automation enabled rapid creation of fully functional designs, saving significant time in the initial design phase. Domain Intelligence: By embedding domain-specific features, Uizard ensured that the design aligned with AgTech industry requirements. Customization: The tool provided flexibility to tweak and optimize screens based on user feedback and operational needs. Collaboration: Real-time collaboration features allowed stakeholders to validate and refine designs, ensuring alignment with business goals. With these intelligently designed screens, AgroApp is well-positioned to provide growers and agricultural professionals with a robust, user-friendly platform for managing their operations effectively. In subsequent stages of development, these designs will serve as a strong foundation for creating an impactful mobile application.   Alternative Tools for GenAI-Driven Design While Uizard offers a robust solution for enhancing the wireframing and design phases of mobile app development, other generative AI-powered tools are making significant strides in redefining design workflows. Tools like Figma AI, Visily, and Galileo AI bring unique capabilities to the table, empowering teams to create innovative, user-centric mobile applications. 1. Figma AI: Revolutionizing collaborative design Figma AI builds on Figma’s collaborative foundation by introducing generative AI capabilities that optimize design workflows. It analyzes user inputs to suggest design alternatives, auto-align components, and ensure accessibility compliance, all while maintaining the platform’s real-time collaboration features. By reducing iteration cycles and ensuring design consistency, Figma AI has become a go-to tool for teams seeking efficiency and scalability in their mobile app design projects. 2. Visily: Simplifying prototyping for non-designers Visily democratizes the design process, empowering non-designers to create professional-grade wireframes and prototypes with ease. Its standout features, like sketch-to-wireframe conversion and AI-suggested UI components, make it an ideal choice for cross-functional teams. With domain-specific templates and intuitive workflows, Visily ensures that even those without formal design expertise can contribute meaningfully to the design phase,

An Expert Take on How AI is Transforming the HELOC Experience in Mortgage Lending

Generative AI is revolutionizing the Fintech industry, turning once slow, manual processes into seamless, efficient operations. In mortgage lending, this technology drives innovation by streamlining tasks and improving customer experiences. Tavant, a leading digital products and platform company, is at the forefront of this transformation, delivering cutting-edge AI-driven solutions across North America, Europe, and Asia-Pacific. Hemanthkumar Jambulingam, Director of Product Management at Tavant, leads the development of the Touchless Lending® suite. This suite leverages AI to reimagine loan origination and servicing, helping lenders improve customer acquisition, conversion, and retention—all while enhancing operational efficiency and speeding up processes. Check out the excerpts from a detailed discussion where Hemanthkumar shares his insights on why HELOCs are becoming increasingly attractive to homeowners and how AI is empowering lenders to provide more personalized and efficient services. Q: What is the impact of Generative AI on the Fintech landscape? Generative AI is fundamentally reshaping the Fintech industry by boosting productivity, enhancing software quality, and accelerating development cycles. It is able to improve productivity by automating many repetitive tasks, particularly in coding and testing. However, it’s not about replacing manual labor, but it has more to do with enhancing human capability. For instance, AI models can analyze massive datasets in real-time, offering actionable insights that accelerate decision-making in financial services. On the customer side, AI-driven chatbots streamline support, resolving common issues instantly, freeing up resources, and improving response times. Generative AI has a profound impact on software quality. AI-powered testing can automatically generate test cases, helping to catch errors that might slip through manual checks. This reduces human error, making software more stable and reliable. Furthermore, AI-driven personalization allows financial products and services to be tailored to individual needs, increasing customer satisfaction by delivering more relevant solutions. AI tool for code generation have accelerated the development cycles. For instance, auto-completion and auto-generation have revolutionized how quickly financial software can be developed and deployed. These tools produce high-quality, contextually accurate code, reducing the time spent on manual coding. Additionally, AI-driven continuous integration and deployment (CI/CD) systems automate key stages of the development pipeline, speeding up product launches. The adaptive nature of AI ensures continuous optimization, driving faster innovation.   Generative AI is not just improving efficiency; it’s ushering in a new era of rapid innovation in Fintech, enabling companies to bring financial products to market faster, with greater precision and reduced risk. Q: What is the current landscape of home equity lending, especially with HELOCs?The home equity lending market has seen significant changes, particularly with the rise of Home Equity Lines of Credit (HELOCs) in 2024. Homeowners are increasingly turning to HELOCs as a flexible financial solution, leveraging their property’s value to gain greater financial freedom. HELOC originations surged by over 20% in 2024, driven by rising home prices and homeowners’ reluctance to refinance at today’s higher mortgage rates. With more than $32 trillion in home equity available, homeowners are using HELOCs to access this wealth without refinancing their primary mortgages. Why Are HELOCs So Appealing? Homeowners are drawn to HELOCs because of their flexibility. A HELOC functions like a credit card—borrowers can draw funds, repay, and re-borrow as needed. This makes HELOCs ideal for ongoing expenses like home renovations or education costs. Another key factor is relatively low interest rates, which hover around 9% in 2024, making HELOCs more affordable than personal loans or credit cards. Tax benefits further boost their appeal, as interest on HELOCs remains deductible when used for home improvements. Key drivers behind the HELOC boom include rising home prices, which have been increasing by 6-7% annually, and rising consumer debt—households in 2024 carry an average debt of $104,215. Many are using HELOCs for debt consolidation, lowering monthly payments and saving on interest costs. Additionally, HELOCs provide a financial safety net in uncertain times, offering flexibility while preserving liquidity.   Q: Can you explain the market dynamics behind the surge in HELOCs? The resurgence of HELOCs in 2024 is driven by a convergence of macroeconomic factors, including rising interest rates, limited housing inventory, and substantial home equity accumulation. Mortgage rates in 2024 are significantly higher, ranging between 6% and 7%. Homeowners with sub-4% mortgages are reluctant to refinance, resulting in a 40% decline in refinancing applications. Consequently, HELOCs have become the preferred option for accessing home equity without losing favorable mortgage terms. Limited Housing Inventory and Rising Home Prices are key factors. The housing market faces a severe inventory shortage, with just 2.7 months of supply available. This has driven home prices up by 7.3% in 2023 alone. As home values rise, so does home equity, prompting more homeowners to use HELOCs to tap into this wealth. Inflation and Economic Flexibility have made a huge impact. With inflationary pressures making it harder for families to manage rising costs, HELOCs offer a low-cost, flexible credit line that can be used as needed. The revolving nature of a HELOC, where interest is only paid on what’s borrowed, makes it a cost-effective and attractive financing option in uncertain economic times.   In conclusion, Generative AI is transforming the Fintech landscape, particularly in home lending with the rise of HELOCs. By enhancing operational efficiency and enabling personalized solutions, AI empowers lenders to meet the evolving needs of homeowners. As rising home equity and demand for flexible financial products grow, HELOCs are set to play a crucial role in navigating economic uncertainty. This synergy between Generative AI and home equity lending will foster a more responsive financial ecosystem, benefiting both lenders and consumers. FAQs – Tavant Solutions How does Tavant enhance HELOC processing through AI technology?Tavant uses AI to automate property valuation, streamline income verification, and accelerate credit decisions for HELOCs. Their intelligent platform reduces processing time from weeks to days while maintaining rigorous risk standards and compliance requirements. What specific AI features does Tavant offer for HELOC lenders?Tavant provides AI-powered automated valuation models (AVMs), intelligent document extraction, risk-based pricing algorithms, and predictive analytics for HELOC portfolio management. These features enable lenders to offer competitive rates while

AI-Powered Claims Automation: Revolutionizing Warranty Management with AI

Warranties are standard for most mechanized products, providing customers with options for repair, replacement, or refunds. Research shows that warranty claims and associated service costs typically account for 2% to 15% of net sales. For large companies, this can mean billions of dollars in annual expenses. Even for medium and small manufacturers, this cost has a considerable impact on the bottom line. Gone are the days when warranty claims were manually assessed, and the details recorded and processed on paper. Original Equipment Manufacturers (OEMs) can now leverage AI-powered tools to streamline operations, enhance efficiency and accuracy, reduce manual effort and costs, and boost customer satisfaction. Additionally, automating workflows with AI also improves the ability to detect and eliminate fraudulent claims. Claims automation ensures a faster claims procedure, enhanced fairness, personalization, and easy availability. With minimal to no requirement for human intervention, the role of AI in claims significantly reduces errors and redundancies, ultimately resulting in warranty efficiency improvement. Tavant’s TMAP Warranty.AI: A Game-Changer in Warranty Management Tavant’s TMAP Warranty.AI is designed to help OEMs navigate the complex world of warranty claims with ease. Powered by AI, this solution reduces warranty costs by up to 5%, automates and streamlines processes, and improves after-sales service—leading to enhanced customer satisfaction and a significant return on investment (ROI). Suspect Claim Analytics: Suspect claims are managed efficiently through a systematic process of verifying and flagging suspicious claims, real-time claim scoring, automatic claim approval, dealer rankings, image-based anomaly detection, and rankings-based warranty audits. QA Code Predictions: Through predictive analysis, the tool accurately forecasts the likelihood of defects or failures in different products with the help of symptom code prediction, defect code prediction, remedy code prediction, and warranty system integration with multi-language support. Peer Averaging: Through labor cost analysis, labor hour clustering, parts claims clustering, and cluster value comparison, the tool helps competitor analysis for establishing benchmarks in warranty claims. Warranty Analytics: This feature covers warranty metrics and analysis of channel, claim, and return of parts. Additionally, analyst performance, warranty registration, dealer performance, supplier recovery, and claims forecast are other aspects analyzed in detail. Machine Failure Cluster: From cluster size monitoring to assessment of failure metrics and real-time product monitoring, it is possible to understand the root cause of machine faults better than ever before. Service Parts Management & Contract Analysis: Through a set of operational metrics, portfolio & pricing analysis as well as parts & service demand forecasting, the tool efficiently handles contracts and service allocation. Quality & Reliability: Weibull analysis, quality KPIs, and product quality systems are important components in ensuring the quality and reliability of outputs. Intelligent Search: Intelligent search forms the core of any AI tool, and with a focus on warranty claims, features like contextual search, Q&A, and knowledge management play a key role in the verification process. Field Service Analytics: A range of fieldwork analyses including service ticket prioritization, trend analysis, work order & inventory report generation, and service technician performance analysis, among others. Dealer Certification: Service & sales certification, performance-based dealer classification, points-based categorization, category-based incentive schemes, and dealer business improvement identification The Power of AI in Claims Automation Tavant’s TMAP Warranty.AI can automate and streamline warranty claims with incredible efficiency through detailed and fast data analysis, helping OEMs make real-time, data-driven decisions. As TMAP Warranty.AI continues to learn from new data, its performance improves over time, further enhancing the warranty management process. Key Benefits for OEMs Using TMAP Warranty.AI: Improved Efficiency and Cost Savings: Automation eliminates routine tasks, enabling faster and more accurate claims handling. TMAP Warranty.AI automates routine tasks involved in the process and encourages decisions backed by accurate data, businesses can expect efficiency gains, lower expenses, cost savings in warranty, and better customer satisfaction. Enhanced Customer Service: Through AI-powered automation in TMAP Warranty.AI, expect claim settlement to be speedy and accurate. Customers will appreciate the lower waiting period and the hassle-free resolution process that increases brand loyalty and trust. Future trends in warranty automation Warranty management no longer needs to be a manual, labor-intensive process. Tavant’s TMAP Warranty.AI is an innovative, advanced solution designed to automate claims. The tool delivers automation excellence through its ability to learn from data, making it a critical asset in modern warranty management. AI-powered solutions like Warranty.AI go beyond offering the convenience of automating tasks – they offer intelligent solutions backed by identifying patterns and trends and offering data-driven insights. While inspired by traditional manual processes, these advanced tools streamline the entire system, enabling faster and more accurate claims resolution. By leveraging data-driven approaches, OEMs can handle a higher volume of claims with precision, enhancing both operational efficiency and customer satisfaction. By adopting AI early, OEMs can not only reduce costs but also deliver faster, more reliable service, making AI a crucial investment for the future of warranty management. Reach us at [email protected] to schedule a demo on TMAP Warranty.AI

Bringing gpt-2 to android with kerasnlp: odml guide

Android developers and AI enthusiasts are exploring the prospect of running powerful language models like GPT-2 directly on your Android device. The KerasNLP workshop from IO2023 has all the insights one might need to make it happen. Here’s a detailed guide to integrating GPT-2 as an On-Device Machine Learning (ODML) model on Android using KerasNLP. Why use ODML on Android? On-device machine learning offers several benefits: Latency: No need to wait for server responses. Privacy: Data stays on the device. Offline Access: Works without internet connectivity. Reduced Costs: Lower server and bandwidth costs.   Setting up the environment: The first requirement in setting up an environment is the need for a robust setup on your development machine. Developers need to make sure they have Python installed along with TensorFlow and KerasNLP. Install KerasNLP using: pip install keras-nlp Loading and Preparing GPT-2 with KerasNLP KerasNLP simplifies the process of loading pre-trained models. For the developers’ purposes, they should load GPT-2 and prepare it for ODML. from keras_nlp.models import GPT2 model = GPT2.from_pretrained(‘gpt2’) Fine-tuning GPT-2: To make the model more relevant for one’s Android application, fine-tuning on a specific dataset is recommended. # Example of fine-tuning the model model.fit(dataset, epochs=3) Converting the model for Android: Once the model is fine-tuned, the next step is to convert it into a TensorFlow Lite (TFLite) format, which is optimized for mobile devices. import tensorflow as tf converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() # Save the model to a file with open(‘model.tflite’, ‘wb’) as f: f.write(tflite_model) Integrating the TFLite model in Android: Step 1: Add TensorFlow Lite dependency Add the TensorFlow Lite library to your build.gradle file. implementation ‘org.tensorflow:tensorflow-lite:2.7.0’ Step 2: Load the model in the Android app Place the model.tflite file in the assets directory and write code to load and run the model using Kotlin. suspend fun initModel(){ withContext(dispatcher) { val loadResult = loadModelFile(context) // Load the model file // Check if loading was successful if (loadResult.isFailure) { val exception = loadResult.exceptionOrNull() return@withContext when (exception) { is FileNotFoundException -> //Handle FileNotFoundException else -> //Handle Exception } } // Initialize the interpreter with the loaded model val model = loadResult.getOrNull() isInitialized = model?.let { interpreter = Interpreter(it) } } } Running inference: Prepare your input data and call the runInterpreter method to get predictions. @WorkerThread private fun runInterpreter(input: String): String { private val outputBuffer = ByteBuffer.allocateDirect(OUTPUT_BUFFER_SIZE)   // Run interpreter, which will generate text into outputBuffer interpreter.run(input, outputBuffer)   // Set output buffer limit to current position & position to 0 outputBuffer.flip()   // Get bytes from output buffer val bytes = ByteArray(outputBuffer.remaining()) outputBuffer.get(bytes) outputBuffer.clear() // Return bytes converted to String return String(bytes, Charsets.UTF_8) } Final thoughts  Integrating ODML with KerasNLP and TensorFlow Lite can transform one’s Android device into a powerhouse for real-time NLP tasks. Whether it’s for chatbots, language translation, or content generation, the capabilities are now in the palm of your hand.

Empowering Farmers: The Realm of Agritech Mobile Applications

Technology has become central to ushering in a new era of efficiency and sustainability in the evolving agricultural landscape. One of the key players in this transformation is the development of mobile applications tailored specifically for the Agritech industry. These applications are not just changing how farmers work; they are also cultivating a revolution.  Mobile technology has seamlessly integrated into agriculture, offering solutions to longstanding challenges such as soil degradation, resource scarcity, pollution, and water consumption. With the global adoption of smartphones and tablets, farmers now gain real-time access to critical information and can efficiently manage tasks that were once paper-based. From inventory management to monitoring crop yields and financial records, operations can be conducted remotely, significantly boosting efficiency. Moreover, enhanced communication through apps and messaging platforms ensures seamless connectivity with employees, customers, and suppliers, improving productivity and responsiveness, especially in critical situations. Mobile technology has also revolutionized decision-making processes by equipping farmers with real-time data and analytical tools to make informed choices around planting, harvesting, and marketing strategies. Socially, mobile technology nurtures a supportive farming community through platforms like social media, facilitating knowledge sharing and resource accessibility among peers, particularly in rural areas.  This blog explores mobile applications’ pivotal role in modern agriculture and highlights how Tavant leverages technical expertise to develop advanced solutions tailored to agricultural needs.  Mobile Applications: Transforming Agricultural Practices  Mobile apps have piloted a new generation of efficiency and innovation in agriculture, offering farmers instant access to vital information and tools. Key benefits include:  Real-Time Information Access: Farmers can access up-to-date weather forecasts, market prices, and agricultural news, empowering informed decision-making in crop management, irrigation scheduling, pest control, and optimal timing for market sales.  Precision Farming in Your Pocket: Gone are the days of manual guesswork in agriculture. With the development of mobile applications, farmers can now quickly implement precision farming techniques. These apps leverage GPS technology, sensors, and data analytics to give farmers insights into soil health, weather patterns, and crop conditions. Utilizing this information, farmers can now make informed decisions about planting, irrigation, and harvesting, leading to optimal resource utilization and increased yields.  Crop Monitoring from Anywhere: Agritech mobile apps empower farmers to monitor their crops closely, even when they are miles away from the fields. By integrating drones and satellite imaging, these applications offer real-time visualizations of crop health, enabling early detection of diseases, pests, or nutrient deficiencies. This proactive approach allows farmers to take timely corrective measures, minimizing crop loss and ensuring a healthier harvest.  Financial Farming: Managing finances is a crucial aspect of agriculture, and mobile applications are making this task more accessible and efficient. From budgeting and expense tracking to accessing microloans and insurance, farmers can handle their financial affairs conveniently through these apps, improving financial literacy and enhancing the overall economic sustainability of their farming operations.  Agricultural Extension Services: Mobile apps offer access to expert advice, training modules, and best practices, enhancing farming techniques, productivity, and sustainability through knowledge-sharing platforms.  Cultivating Connectivity: Agriculture has traditionally been a solitary endeavor, with farmers toiling away in their fields. However, Agritech mobile applications foster connectivity among farmers, researchers, suppliers, and consumers. These apps serve as virtual marketplaces, allowing farmers to connect with buyers, negotiate prices, and streamline the supply chain. Real-time communication ensures improved collaboration, transparency, and trust among stakeholders.    Enhancing Mobile Applications: Technical Expertise at Tavant  Oue, committed to excellence, has helped Tavant develop mobile applications that exceed client expectations. Our solutions integrate advanced features to enhance user experience and application performance, including:  Biometric Authentication: Ensure security with fingerprint or facial recognition and allow only authorized access to sensitive information.  Pre-Caching of Data: Optimize performance by anticipating user needs and pre-loading relevant data to ensure smooth operation in low-connectivity environments.  Responsive Design: Create interfaces that adapt seamlessly across devices to prioritize usability and accessibility for diverse user preferences.  State-of-the-Art Notification System: Deliver real-time updates and announcements directly to users to enhance engagement and user connectivity.  Firebase-Powered Analytics: Leverage Firebase for comprehensive analytics on app usage, interactions, and performance metrics to enable informed decisions and continuous improvement.  Sharing of Reports and Downloadable Content: Facilitate easy sharing of reports, images, PDFs, and Excel files to streamline collaboration and productivity among stakeholders.  OpenID Standard for Authentication: Implement robust authentication and authorization protocols to ensure secure access and compliance with industry standards.    Developing Mobile Applications for Agriculture: Tavant’s Approach  Tavant’s approach to developing mobile applications is rooted in improving collaboration and expertise:  Requirement Gathering: Work closely with agriculture experts to define features such as weather forecasting, crop monitoring, market integration, and educational resources.  Design and Prototyping: Visualize app functionalities and UI design through wireframing and prototyping, emphasizing intuitive navigation and offline capabilities.  Technology Stack Selection: Choose optimal technologies like Flutter or React Native for scalability and performance across diverse devices and network conditions.  Development and Testing: Iteratively implement features with rigorous testing to ensure bug-free functionality, data security, and optimal performance.  Deployment and Maintenance: Launch apps on major platforms and continuously update them based on user feedback, technological advancements, and evolving agricultural practices.    Conclusion  Mobile applications are pivotal in advancing agriculture by equipping farmers with essential productivity, profitability, and sustainability tools. At Tavant, our integration of advanced technologies and agricultural expertise ensures tailored solutions that empower farmers to navigate challenges effectively and thrive in a dynamic digital landscape. As mobile technology rapidly evolves, so does our commitment to innovation, driving transformative change in the agriculture sector worldwide. 

Leveraging GenAI in Ideation and Planning Phase of Mobile SDLC

In the ideation and planning phases of the Software Development Life Cycle (SDLC) for mobile applications, GenAI offers transformative capabilities that simplify and enhance these critical stages. By automating idea generation, analyzing industry trends, conducting comprehensive market research, creating detailed user personas, and fostering creativity, GenAI ensures that the resulting applications are innovative, user-centric, and well-aligned with current market needs and trends. This article delves into how GenAI tools can be leveraged during the ideation and planning stages of various use cases within the AgTech domain. These insights will be particularly useful for designing business solutions tailored to farmers’ needs. Example: A farm management mobile application serves as a comprehensive software solution aimed at helping farmers and agricultural businesses streamline their daily operations. Such an app could encompass features that track and monitor various aspects of farm management, including crop yields, livestock health, and inventory levels. Let’s explore how GenAI contributes to different areas of this phase in the SDLC: 1. Automated Brainstorming: GenAI tools, such as ChatGPT, can generate a diverse array of ideas based on initial inputs, significantly broadening the scope of possibilities. Example: Consider a Crop Management App. GenAI could suggest features like real-time satellite imagery for assessing crop health, automated irrigation scheduling, or AI-driven pest and disease prediction systems.   2. Concept Development: Once basic ideas are generated, GenAI can further develop and refine these concepts, adding depth and detail to initial thoughts. Example: Enhancing Crop Monitoring could involve integrating IoT devices for real-time soil moisture monitoring, utilizing drone imagery for detailed crop health analysis, and employing AI algorithms for predictive analytics on crop yields.   3. Trend Analysis: GenAI has the capability to analyze vast amounts of data from various sources, identifying current trends and predicting future opportunities. Example: Analyzing social media data could reveal a rising trend in organic farming, while market research might identify a growing demand for apps that promote sustainable farming practices.   4. Market Research and Competitor Analysis: GenAI can rapidly assess competitor applications, pinpointing their strengths, weaknesses, and uncovering potential market gaps. Example: For an Agribusiness Insights App, GenAI might identify that competitor apps excel in weather prediction features but lack real-time pest detection capabilities. This opens up opportunities to integrate AI-driven pest detection and offer more comprehensive soil health analysis.   5. Generating User Personas and Stories: GenAI can create detailed user personas by analyzing demographic data, user behaviors, and preferences, which are essential for developing user-centric applications. Example: A user persona might represent a small-scale organic farmer seeking eco-friendly pest control methods. The corresponding user story could be: “As a small-scale farmer, I want an app that provides natural pest control solutions so I can maintain my organic certification.”   6. Enhanced Creativity and Innovation: GenAI continually stimulates creativity and innovation by offering a steady stream of fresh ideas and new perspectives. Example: For a Precision Agriculture App, potential features might include real-time analysis of drone imagery, automated irrigation control based on soil moisture data, and AI-driven crop health assessments.   Conclusion: By leveraging GenAI in the ideation and planning phases of the SDLC, particularly in the AgTech domain, developers and businesses can craft mobile applications that are not only technologically advanced but also precisely aligned with the needs of farmers. The integration of automated brainstorming, concept development, trend analysis, market research, user persona generation, and innovative ideas ensures that the resulting applications are robust, user-friendly, and equipped to meet the evolving demands of the agricultural sector.

Harnessing the Power of Generative AI in Mobile Application Development

Generative AI stands out with its unique ability to create original content by learning from vast datasets, making it inherently proactive. In the realm of application development, Generative AI heralds a new era of automation and creativity, enabling the generation of code, design elements, and even project plans with minimal input. Mobile application development involves a series of steps and processes for designing, building, and deploying software applications for mobile devices. Let’s explore how Generative AI can be utilized throughout the Software Development Life Cycle (SDLC) in mobile application development. Ideation and Planning Phase Generative AI models have the capability to extract and synthesize requirements, identify potential gaps, and suggest additional requirements based on patterns learned from extensive datasets. By analyzing historical user feedback data, these models can generate new requirements, automate the writing of requirements, and create detailed user stories. This streamlines the initial phases of mobile application development, ensuring a comprehensive and user-centric approach. Wireframing and Design Phase Generative AI can significantly impact the design phase by generating design elements, user interfaces, and architectural suggestions. For UI/UX design, GenAI tools can produce multiple design options based on brief descriptions or sketches, allowing designers to explore various concepts quickly. For app architecture, GenAI can suggest design architectures based on project requirements, including scalability, security, and maintainability considerations. Development Phase Developers can leverage Generative AI to generate boilerplate code, jumpstarting projects swiftly and tackling unfamiliar challenges with ease. AI-powered suggestions can significantly reduce development time, leading to more secure product releases and shorter time-to-market. Specifically, Generative AI can: Assist in code generation and improvement. Identify potential bugs. Generate bug fixes, leading to cleaner and more efficient code. Detect potential errors, such as security vulnerabilities, performance bottlenecks, and code smells. Aid in the creation and execution of unit test cases, improving code quality.   Testing Phase Generative AI can revolutionize the testing phase of the SDLC by automating test case generation and analysis. Large language models (LLMs) can analyze code and generate comprehensive test cases, reducing manual errors and testing time. AI tools can also visually test UI screens by comparing expected and actual screenshots to detect discrepancies. Deployment Phase Deployment involves delivering the finished software to users. Generative AI can optimize this process by analyzing deployment patterns and generating automated deployment scripts, pipelines, and workflows. Furthermore, it can outline the necessary steps for successful deployment. Once the deployment pipeline is created, the entire app deployment process can be automated, allowing the app to be released to beta or production environments based on configuration setups on Apple or Google Play stores. Maintenance and Update Phase Post-deployment maintenance is crucial for addressing bugs, improving performance, and updating features. Generative AI can assist in performance monitoring and provide remedy suggestions. It can also generate documentation, suggest refactoring, and help identify the root cause of issues in the code. Generative AI-driven monitoring systems can continuously monitor deployed applications for performance issues, errors, and security vulnerabilities. Conclusion The role of Generative AI in the mobile SDLC is transformative, enhancing every phase from ideation to maintenance. By automating and optimizing key processes, Generative AI boosts productivity, improves software quality, and accelerates development. Its ability to streamline tasks, generate insights, and provide innovative solutions makes it an invaluable asset in modern mobile app development.

Harnessing the Power of IoT Data: A Holistic Approach

In our hyper-connected world, the Internet of Things (IoT) isn’t merely a buzzword—it’s a transformative force reshaping industries and business landscapes. At its core lies a treasure trove of data generated by sensors, devices, engines, and machines. But here’s the untold story: Historic IoT data, when combined with insights from other systems, becomes a game-changer. The Underutilized Library of Data Challenge: Companies invest substantial resources in IoT and Telematics hardware, software, and data connectivity. Yet, all too often, the historical data collected remains underutilized. It’s like having a vast library of books but only reading the latest bestsellers. Solution: Enter IoT data analytics. By delving into historical data, companies can uncover patterns, correlations, and anomalies. Predictive maintenance becomes a reality—machines signal when they need attention before they break down. But here’s where the magic happens: Imagine joining this historic data with insights from other critical systems. The Power of Integration CRM (Customer Relationship Management): Scenario: Your sales team logs interactions, customer preferences, and feedback. Integration: Combine CRM data with historic IoT data. Suddenly, you understand how equipment performance impacts customer satisfaction. You tailor service offerings based on usage patterns. You increase dealer sales opportunities by understanding customer use history and uncovering their needs proactively.   Parts Management and Warranty Systems: Scenario: Spare parts inventory management is a puzzle. Overstocking ties up capital; understocking leads to downtime. You see an uptick in parts use but can’t correlate it. Integration: Historic IoT data reveals which components fail most frequently. Now, your parts management system stocks intelligently. Predictive maintenance reduces emergency orders. Warranty costs are controlled. Proactive product improvement becomes a reality!   Pricing Systems: Scenario: Pricing decisions are often gut-driven or market-based. Integration: Overlay historic IoT data. Understand how equipment usage affects costs. Optimize pricing based on real-world performance.   3.Beyond Silos: Holistic Insights Challenge: Businesses often operate in silos—departments, regions, and customer segments isolated from one another. Solution: IoT data bridges the gaps. Imagine an agricultural equipment manufacturer learning that a specific tractor model excels in vineyards but struggles in wheat fields. Armed with this insight, they fine-tune their offerings. Dealers personalize service recommendations based on usage patterns. Customers benefit from products designed for their unique needs. How Do You Start? The challenge of unlocking historic data’s benefits can be daunting, but you know your high impact use case already, don’t you? Take a moment, write it down, and consider all the platforms and systems in your organization that hold valuable information. Now envision the power of bringing all that data together to solve your problem! Find a trusted partner who can guide you through this journey and help you fast-find the ever-returning ROI that will benefit your business for years to come. Conclusion: The Data-Driven Future IoT data isn’t just about sensors and connectivity; it’s about unlocking actionable intelligence. As businesses embrace data analytics, they move from reactive to proactive, from isolated to interconnected. So, next time you see a sensor blinking quietly in the corner, remember—it’s not just collecting data; it’s shaping the future of business. About the Author: Jon Kent lives in the Metro Atlanta area with his family. He is an IoT, Telematics, and Field Service Technology thought leader and enthusiast. His 20+ year career experiences have brought him to Tavant, a global technology organization with U.S headquarters in Santa Clara, CA.  Jon works within the Tavant TMAP Product Group, that focuses on finding value in a company’s data across any number of systems, including IoT / Telematics, CRM, ERP, Warranty, Parts, Service Case, Contract Management, and Field Service. For more information or to schedule a conversation, please visit: TMAP | Tavant

Crafting a Culture of Quality-Driven Development

The world of software development is often weighed down by one metric: defects. Our obsessions are bug fixes, crash corrections, and error reduction. While this emphasis on technical issues is understandable, it gives a false impression of the quality of the software. Usability, maintainability, scalability, security, and user satisfaction are all components of true quality, which goes well beyond the mere absence of bugs. The quest for quality in the dynamic field of software development extends well beyond eliminating defects. Establishing a culture that prioritizes quality, continual improvement, and a commitment to delivering products that not only fulfill but also surpass expectations is key. If we’re going to build truly unique software, we need to change our thinking. This does not mean completely ignoring bugs but placing them within a broader context of quality attributes. So, how do we escape this trap and build a culture where quality is not just an aspiration but a core value? Here are some fundamental principles of a quality-driven development culture: Shifting Mindsets: From Testing to Quality Assurance: Testing is an essential part of ensuring a product’s quality, but a quality-driven culture goes beyond simply identifying and resolving bugs. It demands a shift in mindset from mere testing to comprehensive quality assurance. This change entails taking preventative steps like code reviews, design inspections, open communication around potential issues, prioritizing refactoring, and recognizing accomplishments in quality alongside product launches. Embracing Continuous Improvement: Continuous improvement is essential to a quality-driven development culture. View defects not as failures but as opportunities to learn and improve. Analyze their root causes, implement preventative measures, and communicate the team’s lessons learned. Motivate your team to embrace an attitude of continuous improvement and learning. Frequent feedback loops, retrospectives, and the integration of lessons from past projects create an environment that develops and changes with every development cycle. Metrics Beyond Bugs: While tracking and fixing bugs is crucial to maintaining software quality, it doesn’t provide a complete picture of a project’s success or health. Use insightful measurements that go beyond the conventional defect count. Measure things like user satisfaction, code coverage, and performance benchmarks. These indicators give you a comprehensive picture of your product’s caliber and can point your team toward areas that need work and development. Investing in the Professional Development of Team Members: A culture that prioritizes quality understands the value of supporting team members’ professional growth. Encourage certifications, workshops, and training courses that improve their abilities. By investing in training, team members remain updated with evolving technologies and learn better ways of doing things. This could lead to greater productivity and creativity. Shared Ownership: Testers and QA teams aren’t the only ones accountable for quality. Everyone engaged in the development process—from developers and designers to executives and product managers—has a shared responsibility for it. Encourage open lines of communication between the development team, stakeholders, and other departments. Promote cross-functional collaboration to ensure that everyone is on the same page with the overall objective of producing a high-quality product. Automation is Key: Use automation to expedite monotonous work so your team can concentrate on more intricate, high-value jobs. In addition to lowering the risk of errors, automated testing, continuous integration, and deployment pipelines also make development processes more dependable and efficient while freeing up human resources for more strategically important tasks. Conclusion In summary, creating a quality-driven development culture involves more than just focusing on defects; it also entails adopting a holistic approach to excellence, which calls for dedication, teamwork, and readiness to continuously learn and adapt. Your team will be able to constantly surpass the expectations of your stakeholders and users by cultivating this culture. The benefits of quality-driven development are well worth the continued journey. Let’s move beyond defects and create software that surpasses users’ expectations and stands the test of time.

Digital Agriculture: opportunities and challenges in the oil palm industry

Introduction: Digital Agriculture, as the name suggests, incorporates technology and data-driven approaches to improve farming practices and helps make informed decisions. Some applications include crop health monitoring, customized inputs (water, fertilizers, etc., to specific areas of the farm based on soil and weather data), yield prediction, labor management, etc. The journey from traditional to digital agriculture continues to advance and address the market demands of the growing population. Let’s discuss one of the use cases where Tavant helped a client step toward their digital journey in the oil palm industry. The oil palm industry plays a significant role in the global agricultural landscape with the extensive use of palm oil in many food products, personal care items, biofuels, etc. Indonesia and Malaysia are the top producers, contributing to ~85% of the world’s palm oil production, with a significant amount of its agricultural land dedicated to oil palm cultivation. Opportunities: The use case focuses on the precise counting of Fresh Fruit Bunches (FFB) from the plantation by leveraging AI technology that offers the following benefits to the farmers and stakeholders to make data-driven decisions. Yield Estimation – Enable the team to understand the yield increase or decrease over time and analyze the factors affecting the same. Harvest Planning – Plan harvesting operations more effectively (Time and frequency), thus preventing the harvesting of overripe or underripe bunches. Resource Allocation – Use the available resources such as equipment, labor, and storage facilities efficiently. Supply Chain Management – Provide accurate information to processors, traders, and distributors to improve logistics and market planning. Quality Control – Identify the exact number of FFBs (fresh fruit bunches) based on grades to minimize the likelihood of mixing different grades.   Challenges: This section will highlight the challenges faced during various implementation phases and an end-to-end demo of the proposed solution. Data Collection: Data Collection is crucial in any use case, as the data’s quality and integrity determine the solution’s efficiency. Major challenges include, Identifying the best way to capture data (Image/Video). Orientation and distance of the camera from the object. Devices used for data capture, such as drones and handheld devices (smartphones, tablets, etc.), have their associated pros and cons.   Drones can capture high-resolution data and images from different angles, but the number of flights and time taken is high due to battery limitations. On-ground conditions are also a factor, making it imperative to identify drone models that can suitably fly under canopies and between trees for better data capture. Handheld (HH) – The quality of the image (Resolution, Zoom Level, Brightness, etc.) will vary greatly depending on the device model; if the tree’s height is too high, it won’t be feasible to use HH devices. A workforce that is skilled in data collection techniques is imperative. Technical infrastructure that collects and transmits data in real-time is also crucial. Weather conditions can affect the quality of data collection activities.   Data Labelling: Data labelling plays a significant role in model performance. It is essential to have discussions with domain experts to, Understand and define annotation guidelines to maintain consistency. It is highly subjective, as the interpretation of images will vary across annotators. It is time-consuming and iterative based on the datasets/results evaluation volume. Complex annotations, such as images containing occlusions, overlapping bunches, flowers, bunches from BG trees, etc., should be considered. Having a class imbalance can affect the results. It requires identifying the right tool for annotation activity while considering data security.   Implementation: Various factors can make implementation challenging, such as: Computational Requirements – The size of the datasets depends on the need for GPU-based instances with high memory and storage capacity. Preprocessing – Categorizing the better-quality image for training (without blur, too dark, out of focus, etc.) requires multiple techniques to be tried out, and identifying the best options to apply across the images can prove challenging. Model Architecture – Identifying the best architecture that suits the dataset is done through multiple experiments. Others – Accurately identifying the rare instances (due to class imbalance) and segmenting smaller or crowded objects due to limited pixel information will be challenging. Post-Processing – Prediction results might have False Positives (FP) (E.g., Flowers getting detected as fruit bunches, etc.) and need a post-processing script to evaluate the results and generate metrics in the required format. Manually checking each image for FP identification is time-consuming and cumbersome and must be automated.   Solution Overview: The solutions proposed to these challenges include: Instance Segmentation model – To Detect and Segment FFB’s Multi-Object Tracking (Required if the input is Video) – To track the bunches of interest and get precise FFB Count Color Analyzer – To categorize the color proportions from the segment per business needs.   Tech Stack: Instance Segmentation model – SWIN Transformer from Microsoft Research (State of the Art Model) Multi-Object Tracking (Required if the input is Video) – ByteTrack or StrongSORT (State of the Art Model) Color Analyzer – Traditional Computer Vision techniques   Conclusion: Even though there are a lot of challenges in the digital agriculture journey, farmers are optimizing practices by incorporating the power of technology and data-driven decisions, leading to a more sustainable future for agriculture.

Build your content through Kentico in the Agtech space

Tavant, as a premier provider of Kentico-based solutions, understands the agriculture industry’s unique needs. Our expertise in developing tailored Content Management Systems (CMS) caters specifically to retailers, brokers, agencies, farmers, growers, and other stakeholders in the agriculture sector. With Kentico as a digital platform, you will receive future-proof tools with stable and secure solutions that help you meet your digital goals at a rapid pace. Our comprehensive SEO website development services optimize your online presence to improve search engine ranking and engage with the right target audience. With our deep understanding and expertise in the Agtech landscape, we can create a website highlighting your products and services that educates and engages visitors. Key Features of our Kentico-based Agtech Website Development: Customized Content Management System: Build a user-friendly and scalable CMS, tailor-made to address the specific challenges faced by Agtech retailers, brokers, agencies, farmers, and growers, allowing you to efficiently manage your website content, product catalogs, blog posts, articles, events, videos, podcasts, social media graphics, online courses, and much more. Mobile Responsive Design: With the increasing dominance of mobile devices, ensure website optimization for seamless viewing and interaction across various screen sizes. Guarantee the best user experience for your visitors, regardless of their device. E-commerce Integration: Our team can seamlessly integrate e-commerce capabilities into your website and enable you to sell agriculture products, seeds, and fertilizers effectively, manage orders, process payments, and track inventory. Empower your customers to purchase directly from your site, making it a convenient platform to access your offerings. Not only this, but you can also tailor and automate your checkout and payment processes to meet your customer needs. With integrated marketing automation techniques, you can boost your retailers’ revenue by nurturing cart abandoners or reminding customers to re-order their seasonal agriculture products. Search Engine Optimization (SEO): Online visibility can be crucial to success. Our SEO experts optimize your website structure, meta tags, keywords, and content to generate organic traffic and better your rankings on search engines, ensuring potential customers quickly discover your website. Engaging Content Creation: Our skilled writers create captivating and informative content to communicate your brand’s story and value proposition effectively. Through engaging blog posts, articles, and other media, we help you captivate and educate your audience while establishing thought leadership in the Agtech domain. Centralizing Your Digital Assets: Our team confidently helps you manage digital assets using Kentico, including your digital assets, images, videos, PDFs, and presentations in a single, unified place. The fully integrated Kentico’s Media Library helps you avoid the hassle of working with files and reduces workflow redundancies. They allow you to upload files of diverse types, formats, and sizes, along with their metadata, across various digital touchpoints in just a few clicks, reducing delivery times, speeding up work, and eliminating inconsistencies. Multilingual Content: We understand the importance of establishing your global brand. A robust online presence of multilingual website content is essential in today’s interconnected world. Kentico helps you translate your website into multiple languages that cater to customers’ needs and help grow your businesses in new markets. Kentico allows you to easily manage websites in English and many languages, including Spanish, Russian, Chinese, Arabic, and Eastern European. Managing Multiple Websites: With Kentico, you can work on multiple digital experiences under one umbrella. It provides you with a multisite management platform from a single login interface, allowing you to easily share content, objects, data, users, roles, and more across any number of managed websites that increases your productivity and deliver advanced scenarios, thereby sparing you from the hassle of accessing multiple applications with different login usernames and passwords. In the Cloud or On-Premises Presence: With Kentico, you can quickly deploy your websites in the cloud or on-premises to a Platform-as-a-Service cloud environment, an Infrastructure-as-a-Service (IaaS), or even a hybrid of the two! Regardless of where you go, you can retrieve the same website possibilities and seamless expansions from on-premises to the cloud when needed. Data Analytics and Reporting: We provide comprehensive analytics and reporting capabilities that track the performance of your website, e-commerce sales, user behavior, and marketing campaigns. This data-driven approach empowers your decision-making and optimizes your online strategies to drive growth. At Tavant, we have a proven record of successfully providing Kentico-based Agtech solutions. Our commitment to delivering high-quality websites is shaped by extensive industry knowledge, which helps us provide tailored solutions to meet your unique requirements. Partner with us for your Kentico Agtech content management system and SEO website development needs and experience the power of a professionally developed digital presence that drives results. Contact us today to get started.

The Ultimate Guide to TMAP Knowledge.AI: Elevating Aftermarket Efficiency with GenAI

Within the OEM and aftermarket industry, retaining knowledge is often met with hurdles such as decentralized data, high employee turnover, the absence of robust knowledge management systems, and customer satisfaction. Mastering product usage, service manuals, and troubleshooting procedures, alongside utilizing knowledge articles and videos, presents a formidable challenge in today’s dynamic business environment. Navigating this landscape requires more than just organizational prowess—it demands a strategic approach to harnessing knowledge effectively. Amidst this complexity, optimizing knowledge becomes paramount. By streamlining processes and enhancing operational effectiveness, organizations can not only improve their ability to tackle challenges but also elevate customer experience. TMAP AI-driven knowledge management offers a potent remedy for these challenges. By seamlessly integrating the power of GenAI and cutting-edge LLM models, OEMs can unlock unparalleled potential to streamline operations and enhance decision-making. This is where GenAI-powered TMAP Knowledge.AI steps in and can help transform how OEMs manage customer interactions, improve service organization competence, and drive revenue growth.   Why is TMAP Knowledge.AI significant in the OEM and aftermarket industry? TMAP Knowledge.AI is designed to cater to the diverse needs of different business functions within an organization, addressing specific pain points and streamlining operations that include: Technical services: For teams handling technical services, TMAP Knowledge.AI offers solutions to understand complex products, fault codes, and troubleshooting steps efficiently. It alleviates resource constraints by providing quick access to relevant information, such as the availability of parts and knowledge. Additionally, it aims to improve first-time fix rates, ensuring prompt resolution of technical issues. Warranty: Warranty processors benefit from TMAP Knowledge.AI by gaining a deeper understanding of service and product knowledge essential for processing warranties. It facilitates the verification of claim attachments, addresses inquiries, and simplifies warranty management processes. Customer support: Customer support teams can leverage TMAP Knowledge.AI to enhance first-call resolution rates and reduce onboarding time for new employees. With comprehensive knowledge of Customer 360 and product complexity, they can deliver personalized support for exceptional customer experiences. Sales: Sales teams can harness the power of TMAP Knowledge.AI to access essential data and knowledge effortlessly. Personalizing content and product offers, streamlining email communication, organizing data, and updating CRM systems seamlessly is possible, ultimately driving sales effectiveness. Parts: TMAP Knowledge.AI provides valuable insights and recommendations, including pricing suggestions based on various factors such as stock levels, competitor pricing, and promotions. It also facilitates automated parts reordering and alerts for safety stock, ensuring optimal inventory management. Dealers: Dealers benefit from TMAP Knowledge.AI by gaining access to additional service contracts and extended warranties for sale. They can validate or submit claims efficiently using serial numbers, while also receiving guidance on warranty creation steps and cost estimates for service. It offers tools for generating quick summaries, quotes, activities for the day, and automated report generation, empowering dealers to streamline operations and drive profitability.   How does TMAP Knowledge.AI work? TMAP Knowledge.AI harnesses the power of LLM models to automate knowledge extraction techniques such as document analysis and natural language processing (NLP). It enhances the search capabilities using semantic search and question answering, ensuring swift access to relevant information. TMAP Knowledge.AI excels in content summarization and generation, efficiently condensing lengthy documents into concise summaries and crafting comprehensive FAQs and training materials. This empowers teams with the knowledge they need and precisely when they need it. Furthermore, by integrating predictive maintenance and troubleshooting ML models with LLM functionalities, TMAP Knowledge.AI unlocks the ability to detect patterns within IoT data. This proactive approach enables one to anticipate and prevent potential failures, minimizing downtime and maximizing operational efficiency. Additionally, LLMs play a pivotal role in onboarding and training virtual customer support agents, enabling effortless navigation through unstructured data and delivery of exceptional service. Additionally, the knowledge-sharing options within the portal foster collaboration, facilitating seamless information exchange and collective problem-solving. With TMAP Knowledge.AI, businesses invest in more than just technology—they invest in a transformative solution that empowers teams, enhances operational efficiency, and drives unparalleled success.   With TMAP Knowledge.AI, maintaining a competitive edge is a breeze! TMAP Knowledge.AI transcends being a mere solution – it is a transformative force in the OEM and aftermarket industry. By furnishing precise responses to customer queries, it minimizes the necessity for human intervention, thus elevating the overall customer experience. Its proficiency in problem resolution allows companies to swiftly diagnose and address technical issues, essential for ensuring prompt repairs and service in the OEM sector. Not only does it bolster customer satisfaction, but TMAP Knowledge.AI also contributes to substantial cost savings by automating repetitive tasks like addressing common queries or offering troubleshooting assistance. TMAP Knowledge.AI stands as a versatile ally, empowering OEMs to navigate the intricate landscape of modern business with unparalleled agility and insight. Are you interested in knowing more? Get in touch today or request a demo.

World Agri-Tech Innovation Summit 2024

Introducing the World Agri-Tech Innovation Summit Overview With the upcoming London Agri-Tech event on the horizon, it seems timely and relevant to reflect on the recent US event and its key highlights. As we gear up for another round of insightful discussions and innovative showcases, understanding the advancements and learnings from the US event will provide valuable context and momentum. This recap will not only set the stage for the London event but also help us build on the progress made in the industry so far, ensuring we stay ahead in the rapidly evolving Agri-Tech landscape. The World Agri-Tech Innovation Summit isn’t just any conference; it’s a vibrant gathering that takes place twice a year, knitting together a tapestry of over 2,500 leaders and decision-makers across a broad spectrum of sectors, including food production, equipment manufacturing, farming, IT, and the investment community. At the heart of this two-day event is a shared passion for pioneering a future in agriculture that is both sustainable and innovative. Attendees have the unique opportunity to engage with global industry advisors, sparking conversations that transcend the ordinary. It’s a space where ideas bloom, solutions emerge, and new business ventures take root, all within the rich and diverse agricultural ecosystem. Importance The event is dedicated to helping agribusinesses advance and invest in technologies that build a more robust and resilient agri-food supply chain. Their sustainability-led focus encourages harnessing nature-based solutions that meet climate commitments and successfully commercializing technologies like AI, automation, biological inputs, and more. The event serves as the frontline to uncover innovations and investments propelling new value creation among leading businesses within the agricultural sector. Their commitment to agricultural success also expands towards start-ups, with their start-up arena designed for founders to ignite inspiration, foster knowledge exchange, and facilitate critical partnerships for future business prosperity. Key themes This year’s theme focused on innovation in food security, digitization, data agility, finance, Gen AI-powered agriculture, and sustainability. Day 1 highlights Day one kicked off with breakfast and opening remarks and swiftly transitioned toward our first speaker session, “Navigating Incentives for Decarbonization in Agriculture.” Thirteen speaker sessions, fourteen breakout sessions, and six start-up pitches ensured a dynamic day one for the attendees at the World Agri-Tech Innovation Summit. A notable highlight was Tavant’s speaker session with representative Vineet Durani, who shared their insights on “Advancing Real-Time Decision Agriculture: Data Integration, Equipment & Future Business Models.” The session conveyed strategies for developing real-time decision agriculture through equipment and data integration, contributing to an agribusiness’s efficiency, sustainability, and ROI. It also sheds light on the possible business models that enable agribusinesses to measure carbon emissions and capitalize on emerging carbon markets while aligning with their sustainability goals. Day 2 highlights Day two began with an early morning discussion and networking session featuring inspiring women leaders in the Agtech industry. A key difference between day one and day two was the roundtable sessions. While day one showcased more speakers and breakout sessions, day two boasted seven roundtable sessions with thirty-five different topics to ensure a lively afternoon filled with information and learning. Ten speaker sessions, three start-up pitches, and eight breakout sessions ensured the attendees always had something at hand, if not attending meetings or exhibition booths. Both days featured a wide range of exhibition booths in large hallways showcasing the latest technologies in Agtech, allowing attendees to stop by if anything caught their eye. Emerging trends in Agritech With some of the biggest names and leading agribusinesses sponsoring this event, you could find emerging trends and technologies in Agtech showcased in almost every corner. This year’s event significantly focused on data-integrated agriculture and some of the latest AI technologies used to improve profitability and efficiency, with Gen AI taking center stage. A notable highlight was Tavant’s “MyFarm” application demo at booth #47. It integrates AI with ADMA and enables farmers to gain critical field insights such as NDVI maps, carbon heat maps, nitrogen heat maps, and more to facilitate farm management and precision farming. You could also ask the application questions and receive answers and suggestions personalized to your farms through their generative AI, “The Agri Advisor”! Networking and Collaboration Opportunities With over 2500 attendees gathering for the two-day networking event, it’s impossible not to meet anybody, even if you tried! In addition, the attendees were provided access to an event networking application that allowed attendees to view exhibitor and attendee profiles. ‘Virtual Booths’ were set up on the application three weeks before the event, allowing attendees to glimpse what’s in store and set up meetings in advance if anything caught their attention. The application also allowed attendees to take virtual meetings in advance or book meeting slots such as 1-on-1 discussion rooms at dedicated tables and lounges at the event. The event also facilitated roundtable discussions, breakout sessions, and several networking breaks to promote networking further. These networking opportunities played a vital role in maximizing the time of all attendees at the event. The two days felt as if only a moment, from roundtable discussions, speaker sessions, and exhibits to meetings, start-up pitches, and breakout sessions, it was almost impossible to cover everything. Overall, the event was a huge success, as expected! Missed us at the event? Don’t worry! Over the years, the Tavant name has become synonymous with technologies such as farm management systems, grower advisory solutions, computer vision, and Gen AI. With the event’s theme aligning with sustainability and AI in agriculture, it’s safe to say we will see more of Tavant in the years to come.

Unlocking Home Equity: A Strategic Move for 2024

As we make strides into 2024, American homeowners find themselves amidst an intriguing landscape of financial opportunities, particularly concerning the utilization of home equity. The past year witnessed a surge in the popularity of Home Equity Lines of Credit (HELOCs), a trend poised to continue into the current year. But why the HELOC frenzy, and what makes 2024 an opportune moment for homeowners to tap into their home equity? Market Dynamics and Demand Surge In understanding the HELOC boom, we must dissect the current market dynamics. Rising interest rates coupled with a dwindling housing inventory have created a scenario where homeowners are opting to stay put, resulting in a substantial accumulation of home equity. However, this accumulation often contrasts with a lack of liquid savings, leaving homeowners in a peculiar position. Enter the HELOC, a financial instrument tailor-made for such circumstances. Flexibility and Favorable Rates HELOCs offer homeowners a flexible credit line, enabling them to access the equity in their homes without altering the interest rate on their primary mortgage. This flexibility is particularly attractive in a landscape where there are hopeful expectations for interest rates to decline. Variable-rate HELOCs with enticing introductory rates present a compelling proposition, aligning with the anticipated trajectory of interest rates in 2024. The competitive marketplace has ushered in a wave of consumer-centric benefits, including lower origination fees, special terms and improved rates. This increased competition empowers consumers to shop for the most favorable options tailored to their financial needs. Navigating Through Friction: The Role of Technology However, amidst the allure of HELOCs, challenges persist, notably in the application and approval process. The traditional timeline of two to six weeks for approval often falls short of meeting the immediate needs of borrowers. This discrepancy underscores the imperative for a streamlined and efficient process, one that addresses consumer expectations of ease, clarity and speed. In response to this demand, innovative solutions have emerged to bridge the gap between consumer expectations and industry capabilities. Advanced technologies, including AI-driven platforms, have played a pivotal role in streamlining the HELOC experience, offering consumers a smoother journey from application to approval. These solutions have significantly reduced the time and complexity traditionally associated with underwriting processes. HELOC vs. Alternatives: A Comparative Advantage When evaluating the merits of HELOCs against alternative financial instruments, several key advantages emerge. Compared to home equity loans, HELOCs offer unparalleled speed of origination and availability, aligning with the urgency often associated with financial needs. Additionally, the variable rates characteristic of HELOCs, especially in the context of projected rate drops, provide homeowners with a strategic advantage in managing their borrowing costs. In contrast to credit cards, HELOCs offer lower interest rates and structured repayment periods, ensuring greater financial stability and long-term planning. Furthermore, the potential tax deductibility of HELOC interest payments further enhances its appeal, setting it apart as a financially astute choice for homeowners. As we navigate the financial landscape of 2024, the strategic utilization of home equity emerges as a compelling option for American homeowners. HELOCs, with their flexibility, competitive rates and technological advancements, stand as a beacon of opportunity amidst a sea of financial choices. By tapping into their home equity intelligently, homeowners can unlock a world of possibilities, realizing their financial aspirations while safeguarding their most valuable asset—their home. (*Article was originally published on MBA Newslink) FAQs – Tavant Solutions How does Tavant help lenders unlock home equity opportunities in 2024?Tavant provides specialized home equity lending platforms with automated valuation models, streamlined application processes, and flexible product offerings. Their technology enables lenders to quickly assess property values, evaluate borrower equity positions, and capitalize on the growing home equity market with efficient, competitive HELOC and home equity loan products. What strategic advantages does Tavant offer for home equity lending in 2024?Tavant offers real-time market data integration, predictive analytics for risk assessment, automated compliance management, and personalized customer experiences. Their platform helps lenders expand market share, improve approval rates, and provide competitive home equity solutions that meet diverse borrower needs in the current market environment. Why is 2024 a strategic year for home equity lending?2024 is strategic for home equity lending due to accumulated home value appreciation, elevated interest rate environment making home equity attractive compared to other credit options, increased homeowner equity positions, and growing consumer awareness of home equity as a financing tool for various needs. What home equity opportunities exist in 2024?Key opportunities include debt consolidation for high-interest credit, home improvement financing, education funding, investment capital, emergency funds, and business startup funding. Rising home values have created significant equity that homeowners can access for various financial goals. How can lenders capitalize on the home equity market in 2024?Lenders can capitalize through competitive product offerings, streamlined application processes, marketing focused on equity awareness, technology that enables fast approvals, flexible repayment terms, and educational content that helps borrowers understand home equity benefits and uses.

From Dirt to Data: How Precision Farming is Changing Agriculture Forever

Today’s agriculture has long evolved past manual labor and traditional farming. The journey to increased efficiency and productivity has led to exponential technological growth within the agricultural ecosystem. One of the most significant changes in recent years has been the rise of precision farming, also known as precision agriculture. This data-driven approach to crop management has revolutionized how we grow and produce food, making it more sustainable, precise, and profitable. The blog explores how precision farming is changing the face of agriculture and why it is here to stay. The Dawn of a New Era in Farming: Understanding Precision Agriculture: Imagine a world where farmers can monitor the health of their crops, detect nutrient deficiencies, and even predict weather patterns with precision. Precision agriculture breathes life into this very concept, turning it into reality. Technology transforms how we grow food, ushering us into the new farming era. Precision agriculture optimizes crop production by combining cutting-edge technologies like drones, sensors, and data analytics. These technologies allow farmers to collect real-time data on soil conditions, moisture levels, and pest infestations. This information enables them to make informed decisions, improve resource allocation, and minimize waste. But precision agriculture is not just about efficiency. It also has a significant environmental impact. By using precise amounts of fertilizers, water, and pesticides, farmers can reduce their carbon footprint and protect ecosystems. The dawn of precision agriculture marks a shift towards a more sustainable and profitable future for farming. It is an exciting time to be a farmer as technology revolutionizes how we feed the world. The Digitalization of Crop Management: How Data Plays Its Part The digital age has made data an invaluable resource in modern agriculture. Precision farming has paved the way for the digitalization of crop management, harnessing data’s power to revolutionize how farmers approach their work. Farmers can now utilize advanced technologies to gather real-time crop data, including soil conditions, moisture levels, and pest infestations. This vast amounts of information allow them to make data-driven decisions, optimizing resource allocation and minimizing waste. The digitalization of crop management is not just about collecting data; it’s about using that data to drive actionable insights and improve agricultural practices. By leveraging technology and data analytics, farmers can identify patterns and trends, allowing them to make informed choices about irrigation, fertilization, and pest control. This level of precision and accuracy enhances productivity and promotes sustainability by minimizing resource usage and reducing environmental impact. In short, the digitalization of crop management is transforming agriculture by giving farmers the power of data. This data enables them to make more informed decisions, increase efficiency, and ultimately contribute to a more sustainable and profitable future for farming. Real-Life Impacts of Precision Farming on Modern Agriculture Precision farming has profoundly impacted modern agriculture, bringing numerous real-life benefits, including increased crop yield and quality. One critical impact is that farmers can optimize irrigation, fertilization, and pest control with precise monitoring and data-driven decision-making, resulting in healthier and more abundant crops. It allows for increased food production with fewer resources, helping farmers address the global challenge of feeding a growing population. Precision farming has also made agriculture more sustainable. Using sensors and data analytics, farmers can identify areas of their fields requiring less water or fertilizer, thereby minimizing waste and reducing the environmental impact. Additionally, precision agriculture allows for targeted pest management and promotes biodiversity by reducing the need for harmful pesticides. Another significant impact of precision farming is improved farm management and financial stability. By having access to real-time data on crop conditions, farmers can proactively address issues and prevent losses, thereby saving money and assuring a stable income. Precision farming is revolutionizing modern agriculture by improving crop yield, sustainability, and farm profitability. It is a game-changer that will continue to shape the future of agriculture. Future Predictions: What’s Next for Data-driven Agriculture? The future of data-driven agriculture holds even more exciting possibilities for farmers and the industry. The continuous advancement of data analytics and technology ensures precision farming will become even more precise and efficient in the coming years. Here are a few predictions for what’s next: Artificial Intelligence Integration: As AI technology evolves, we can expect to see it integrated into precision farming systems. AI algorithms can analyze large datasets, identify patterns, and make autonomous decisions, further optimizing crop management. Internet of Things (IoT) Expansion: IoT devices, such as sensors and drones, will likely expand, allowing farmers to collect even more detailed and real-time data. IoT devices will provide a more comprehensive understanding of crop conditions and enable proactive decision-making. Predictive Analytics for Climate and Pest Control: Farmers can accurately predict climate patterns and pest outbreaks by leveraging historical and real-time data. Predictive analytics enables a more proactive approach, reducing the reliance on pesticides and mitigating potential crop losses. Integration with Robotics: The integration of robotics into precision farming will continue to increase. Robots can now handle tasks such as planting, harvesting, and weed control with precision and efficiency, reducing the need for manual labor. Blockchain Implementation: Blockchain technology has the potential to revolutionize the agricultural industry by optimizing the supply chain and ensuring transparency and traceability. Blockchain can enhance consumer trust and enable farmers to get fair product prices. The future of data-driven agriculture is exceedingly promising. As technology evolves, we can expect precision farming to become even more precise, sustainable, and profitable. Farmers will have access to more advanced tools and analytics, enabling informed decision-making that will further optimize crop management and contribute to a more sustainable future. It is an exciting time to be a part of the agriculture industry as we witness the continued transformation of farming through data-driven innovation.

Generative AI – Impact on Software Testing

What is Generative AI?  Generative AI uses deep learning algorithms, like those in machine translation, to analyze massive datasets. It utilizes the patterns and relationships it discovers in the data to generate entirely new outputs that resemble, but differ from, what it has previously seen. Relevance in Software Testing: Generative AI has significant implications for the software testing field. It can help with test data generation, code development, and repetitive activity automation, boosting productivity and efficiency. In software testing, it is acting as a notable change by automating and optimizing various aspects of the QA process. Trends and Opportunities for Generative AI in Testing:  Advancements In Test Case Generation: Not only can generative AI automatically generate a variety of test cases and scenarios, but it can also cover a wide range of scenarios that human testers could miss. It may also analyze current code and software features to generate thorough test cases independently. This guarantees that tests cover a more comprehensive range of scenarios and frees up testers’ time. It is a creative tool with fast input processing speed and nearly free per invocation. It must be utilized to help and encourage, bounce ideas off, and get ideas for new directions.  Intelligent Test Data Generation: Generating realistic test data is crucial for testing software systems’ robustness and scalability. Generative AI can generate diverse test data sets, improving the accuracy and effectiveness of software testing.  While generative AI has solved the challenge of test data production for relatively simple systems, there is still much to learn regarding complicated application test data generation. Indeed, generative AI can help with certain modest jobs in this problem field.  Enhanced Test Automation: Generative AI can automate writing test scripts, reducing manual effort. It is even capable of modifying these scripts to fit various programming languages. This can significantly reduce the manual effort required to create and maintain test suites, leading to increased productivity and faster release cycles. Generative AI can and should help with writing test automation. It excels as a code completion tool (Examples include CodeAI and GitHub’s CoPilot). In response to a prompt or remark, it can automatically develop methods or construct scaffolding. It can identify dubious code. It can translate an implementation between different frameworks or languages. It is an excellent teaching tool that demonstrates how to utilize a new library and can offer thorough examples when necessary. It can suggest code snippets for tests or code snippets given tests.  Predictive Analytics for Issues: Generative AI can assist in diagnosing the underlying causes of problems by analyzing patterns in code and previous bug reports, as well as historical data and finding trends. By utilizing AI and machine learning techniques, it can anticipate defects, identify patterns, and learn from past errors.  Improved Test Coverage: Traditional software testing methods have issues ensuring sufficient test coverage. Manually covering all possible circumstances is typically challenging. Nevertheless, generative AI can analyze user behavior patterns and application code to find edge cases and produce test cases with thorough coverage.  Continuous Integration and Delivery: Generative AI can automatically build and run tests as part of pipelines for continuous integration and delivery anytime changes are made to the codebase. This helps maintain lofty standards of quality throughout the development process and guarantees that any new features or bug fixes do not introduce novel issues.   Challenges and Limitations of Generative AI in Testing:  Data Quality: The quality of AI-generated tests heavily relies on the quality and quantity of data used to train the model. Insufficient data or data with errors can lead to nonsensical or ineffective test cases (e.g., focusing on a specific user demographic and missing functionality for others). AI-generated tests might not always be relevant or practical. The model’s dependence on training data can lead to nonsensical tests if the data is inadequate or lacks context.  Data Bias: Generative AI models can inadvertently learn and reproduce biases present in the training data. Biases in the training data can lead to biased tests, potentially overlooking critical functionality or security vulnerabilities. For example, a model trained on data from a specific region or demographic might miss crucial functionality relevant to other user groups. This can lead to software that caters to a particular subset of users and overlooks the needs of others.  Ethical Considerations: Using generative AI raises ethical concerns, such as potential misuse or malicious intent. Establishing ethical guidelines and safeguards is highly critical.  Computational Cost: Training and running generative AI models, especially complex ones, require a large amount of computer power. This can be a hurdle for smaller organizations with limited resources. Ongoing efforts are being made to create more effective models that need fewer processing resources.  Limited Creativity and Human Oversight: Although generative AI models might perform well on specific tasks they are trained for, they need help generalizing to unseen scenarios and lack human abilities like genuine creativity. They require ongoing training and adaptation to maintain effectiveness. For example, testers (human oversight) are essential in defining clear testing objectives, analyzing test findings, and guaranteeing overall software quality.    Summary:  Generative AI will only empower humans and not replace them. Overall, it has the potential to revolutionize the way software testing is conducted, leading to faster, more efficient, and more effective testing processes. The truth is, ensuring software quality is an intricate challenge that demands critical analysis and a profound grasp of various subjects. Companies prioritizing quality expertise and equipping their experts with suitable tools, including AI, will thrive. Conversely, those relying on simplistic solutions instead of critical thinking will falter. Human testers remain vital for defining testing goals, interpreting test results, and applying critical thinking skills to ensure software quality.   Generative AI should be seen to augment human testers, not eliminate them. 

Maximizing the Impact of Test Automation

As we are all aware, software permeates various aspects of our lives, from mobile apps to business-essential systems. As software becomes more complicated, reliability and quality become harder to assure. Test automation proves particularly valuable when this occurs. Time has witnessed the evolution of test automation into an integral aspect of software development, resulting in improved efficiency and cost-effectiveness. Enhancing effectiveness, precision, and feedback cycles through automation, we can achieve higher quality. Common Pitfalls in Test Automation By leveraging test automation, software quality and test execution speed can be significantly improved. Insufficient execution and management of test automation hinder many organizations, resulting in subpar results. ROI’s effectiveness is often threatened by difficulties in ensuring long-term success and precise ROI calculation. The article offers practical guidance on leveraging test automation to generate the greatest possible impact. Effective Test Automation Implementation and Management: To maximize the impact of test automation, a comprehensive approach that includes many areas of testing, development, and collaboration is required. The success of automation testing depends on implementing and managing test automation effectively. Here is a detailed way to achieve this goal: Define Clear Objectives: With the right strategies in place, test automation can yield substantial results. Starting with the goals, detail your test automation objectives. With a clear understanding of the desired outcomes, tailor your automation testing strategy to align with your goals. Choose the Right Tool/Framework: Selecting the appropriate tools and frameworks is necessary. A dependable, adaptable, and user-friendly tool should be chosen by considering tech stack, project requirements, and team proficiency. In the grand scheme of things, this will be a time and effort conserving solution. Solid testing approach: By concentrating on the most important tests, test automation can be accomplished efficiently. Automation’s applicability is limited to certain tests. By focusing on these tests, you can optimize the value and scope they offer. Group tests according to their significance, risk, and execution frequency. Testing should start with the most critical areas to achieve prompt results. Prioritization becomes more manageable when focusing on essential aspects. Maintainable Test Scripts: Create test scripts that are modular, efficient, and maintainable, ensuring scalability. Implement design patterns like Page Object Model (POM), use data-driven testing, and keyword-driven testing, maintain a clear structure, apply coding standards, ensure proper documentation, and leverage best practices for creating reliable automated tests. By combining these methods, one can create well-organized and well-documented automated tests, highlighting the advantages of industry standards. Test Data Management: Consistent test results are achieved by expertly managing test data, allowing for reliable conclusions. By incorporating automated data setup and cleanup, you can improve your testing process. Continuous Integration and Continuous Delivery (CI/CD): Implementing test automation in your CI/CD pipeline enables tests to be triggered by code commits, resulting in early issue detection, quick feedback on changes, and prevention of defects in production. Test Environment Management: Emulate the production environment in test environments for optimal results. resemble those in production. By doing this, automation results appropriately mirror real-world situations. Continuous Learning and Training: Offer testing team training and skill enhancement opportunities. Verify that they possess the necessary skills to construct, maintain, and execute automated tests. Stay current on the latest automation methods, instruments, and technology. Investing in training yields returns in the form of improved team skills and industry awareness. Reporting and Monitoring: Create thorough reports that detail test outcomes, coverage data, and defect patterns. Visualizing testing progress, dashboards play a crucial role. By leveraging detailed reporting and analytics, you can monitor the performance of automation and uncover patterns. Get buy-in from stakeholders and Feedback Loop: Early stakeholder involvement is crucial. By involving all individuals from the onset, a unified vision can be fostered. Ensure optimal impact, gather stakeholder feedback, monitor automation efficiency, and adjust iteratively. Conducting reviews and retrospectives at regular intervals helps determine the effectiveness of your test automation. Identifying areas for improvement is crucial to adjusting your strategy. Summary In conclusion, we discussed the pros and cons of test automation and how to overcome any difficulties. In addition, we provide guidance on improving test automation, including selecting the appropriate tools and frameworks, developing a thorough testing approach, and involving key stakeholders early in the process. Effective test automation management is essential for success. Setting clear goals, monitoring progress, and continuously improving the process will ensure that your organization capitalize on the full potential of test automation. By acting and implementing these best practices in your own organizations, your organization can experience enhanced efficiency, accuracy, and faster feedback loops.

Mastering Seamless Integration: A Deep Dive into Salesforce’s External Services

In today’s interconnected digital landscape, businesses depend on a diverse range of applications and systems to efficiently manage their operations, making seamless integration imperative. Salesforce, a pioneer in cloud-based CRM solutions, provides an impactful integration tool called External Services. Harnessing the OpenAPI 3.0 standard, External Services empowers organizations to integrate systems seamlessly with their Salesforce environment. What is External Services? External Services is a cutting-edge integration solution offered within Salesforce. It enables organizations to seamlessly incorporate external web services into their Salesforce environment by leveraging the OpenAPI (previously known as Swagger) specification, which precisely defines the web service. This integration empowers users to seamlessly bring the functionalities of their external web service into the robust Salesforce platform, utilizing intuitive point-and-click tools such as flow builder. As the picture depicts, the OpenAPI specification connects different types of APIs with Salesforce using External Services. Key features and benefits Standardized integration: With OpenAPI, External Services promotes standardization and interoperability, allowing seamless integration with a wide range of external systems and services. Simplified configuration: External Services offers an intuitive interface for configuring and consuming external APIs. Users can import OpenAPI specifications directly into Salesforce and map API resources to custom objects, making integration setup a breeze. Enhanced flexibility: By leveraging OpenAPI, External Services supports advanced features such as data validation, schema referencing, and parameterized requests, providing greater flexibility and control over integration workflows. Streamlined development: External Services accelerates integration development by generating Apex code stubs based on imported OpenAPI specifications called dynamic classes. This automates much of the coding process, reducing development time and effort. Before we discuss External Services in depth, let’s understand the OpenAPI specification and how it helps with the integration of external systems. Understanding OpenAPI OpenAPI, is a specification for building APIs. It provides a standardized way to describe RESTful APIs, which makes it easier for developers to understand and interact with APIs. Defines a standard, language-agnostic interface to HTTP APIs. Enables humans and computers both to discover and understand the service’s capabilities without access to source code, documentation, or through network traffic inspection. As seen in the above diagram, seven different components constitute OpenAPI specification (currently, OpenAPI 3.0 is taken as reference). The following components represent any REST-based API covering all web service aspects: endpoints, request/response formats, and security definitions. External Service Setup We need to configure the following three entities to consume any REST-based API to start making callouts. 1. Setup named credentials: For any REST API callouts, we need integration user details to connect with external systems, such as username/password as part of the basic authentication model, client ID/client secret as part of the OAuth authentication model, etc. All these details will be stored in the named credentials entity. Therefore, named credentials in Salesforce provide a secure and easy way to authenticate external services within your Salesforce org. They abstract the endpoint URL and authentication details, making integrations more secure and manageable. Named credentials define the URL of a callout endpoint as well as its required authentication parameters in a single definition. To put it simply, the setup of authenticated callouts defines a named credential as the callout endpoint. 2. Register External Service: In the first step, “Setting up named credentials,” the system allows us to manage the authentication details needed for making callouts. Now, we need to register the service in External Service. All it takes is selecting named credentials and importing the OpenAPI specification into the External Service configuration wizard, which will automatically generate the stub classes. All these auto-generated classes get stored under dynamic classes in the Apex Classes section. An OpenAPI spec can have a whole set of APIs as part of the module, and external service enables us to consume all the APIs in one go. While registering, the wizard takes us to a screen where we can only enable the required services. 3. Create a flow/apex component to make callouts With the named credentials and external service defined, we start making calls to web services using Flow or Apex. Flow:As soon as an external service is defined, the system creates an action for the flow under the External Service section, which can be used to select the API/Operation we want to do. Apex:After registering the external service, we can call it natively in your Apex code. Objects and operations defined in the external service’s registered API specification become Apex classes and methods in the ExternalService namespace.   Conclusion Powered by the OpenAPI 3.0 standard, Salesforce’s External Services revolutionizes how organizations integrate external systems with their CRM platform. External Services enables businesses to unlock new opportunities for innovation, efficiency, and growth by providing a standardized, flexible, and efficient approach to integration. Whether integrating payment gateways, marketing automation platforms, ERP systems, or any other external services, External Services with OpenAPI empowers organizations to streamline processes, enhance collaboration, and deliver exceptional customer experiences. With External Services, the possibilities for integration are endless, paving the way for a more connected and agile digital ecosystem.

Aftermarket Price Optimization and Increased Profitability with Price.AI

The aftermarket industry, which comprises components like spare parts, repair works, and maintenance services, represents a significant revenue management stream for manufacturers (OEMs) and dealers alike. In a market that is prone to constant fluctuations, pricing is a crucial aspect of attracting and retaining customers. However, relying on traditional pricing strategies often leads to untapped profits. This gap is precisely where AI for price optimization steps in, delivering dynamic, intelligent, and data-driven solutions. Decoding the Price: Challenges in Aftermarket Key challenges that have a significant impact on pricing: Reliance on traditional pricing methodologies: Many businesses still rely on old and traditional price lists or what they believe is the right value for the offering, failing to consider factors like market fluctuations, competitor actions, and demand variations. This approach can lead to missed profit opportunities. Lack of modern technology adoption : Several tools offer real-time insights into market trends and competitor pricing, but many such technology solutions remain underutilized. Without a holistic view, businesses cannot make informed pricing decisions, nor can they stay competitive in the industry. Margin-volume predicament: Striking the balance between profit margins and sales volume is a constant struggle. Lower prices might be attractive but will erode margins over time. Conversely, high prices may deter sales and lead to excess inventory. The inability to manage margin-volume trade-offs will increase costs over time. It thus becomes pertinent that the aftermarket industry shifts towards adopting AI-based solutions and leveraging analytics and real-time insights to automate pricing decisions. The AI Advantage: Focus On Data-Driven Practices and Real-Time Insights Implementing AI pricing strategies for success is necessary to deliver personalized and optimized pricing experiences. AI can revolutionize pricing by introducing a dynamic and analysis-driven approach some of which include: Precision pricing through machine learning: AI algorithms analyze vast datasets like historical sales data, competitor pricing, and market trends to create sophisticated pricing models for identifying optimal prices for spare parts while considering market conditions and budget. Demand forecasting with analytics: AI can predict future demand by factoring in seasonal demand, product lifecycles, and competitor analysis. Businesses can maintain optimal inventory levels, preventing lost sales and avoiding excess stocking. Real-time monitoring: AI can monitor competitor pricing strategies and help businesses adjust their prices, ensuring they remain competitive without affecting profits. Price analysis: AI helps analyze price elasticity enabling price adjustments without significantly impacting sales volume or profit margins. Further, one can adjust prices for dealer net, promo, and high-demand periods or meet specific promotional goals.   AI Pricing: A Plethora of Benefits The main advantage of using AI-powered price optimization models is that it goes beyond helping increase profits. It unlocks various strategic benefits that can be realized only when implemented. Enhanced decision-making: AI automates tedious tasks like data analysis and pricing. It provides up-to-date data and frees up valuable time to focus on other business priorities. Improved risk management: Data-driven pricing decisions based on AI insights minimize the risk of underpricing or overpricing. This leads to more stable profit margins and improves overall financial health. Streamlined operations: AI facilitates efficient inventory management by optimizing forecasting and preventing unnecessary stocking. This reduces costs associated with excess inventory or lost sales due to understocking.   The future of AI in aftermarket pricing is brimming with exciting possibilities. AI will evolve beyond recommending optimal prices. It will delve deeper, providing businesses with recommended actions based on different market scenarios, competitor strategies, and economic indicators. It can analyze various data points for personalized pricing strategies, especially ideal for incentivizing high-value deals while ensuring profitability. Seamless integration of AI-powered pricing tools will allow for more personalized customer experiences and dynamic pricing strategies. Price.AI: The Ultimate Solution to After-Sales Profit Optimization Tavant’s Price.AI stands as a gateway for dealers and OEMs seeking to unlock the full potential of AI-powered after-sales pricing strategies. Key characteristics of Price.AI include: Dynamic pricing insights: Offers in-depth insights and personalized recommendations by region, segments, product types, and timeframes that influence pricing. Optimize pricing strategies: Leverage AI-powered algorithms to set optimal prices for spare parts and services, ensuring maximum profitability while remaining competitive. Understand your competition: Gain a clear view of competitor list prices and discounted pricing tactics for better decision-making and profitability. Mitigate risks: Identifying risks and taking proactive steps through price simulation offers an effective way to plan smarter business strategies. With data pervading all aspects of business, AI is fast becoming an indispensable tool to drive growth and achieve consistent profitability. AI-based solutions powered by deep analytics and insights can drive significant business value by helping OEMs and dealers adapt to the evolving landscape. Among such innovations, Tavant’s Price.AI stands out by offering OEMs tailored, industry-specific insights that enhance part pricing strategies through comprehensive analysis and real-time monitoring ensuring OEMs and dealers stay ahead of the curve, making informed decisions that drive significant business value.

Sustainable Housing, Inclusive Lending–Toward a Unified Vision for Mortgage Industry Transformation

In today’s rapidly evolving mortgage industry, two key pillars stand tall: sustainability and diversity, equity and inclusion. As we navigate through shifting paradigms and societal expectations, it’s imperative for industry leaders to not only embrace these principles but also intertwine them to create a more resilient and equitable housing ecosystem. Green Mortgages: A Beacon of Hope An Energy Efficient Mortgage, also known as a green mortgage, allows lenders to offer borrowers a way to finance cost-effective, energy-efficient improvements to an existing property at the time of purchase or refinancing or for upgrades above the established residential building code for new construction homes. Green loans contribute to aligning lending and environmental objectives. The surge of environmentally friendly mortgage products heralds a new era of conscientious lending. With a growing emphasis on sustainability, financial institutions are integrating green criteria into their underwriting processes, incentivizing eco-conscious homeownership practices. From renovating existing properties to constructing energy-efficient homes, the allure of green mortgages extends far beyond financial benefits. Consider the case of a neighbor of mine in the San Francisco Bay Area who embarked on a mission to transform their condo into a model of sustainability. Their initiative not only inspired the local community to be greener themselves, but it also underscored the potential for collective action in fostering greener living environments. Indeed, the adoption of sustainable mortgage programs isn’t just a financial decision, it’s a commitment to a brighter, more sustainable future for generations to come. Diversity, Equity and Inclusion: The Cornerstones of Fair Lending In parallel, the mortgage industry is witnessing a concerted effort to promote diversity, equity, and inclusion. Organizations like the American Mortgage Diversity Council (AMDC) are championing initiatives to address disparities in homeownership rates among different demographic groups. By fostering a culture of inclusivity, lending professionals can tap into a diverse array of perspectives, driving innovation and better serving the needs of a multicultural clientele. But why is DEI so crucial in the mortgage industry? Simply put, it’s a gateway to deeper connections with diverse communities. Lenders can navigate cultural nuances with finesse, building trust and rapport with customers from all walks of life, by embracing inclusivity. Moreover, the integration of AI-driven decisioning algorithms offers a powerful tool in combating bias and promoting fair lending practices. Leveraging machine learning enables lenders to detect and mitigate potential sources of discrimination, ensuring that mortgage decisions are made on merit rather than preconceptions. Looking Ahead: A Unified Vision for Mortgage Lending As we chart the course ahead, the convergence of sustainable and inclusive lending practices emerges as a beacon of hope. By intertwining the principles of sustainability and DEI, we can forge a path toward a greener, fairer future for all. From promoting energy-efficient homes to fostering cultural inclusivity, the mortgage industry has a unique opportunity to drive positive change on a global scale. We should all aspire to be like my neighbor and set the right example for the community. Let us seize this moment to reimagine mortgage lending as a force for good—one that not only sustains our planet but also uplifts communities, one loan at a time. FAQs – Tavant Solutions How does Tavant support inclusive lending practices for sustainable housing?They enable alternative credit scoring, automated bias detection, and expanded data sources to identify qualified underserved borrowers, supporting green mortgage programs and energy-efficient property financing. What role does Tavant play in mortgage industry transformation for inclusive lending?Tavant removes barriers to homeownership, streamlines lending for diverse borrowers, implements fair lending algorithms, and supports CDFIs and CRA compliance while expanding sustainable housing access. What is inclusive lending in mortgage industry?Practices expanding access to homeownership for underserved communities using alternative data, flexible underwriting, and removing systemic credit barriers. How does sustainable housing relate to mortgage lending?Financing energy-efficient homes and environmentally responsible construction, often with favorable green mortgage terms. What are the benefits of inclusive mortgage lending?Expanded homeownership, stronger communities, better compliance, reduced defaults, and positive social impact through accessible housing.

Transforming Aftermarket Experiences: The Power of Service Lifecycle Management

The significance of providing exceptional aftermarket services cannot be overstated in today’s times as organizations strive to meet the dynamic expectations of their customers and stay competitive. Service Lifecycle Management (SLM) emerges as a powerful solution, seamlessly integrating various aspects of post-sales support to create a connected and customer-centric experience. In this blog post, we’ll delve into the multifaceted features of SLM, exploring how it revolutionizes field service, warranty management, service contracts, service parts management, customer service, supplier recovery, service intelligence, recalls, auditing, and service quality. Additionally, we’ll shed light on how Artificial Intelligence (AI) and Advanced Analytics are playing a pivotal role in powering SLM. Customer Service: SLM enhances customer service by providing a 360-degree view of customer interactions and service history. AI-driven chatbots and virtual assistants enable quick issue resolution, while predictive analytics anticipates customer needs, ensuring a proactive approach to service delivery. Warranty Management: SLM enables efficient warranty management by automating claims processing, tracking warranty periods, and ensuring compliance. AI algorithms can predict potential warranty issues, allowing organizations to take preventive actions before problems escalate, ultimately saving costs and improving customer trust. Service Intelligence: Harnessing the power of AI and Advanced Analytics, SLM provides actionable insights into service performance. Predictive analytics identifies trends and areas for improvement, empowering organizations to make data-driven decisions and continuously enhance service quality. Field Service: SLM streamlines field service operations by optimizing technician scheduling, route planning, and real-time communication. AI-driven predictive maintenance ensures proactive service, reducing downtime and enhancing overall customer satisfaction. This feature is particularly beneficial for industries relying heavily on equipment maintenance, such as manufacturing and healthcare. Service Parts Management: Effective inventory management is crucial in providing timely service. SLM optimizes service parts logistics, minimizing stockouts and excess inventory. AI algorithms predict demand patterns, ensuring that the right parts are available when needed, reducing lead times and costs. Service Contracts: The management of service contracts becomes seamless with SLM, providing a unified platform to create, manage, and renew service agreements. AI-powered analytics can identify upsell opportunities and recommend personalized contract options based on historical data and usage patterns. Recalls and Auditing: SLM ensures a rapid response to product recalls by efficiently tracking affected units and managing the entire recall process. Advanced analytics aids in auditing, ensuring compliance with industry regulations and providing a comprehensive overview of service processes. Supplier Recovery: SLM facilitates collaboration with suppliers by streamlining communication, order processing, and performance tracking. AI analyzes supplier data to identify potential risks, enabling organizations to proactively address issues and maintain a reliable supply chain. Service Quality: Continuous improvement is at the core of SLM, as it enables organizations to monitor and enhance service quality. AI-driven analytics identify patterns in customer feedback, allowing companies to address issues promptly and refine their service offerings. Final Thoughts Service Lifecycle Management is a game-changer in the aftermarket services landscape, fostering seamless and connected experiences for both businesses and customers. The integration of AI and Advanced Analytics adds an extra layer of intelligence, enabling organizations to not only meet but exceed customer expectations. As industries evolve, embracing SLM becomes imperative for those aiming to stay ahead in the competitive market, delivering unparalleled post-sales support and solidifying customer loyalty. Tavant SLM solution is a comprehensive solution suite comprising of products and services designed to empower manufacturing ecosystem by simplifying and streamlining service lifecycle management processes.

Service Contracts in Manufacturing: A Blueprint for Revenue Growth and Customer Loyalty

In today’s competitive manufacturing landscape, the imperative to stay ahead transcends the realm of producing high-quality products. Service contracts have evolved into a strategic cornerstone for manufacturers, providing an additional revenue stream, fostering customer loyalty, and delivering crucial insights into customer expectations. The symbiotic relationship between service contracts and manufacturer success hinges on the ability to consistently exceed customer expectations while capitalizing on the wealth of data generated through service interactions. Let’s explore the various advantages of Service Contracts in Manufacturing below: Diversifying Revenue Streams Service contracts offer manufacturers a dependable additional revenue source, extending far beyond the initial product sale. Ongoing services such as maintenance, repairs, and upgrades create a steady income throughout the product’s lifecycle. This predictable revenue ensures financial stability and facilitates better planning and investments in research and development. As manufacturers bolster their ability to innovate, they gain a competitive edge, positioning themselves as dynamic entities capable of adapting to the market’s ever-changing demands. Building Long-Term Customer Loyalty The significance of service contracts goes beyond monetary gains; they play a pivotal role in nurturing enduring customer relationships. Offering comprehensive service packages leads to increased customer loyalty. Timely resolution of issues, proactive preventive maintenance, and efficient support contribute to positive customer experiences. These positive experiences foster loyalty and potentially translate into repeat business and positive word-of-mouth referrals, further solidifying a manufacturer’s market position. Insights from Service Interactions Every service interaction allows manufacturers to gather valuable data about their products and customer needs. The nuanced analysis of service contract data yields insights into common issues, usage patterns, and emerging trends. This treasure trove of information becomes a potent tool for continuous improvement. Manufacturers can enhance product design, identify areas for innovation, and proactively address customer concerns, ultimately ensuring their offerings remain in sync with evolving market dynamics. Tailoring Products to Customer Needs Armed with a profound understanding of customer expectations, manufacturers can tailor products and services to better align with those needs. Whether introducing new features, optimizing existing functionalities, or addressing pain points highlighted by service interactions, manufacturers can continually refine their offerings to resonate with customer preferences. This not only boosts customer satisfaction but also positions the manufacturer as a customer-centric entity capable of adapting swiftly to evolving market demands. Proactive Maintenance and Risk Mitigation Service contracts empower manufacturers to adopt a proactive approach to maintenance, substantially reducing the likelihood of product failures and downtime. Predictive analytics derived from service data allow manufacturers to identify potential issues before they escalate. This proactive stance facilitates timely interventions, minimizing disruptions for customers and enhancing the overall product experience. Furthermore, it instills confidence in customers regarding the manufacturer’s commitment to delivering reliable products. Strategic Expansion Opportunities Beyond the immediate benefits, service contracts open avenues for strategic expansion. Manufacturers can explore additional service offerings, creating new revenue streams and diversifying their portfolio. This strategic expansion reinforces financial stability and positions manufacturers as comprehensive solution providers capable of addressing a spectrum of customer needs. Final Thoughts In conclusion, service contracts represent a multifaceted strategy for manufacturers to secure additional revenue, build customer loyalty, and gain invaluable insights into customer expectations. To unlock these benefits, manufacturers must prioritize meeting and exceeding customer expectations in their service offerings. By leveraging the data generated through service interactions, manufacturers can address immediate concerns and position themselves as dynamic entities capable of adapting to the ever-changing landscape of customer needs and preferences. As the manufacturing industry evolves, service contracts emerge as a vital tool for those seeking to survive and thrive in a customer-driven marketplace.

AI in Agriculture: Key Trends

In the vast expanse of agriculture, where every seed planted carries the weight of feeding a growing global population, the infusion of Artificial Intelligence (AI) has sparked a revolution. As we stand at the cusp of a new era, the future of AI in agriculture technology promises to redefine how we cultivate, monitor, and sustain our crops. This blog delves into the exciting prospects that lie ahead as AI takes center stage in agriculture. Precision Farming 2.0 AI is poised to take precision farming to higher levels as technology evolves. Advanced sensors, drones, and satellite imaging fueled by machine learning algorithms will provide farmers with unparalleled insights into their fields. These technologies will assess soil health and crop conditions and offer predictive analytics for more efficient resource management. Autonomous Farming Systems Picture a farm where tractors navigate the fields autonomously, sowing seeds with precision, and harvesters discerning the perfect moment to reap the rewards. AI-driven autonomous farming systems are on the horizon, minimizing labor costs, optimizing workflows, and increasing efficiency. The result? Increased productivity and reduced environmental impact. AI in Crop Breeding and Genetic Enhancement The marriage of AI and genetic science holds immense promise for crop improvement. Analyze vast genomic datasets, accelerating the identification of desirable crop traits through machine learning algorithms. Genetic enhancement expedites the development of hardier, more resilient varieties and facilitates the creation of crops tailored to specific environmental conditions. Climate-Smart Agriculture AI is becoming a significant tool in adjusting to the climate changes impacting agriculture practices. Smart irrigation systems, informed by real-time weather data and soil moisture sensors, will optimize water usage. AI algorithms will help farmers anticipate and mitigate the impacts of climate-related challenges, ensuring sustainable and resilient farming practices. Computer Vision Computer vision is redefining agricultural practices by enabling detailed monitoring of crop health, precise weed detection, and automated fruit picking through high-resolution imaging and AI analytics. This technology facilitates early pest detection and disease diagnosis, ensuring timely intervention. By analyzing plant growth patterns and detecting anomalies, computer vision systems optimize irrigation and fertilization, significantly increasing efficiency and yield while reducing resource waste. Generative AI Generative AI is revolutionizing agriculture by simulating environmental impacts on crop yields, creating virtual models for optimal farm designs, and accelerating crop breeding processes. It assists in developing climate-resilient crop varieties by predicting the outcomes of genetic modifications, thereby reducing trial and error. Additionally, Generative AI can optimize planting strategies and predict future food demands, ensuring food security and sustainability in agricultural practices. The future of AI in agriculture is not just a vision; it is a roadmap to a more sustainable, efficient, and resilient global food system. As we embrace the potential of AI in agriculture, it is imperative to navigate the ethical landscape carefully. Responsible AI deployment involves addressing algorithmic bias, data privacy, and the impact on rural communities. Finding the right balance between ethical consideration and technological advancement is crucial for a sustainable and inclusive agricultural future. As we plant the seeds of change, we’re poised to reap a harvest of unprecedented productivity, sustainability, and abundance. While we cultivate tomorrow’s fields, the symphony of artificial intelligence orchestrates them.

From Warehouse to Customer: The Strategic Journey of Service Parts in Service Lifecycle Management

Within the aftermarket world, service parts management assumes a pivotal role in upholding customer satisfaction and operational excellence. This blog explores the role of service parts management, its far-reaching influence on various stakeholders, and the nuanced challenges it presents alongside strategic solutions. The Significance of Service Parts in Service Lifecycle Management (SLM) Aftersales service transcends mere technical support; it is a commitment to upholding customer satisfaction and brand integrity. Service parts help in product longevity and performance, facilitating timely repairs, maintenance, and upgrades. Service Parts Management (SPM) is not just a logistical function; it is the backbone that reinforces trust, loyalty, and an enriched customer experience, solidifying a brand’s reputation for reliability and support. By harnessing the synergies across interconnected SLM modules, organizations can attain greater agility, visibility, and control over their spare parts operations. This, in turn, leads to the maximization of service parts availability, minimization of costs, and the facilitation of sustainable growth. Customer & Field Service – The seamless orchestration of service parts ensures that orders are initiated promptly when service requests or work orders are raised. Real-time visibility into service activities enables proactive planning and inventory management to meet the dynamic demands of the service domain. SPM acts as the linchpin, aligning service parts orders with contractual obligations and minimizing errors and disputes. This not only improves customer satisfaction but also maintains compliance with contractual commitments. Warranty Management – An often overlooked facet of SPM is its role in warranty management. It allows for the automatic identification of warranty-eligible parts, streamlining the process of identifying, ordering, and replacing parts covered under warranty. Enhanced visibility into warranty claims and coverage aids in optimizing service parts inventory, ensuring that organizations are well-equipped to fulfill their warranty commitments. Service Campaign Management – SPM facilitates proactive identification of parts subject to recalls or service campaigns. This proactive stance ensures the timely fulfillment of replacement parts, mitigating risks associated with non-compliance or safety issues. The interconnected nature of SPM within the broader SLM framework ensures that organizations are not only responsive but also preventative in their approach to potential issues. Supplier Recovery – A crucial aspect of SPM is the improved visibility into supplier recovery processes. This transparency helps in tracking returns, processing refunds or replacements, and optimizing inventory levels to minimize financial losses. Synchronized efforts between organizations and suppliers foster a mutually beneficial relationship, contributing to streamlined supply chains and shared growth. Service Quality Management – SPM goes beyond logistics; it enables organizations to monitor and analyze parts performance metrics and quality. Key indicators such as fill rates, lead times, and order accuracy are closely tracked, providing insights into the effectiveness of service operations. This data-driven approach empowers organizations to continuously enhance service quality. Service Contracts For organizations operating within contractual frameworks, SPM ensures that service parts orders align with contractual obligations and service level agreements (SLAs). This meticulous alignment minimizes errors and disputes, thereby improving customer satisfaction and maintaining compliance with contractual commitments. Service Parts Management At the heart of it all lies the centralization of service parts management within an integrated SLM solution. This not only streamlines end-to-end service parts lifecycle processes but also provides data-driven insights. These insights, derived from integrated modules, enable predictive analytics and optimization algorithms to anticipate service parts demand. This, in turn, optimizes stocking strategies and ensures the timely availability of critical parts.   Connecting Stakeholders: OEMs, Suppliers, Dealers, and Customers Service parts management serves as the nexus connecting a myriad of stakeholders within the aftersales ecosystem. This interconnected network collaborates harmoniously to ensure that the right part is at the right place at the right time, delivering superior service experiences and driving operational excellence. Suppliers – Effective communication, shared data, and synchronized efforts between suppliers and organizations contribute to streamlined supply chains and mutual growth. SPM acts as a bridge, facilitating this collaboration and ensuring that suppliers play a pivotal role in supplying high-quality components on time. OEMs – For Original Equipment Manufacturers, the efficient supply and management of service parts are not merely logistical puzzles but strategic imperatives. It contributes to brand integrity, customer satisfaction, revenue growth, and the ability to uphold warranty commitments. Additionally, it plays a pivotal role in fostering customer loyalty and repeat business. Dealerships – Dealerships serve as frontline ambassadors, providing expert guidance and support to customers seeking service parts and aftersales services. Their role in the aftersales ecosystem is critical, and SPM ensures that they have the necessary tools and information to serve as trusted service partners. Customers – For customers, service parts become the lifeline for maintaining and repairing their cherished products. The availability of the right service parts at the right time directly influences the customer experience, shaping perceptions of brand reliability and customer care.   Challenges in Service Parts Management: Solutions for Success Understanding challenges in service parts management and implementing strategic solutions is crucial for unlocking untapped potential and ensuring operational excellence. Demand Forecasting and Inventory Optimization Inaccurate demand forecasting and suboptimal inventory levels can lead to stockouts or excess inventory, impacting customer satisfaction and operational costs. The solution lies in implementing advanced analytics and forecasting models that leverage historical data, customer trends, and market insights to predict demand accurately. Additionally, employing inventory optimization techniques such as ABC analysis and just-in-time inventory helps optimize stocking levels and minimize carrying costs. Parts Obsolescence and Shelf-Life Management Managing parts obsolescence and shelf-life expiration poses a significant challenge, particularly for components with limited usage or those susceptible to degradation over time. Excess and obsolete inventory tie up valuable resources and can result in significant financial losses. The solution involves regularly reviewing service parts inventory and implementing proactive strategies such as phase-out plans and shelf-life management protocols. Prioritizing the use of First-In-First-Out (FIFO) or First-Expired-First-Out (FEFO) methods helps mitigate the risk of expired inventory. Supply Chain Disruptions and Lead Time Variability Supply chain disruptions and lead time variability can result in delayed service parts delivery and customer dissatisfaction. The solution lies in diversifying the supplier

A Comprehensive Guide to Mastering Salesforce Flow Orchestrator

In the ever-evolving landscape of Salesforce, where automation is the key to operational excellence, Salesforce Flow Orchestrator emerges as a powerful tool for advanced orchestration capabilities. In this comprehensive guide, we will understand the layers of Salesforce Flow Orchestrator, dissect its key components, and navigate through a real-world use case to understand its practical applications. Salesforce Flow vs. Flow Orchestrator Salesforce Flow is adept at automating straightforward processes within the Salesforce ecosystem, such as updating records, sending mail, and creating tasks. Salesforce Flow Orchestrator takes center stage when the need for advanced workflow coordination arises. This includes managing intricate approval processes, integrating with external systems, and navigating complex business processes with multiple steps and decision points. Types of Salesforce Flow Orchestration Autolaunched Autolaunched orchestrations launch in response to triggers like Apex, Rest API, and more. They bring forth automation seamlessly, responding to external stimuli with quiet efficiency. These orchestrations make automation feel natural and intuitive, initiated not by manual efforts but by the touch of code or external signals. Record-Triggered Orchestration Record-triggered orchestrations take center stage when a record is created or updated. They dynamically respond to changes in the Salesforce landscape, ensuring the rhythm of your processes aligns with record activities. These orchestrations are vital to responsive automation, crafting a harmonized narrative that works seamlessly alongside your business activities.   The Building Blocks of Flow Orchestrator Stages These are logical phases that group related steps together, are executed sequentially, and are bound by specified conditions for completion. Steps A step is where we can define which flow or flows can run sequentially or parallel. The system can complete the background steps, whereas interactive steps can be assigned to the user to complete and send a notification. Flows: Every step runs a flow. Flows can be autolaunched or screen flows, determining the actions and interactions within the orchestration. Orchestrator Work Guide: Guiding User Input Imagine a visual guide leading users through the input process—this is the Orchestrator Work Guide. A component embedded in record pages via App Builder ensures a seamless experience for users providing input and completing tasks. Under this Work Guide section, users can access the assigned screen and provide inputs. Find the image below with the Work Guide input screen. Real-World Use Case: Contract Approval & Order Creation Flow Orchestra is an invaluable asset for businesses seeking to automate intricate processes characterized by interrelated steps and the necessity for approvals spanning multiple organizational levels. A noteworthy example will be an IT company’s Contract Approval and Order Creation procedures. The platform seamlessly facilitates interaction with diverse teams and external vendors in this complex workflow, ensuring seamless collaboration. The requirement for approvals from various managerial positions is met with precision as flow orchestration creates a systematic and efficient approval workflow. Within this orchestrated process, managers at different levels are seamlessly integrated into the approval chain, ensuring a robust and compliant procedure. Moreover, the platform excels in task delegation, systematically assigning responsibilities to the organization’s most relevant individuals or teams. This meticulous assignment of tasks ensures that each step in the process is executed by the most qualified personnel, optimizing the overall efficiency of the operation. Flow orchestration becomes the linchpin in the orchestration of Contract Approval and Order Creation, navigating the complexities with finesse. Its ability to streamline interactions, obtain requisite approvals, and allocate tasks judiciously contributes to the timely signing of contracts and the expeditious creation of new orders. By seamlessly integrating into the organizational framework, flow orchestration elevates operational efficiency and empowers businesses to navigate intricate workflows precisely. Evaluation Flow: Precision in Orchestration Criteria In the toolkit of orchestration, the Evaluation Flow stands out. It’s an autolaunched flow that evaluates custom criteria for stages or steps within an orchestration, providing precise control over the process. Debugging an Orchestrator: Peering into Execution Details When the orchestrator takes center stage, administrators can access the Orchestrator Runs tab via the App Launcher for an in-depth look into its status, variable values, and overall execution. Debugging a Failed Flow Orchestrator Administrators can navigate to the Paused and Failed Flow Interviews section in the setup menu in troubleshooting mode. They can dissect failed orchestrations here, revealing crucial details to identify and address the root cause. Empowering Business Processes with Flow Orchestrator In conclusion, Salesforce flow orchestrator emerges as a pivotal tool in the realm of Salesforce automation. From its foundational blocks to real-world applications, flow orchestrator can revolutionize your Salesforce experience. Embrace the power of orchestration and witness streamlined business processes reaching new heights of efficiency!

Redefining Manufacturing Efficiency with Warranty Management Solution

In the manufacturing world, precision and efficiency are paramount, and staying ahead of the competition requires innovative solutions. One such game-changer is warranty management solution, which not only ensures but also product quality boosts overall manufacturing efficiency. In this blog, we will delve into the transformative impact of warranty management software on the manufacturing industry, exploring its integration with manufacturing operations, quality control, and supply chain management. The Foundation: Warranty Management Solution At its core, warranty management solution is designed to streamline the entire warranty process, from registration to claims processing. However, its benefits extend far beyond the realm of customer satisfaction and after-sales service. The integration of this solution with manufacturing operations is a strategic move that propels efficiency to new heights. Seamless Integration with Manufacturing Operations Manufacturing efficiency is a delicate balance of precision and speed. Warranty management solution ensures that this balance is maintained by seamlessly integrating with manufacturing operations. Real-time data exchange between the manufacturing floor and the warranty management system allows for immediate identification of potential issues during production. For instance, if a certain component consistently triggers warranty claims, the software can alert manufacturing teams to conduct a thorough quality analysis. This proactive approach not only prevents defective products from reaching the market but also enhances the overall quality control process. Quality Control Reinvented Quality control is the backbone of any manufacturing process, and warranty management software acts as a catalyst in its continuous improvement. By analyzing warranty data, manufacturers gain valuable insights into product performance, enabling them to identify weak points and enhance design or manufacturing processes. Moreover, the solution facilitates a closed-loop feedback system. As warranty claims are processed and resolved, the feedback loops back into the manufacturing process, guiding necessary adjustments. This iterative improvement cycle leads to the production of higher-quality goods, reducing warranty claims and associated costs in the long run. Streamlined Workflows for Operational Excellence Efficiency thrives on streamlined workflows, and warranty management software acts as a conductor orchestrating harmony across various manufacturing functions. From order processing to inventory management, the solution ensures that every stage of the manufacturing lifecycle is optimized. Automated workflows reduce manual intervention, minimizing the likelihood of errors and delays. For instance, warranty information can be seamlessly linked with inventory systems, enabling automatic updates on the availability of spare parts. This not only expedites the resolution of warranty claims but also optimizes inventory levels, preventing overstock or shortages. Supply Chain Management: A Well-Oiled Machine The integration of warranty management software extends its influence to the intricate web of supply chain management. Timely and accurate information about warranty claims aids in forecasting demand for replacement parts, allowing manufacturers to maintain optimal stock levels. Additionally, suppliers can benefit from this integration by gaining insights into the performance of supplied components. This transparency fosters collaborative relationships, with manufacturers and suppliers working together to improve the quality of raw materials and reduce the likelihood of warranty claims. The Bottom Line: Reduced Costs Efficiency in manufacturing is synonymous with cost-effectiveness. Warranty management software, by addressing issues at their root and optimizing processes, significantly reduces costs associated with warranty claims and post-sales support. The proactive approach to quality control prevents the production of defective goods, eliminating the need for extensive warranty-related expenses. Furthermore, streamlined workflows and optimized supply chain management contribute to overall cost reduction. With automated processes and real-time data, manufacturers can allocate resources more efficiently, focusing on innovation and strategic growth initiatives rather than firefighting warranty-related crises. Final Thoughts: A New Era of Manufacturing Efficiency In conclusion, the integration of warranty management solution into manufacturing operations marks a paradigm shift in the industry. The seamless collaboration between warranty processes, quality control, and supply chain management fosters a culture of continuous improvement and operational excellence. Manufacturers embracing this technological advancement not only ensure customer satisfaction through reliable products but also position themselves as industry leaders in terms of efficiency and innovation. In the ever-evolving landscape of manufacturing, those who harness the power of warranty management solution are not just building products; they are forging a path towards a new era of manufacturing efficiency.

REVOLUTIONIZING BUSINESS INTELLIGENCE: UNVEILING THE POWER OF SALESFORCE DATA CLOUD

In today’s business landscape, data plays a pivotal role in the success of enterprises, serving as a crucial foundation for informed decision-making. Salesforce Data Cloud is a comprehensive solution that addresses critical industry challenges related to data integration and consolidation, data quality and accuracy, customer personalization, and competitive agility. Let us explore why Salesforce Data Cloud is the cornerstone of modern business intelligence and the features that set it apart. What are the challenges? 1. Fragmented Data Landscape Modern businesses grapple with the challenge of data scattered across diverse systems, impeding a unified view. The absence of cohesion hinders seamless data integration and consolidation, making it difficult for organizations to obtain a holistic perspective for informed decision-making. 2. Data Inconsistency and Inaccuracy Ensuring the integrity of data remains a significant hurdle. The prevalence of inconsistent and inaccurate data threatens the reliability of information that drives operational processes. Organizations need help maintaining data quality to avoid potential errors and misinformation. 3. Limited Analytical Capabilities Deriving meaningful insights from data is a constant challenge in the industry. The ability to extract valuable information, identify trends, and make strategic decisions based on data-driven analysis is necessary. 4. Rigidity and Slow Adaptation The fast-paced nature of the business environment demands agility, but industry players face challenges in adapting swiftly to change. Organizations need a scalable and infrastructure-ready solution to connect disparate data sources efficiently and access real-time information. Data Cloud Overview: Salesforce Data Cloud is a dynamic and comprehensive real-time data platform. It enables businesses to integrate and unify external and internal data sources to create a single source of truth and make a golden customer data record. Data Cloud integrates AI and automation to enable data-driven decision-making and personalized experiences across multiple business functions; most importantly, it has a layer of data policy management to help customers keep their data safe and meet regulatory compliance requirements globally. Why Data Cloud? 1. Handle tremendous scale Salesforce Data Cloud is designed to handle vast amounts of data, making it suitable for businesses of all sizes. Its scalability ensures that organizations can grow and evolve without worrying about data limitations. 2. Improve data accuracy It streamlines the data accuracy process through robust data management capabilities such as data integration, harmonization, unification, cleansing, and deduplication. This improves data quality and accuracy while reducing manual efforts and chances of error and ensuring that organizations can rely on their data for critical decision-making processes. 3. Data analytics & enrichment capabilities The platform’s robust analytics capabilities empower organizations to derive valuable insights from their data, driving innovation and competitive advantage; it integrates AI and automation to enable data-driven decision-making across multiple business functions. 4. Personalized experiences Capture real-time data and leverage it for personalized customer experiences, predictive insights, and proactive services to build strong customer relationships. Integration with all the disparate systems enables your sales and marketing teams to comprehensively tailor their approach, anticipate needs, and provide a personalized buying experience for customers. 5. Customer 360 view Salesforce Data Cloud’s ability to create a comprehensive 360-degree view of customers’ data from all sources and easy accessibility and centralization of their information ensures that organizations can understand and cater to individual preferences, fostering robust and meaningful customer relationships and improving sales and marketing efforts to boost ROI 6. Trusted Infrastructure The platform’s infrastructure readiness makes it a future-proof solution, allowing organizations to grow and adapt without limitations. It also scales with the required privacy, security, and compliance with Hyperforce. How Does Salesforce Data Cloud Work: 1. Connect: Salesforce Data Cloud seamlessly connects various data sources by offering pre-built connectors for external and Salesforce platforms to bring data into the data cloud in real-time/batch mode to build the data lakehouse for your customer. 2. Harmonize: Data Cloud harmonizes and stores your customer data at a massive scale, transforming it into a single and dynamic customer profile to provide a golden record. Regardless of the data source and how it’s labeled, you will see all the data of individual customers in their specific profiles separately. 3. Engage: Salesforce Data Cloud uses a Lakehouse architecture that simplifies the categorization and classification of unstructured data into a structured form for known and anonymous users. As a result, the historical data can be accessed more quickly and efficiently. 4. Experience: Data Cloud unifies all the data in one spot so that you don’t need to narrow your search for each customer. Due to the ability to capture real-time data, Data Cloud can leverage it for personalized customer experiences, predictive insights, and proactive services. This way, sales/support staff can proactively respond to customers without asking repeated queries when receiving a call from a customer. Industry Use Cases 1. Failure Prediction (Manufacturing Cloud) – Leveraging machine data and service information, we predict component failures or replacements, delivering preemptive information to customers or dealers for potential servicing or replacement options. Implementing Data Cloud features has been instrumental in realizing this use case. Data Ingestion: ingests 3rd party data (service data and IOT data) through ingestion API. BYOM: brings the prediction model and runs that model on CDP data Calculated Insights: builds the aggregated view of each customer’s vehicle’s reading and failure points against it Data Action: sends alerts to dealers and customers when they approach the failure production limit Analytics:  builds dynamic dashboards   2. Dealer Engagement (Commerce/Experience Cloud) – Capturing dealers’ engagement through the Warranty Catalogue Portal, we extract clickstream data detailing user journeys with products. This information is then transmitted to the marketing and sales teams to facilitate the rollout of targeted product offers and develop effective product selling strategies. The integration of the following Data Cloud features has played a vital role here: Data Ingestion: ingests data through Web and mobile Connector Calculated Insights: builds the aggregated product view to capture clicks per dealer and consolidates it by dealer, region, and product Data Action: sends an alert to the marketing and sales team Analytics: builds the dynamic dashboards   3. Financial Services – Banks can unify data from core banking systems, credit card programs, insurance

Revolutionizing Manufacturing with TMAP’s Rule Engine: A Deep Dive into AI Alerts

Globally, manufacturers are increasingly turning to data and advanced analytics to enhance efficiency, drive innovation, and optimize overall performance in their aftermarket service processes. TMAP, powered by Advanced Analytics and AI, offers actionable insights to streamline and improve these processes. In the dynamic landscape of manufacturing, proactive management of potential challenges is essential for seamless operations. In response to this need, Tavant introduces the Rule Engine, a robust tool integrated within the TMAP framework, to address and anticipate challenges effectively. Unveiling the Power of Rule Engine The Rule Engine module within the TMAP platform empowers users to craft rules tailored to their unique requirements. These rules are applicable across various scopes, including IoT (telematics), service maintenance, claims, campaigns, and warranty. Each rule is defined by specific criteria, and upon meeting these criteria, designated actions are triggered.  For instance, a rule may specify that an alert should be triggered if the vehicle model matches, and the speed exceeds 140. The action associated with this rule could be to ‘Notify Dealer’ or ‘Notify Customer’, ensuring that relevant stakeholders are promptly informed. The Role of AI Alerts in Manufacturing The AI Alerts feature, woven into the Rule Engine module, stands as a true game-changer. This feature introduces three fundamental functionalities, reshaping communication, and issue resolution in manufacturing: 1. Notify: Proactive Communication The ‘Notify’ function allows the system to dispatch email alerts to relevant stakeholders, ensuring timely communication when a predefined rule is triggered. This capability is particularly valuable for matters that demand immediate attention. 2. Mute: User-Controlled Alerts Users have the power to mute or unmute AI Alerts based on their preferences. This level of customization empowers users to manage the flow of information and prioritize their focus on critical issues. 3. View Alert: Comprehensive Information at Your Fingertips The ‘View Alert’ functionality offers a detailed overview of data that meets the criteria of a business rule. Users can access information such as product details, scope, model, business rule name, serial number, customer information, priority, dealer details, timestamp of alert creation, and scope-related data. Additionally, the platform allows for the automatic creation of cases or manual status changes, providing a seamless workflow for issue resolution. When cases are created automatically, they are generated in Salesforce, accompanied by an email notification to stakeholders. This email includes crucial information like vehicle number, model name, case number, case URL, and the timestamp at which the case was created. Unveiling Performance Features •  Scalability in AI Alerts: TMAP’s Rule Engine incorporates AI Alerts, a feature that brings scalability to issue management. This feature includes: Automated Notifications: The ‘Notify’ function ensures proactive communication by sending email alerts when predefined rules are triggered. User-controlled Customization: Users can mute or unmute AI Alerts based on preferences, providing a personalized approach to information flow. •  Rule Engine Processing Time: Performance metrics also include the processing time of the Rule Engine, which is noteworthy. The Rule Engine demonstrates remarkable efficiency, processing over a million records within 10 minutes. This rapid processing time contributes significantly to the real-time  responsiveness of the platform. Performance Metrics: Driving Informed Decision-Making: AI Alerts go beyond mere notifications; they provide valuable statistical insights for informed decision-making. Here’s a glimpse of the statistical overview for 30 days: Alerts per Day, by Priority: A breakdown of the number of alerts recorded each day for different priority levels. Manufacturers can assess the frequency of alerts for each priority level daily, enabling them to allocate resources more effectively. Total Alerts by Priority: An aggregate view of the total number of alerts, categorized by priority. Status Overview: A comprehensive overview of case statuses, indicating whether they are open, closed, or in-progress. Additionally, the report includes details on muted alerts. This overview ensures that manufacturers stay on top of their operational challenges. Total Alerts by Scope: A summary of the total number of alerts within each predefined scope. Understand the distribution of alerts across different scopes, providing insights into areas that may require more attention or optimization.   Final Thoughts TMAP’s Rule Engine and AI Alerts is an advanced solution for proactive issue management. Empowering users with customizable rules, real-time notifications, and insightful statistics, this platform guarantees the agility, efficiency, and resilience of manufacturing processes in the face of challenges. TMAP’s capability to foster a proactive and responsive manufacturing environment positions it as a valuable ally for businesses aiming to elevate their operations to new heights.

Empowering DevOps Testing: The Strategic Evolution of Quality Assurance

Incorporating software testing into the DevOps paradigm can immensely affect project results. The main idea behind DevOps is that it promotes cooperation between different departments and helps to unify diverse teams. Teamwork is crucial in a DevOps approach. It fosters closer collaboration between the testers, developers, and operations staff in which they eliminate age-old walls that existed previously. This all-encompassing integrated approach not only addresses the voids across different teams but also provides quality, tested software with consistent quality to customers all the time to match the customer’s needs/expectations. Here are some of the critical benefits of empowering software testers in DevOps: Faster Delivery: DevOps focuses on CI/CD as a process of building, testing, and releasing software in much smaller increments than those used in traditional development approaches. CI/CD requires automation. It is crucial for there to be automated tests that skilled testers manage and execute. Automating the testing process will enable developers to detect potential errors early enough and rectify them at the initial stage rather than escalate them. In addition, it involves working hand in glove with developers to ascertain that the code is sufficiently and correctly tested during all stages of its development. For instance, testers can make the CI/CD process smooth through the automation of testing activities that reduce the time taken to release features to the user while at the same time ensuring the user gets timely quality updates.   Continuous Feedback and Iterative Improvement: Incorporating testers’ feedback into the development process creates a dynamic analysis, adjustment, and refinement loop. This allows developers to resolve problems in these applications, optimize them by improving their quality, and make the interface more user-friendly. Therefore, every cycle offers an opportunity for implementing enhancement, leading to continuous improvement in the software. The iterative development process requires constant feedback from the testers. The resulting insights enable improvements in software quality and facilitate innovations to ensure that every iteration benefits from the successes and lessons of the previous iterations. The result is an iteratively improving software that is equipped to meet today’s demands and near-future challenges or openings.   Increased Collaboration: DevOps is a transformational technique that breaks down the barriers within an organization as it involves easy flow of communication among varied teams. Empowering testers to participate in the whole development process encourages a culture that embraces shared ownership and accountability for developing high-quality software. Such alteration triggers team spirit that makes everyone involved, including developers, testers, and other stakeholders, feel responsible for the success of the whole product. Testers also bring a fresh take to design discussions, sprint planning, and retrospectives. They offer valuable input in which they share their expertise, which is essential for developing the overall software architecture and functionality. As such, it increases synergies, improving quality at large and developing products more suited to users’ expectations.   Improved Quality: When testers are given the authority to uphold the quality standard, they become adept at spotting and reporting defects at the nascent stages of development. As a result of this empowerment, it is possible to build robust testing methodologies that thoroughly examine the software from various perspectives. The emphasis shifts towards complete test coverage that covers numerous situations and use cases. In effect, such as in the case of an empowered tester, the outcome is improved through quality software that satisfies and surpasses users’ expectations.   Increased Customer Satisfaction: Enhanced software quality through empowered testers leads to timely bug fixes, immediate feature deliveries, and consequently higher customer satisfaction. Customers feel higher trust and satisfaction when they use a product without many problems and receive easy access to new functions. The efforts of such empowered testers directly influence this cycle of customer satisfaction, loyalty, and advocacy. They not only provide great user experiences, but they also establish an impressive brand impression. Together with prompt responses to bugs and the provision of innovative feature updates, they lay a solid basis for ensuring clients remain faithful to the brand and recommend it to others positively.   Cultural Transformation: When testers are empowered, they are not confined to their role but are seen as essential contributors to the development process. This helps keep everyone in mind that every member’s input is necessary for producing a first-rate product, and the group makes this possible by involving them. There is thus this sense of shared duty in which everyone in the company works towards improving processes, seeking out choke points, and providing innovative thought for the benefit of all.   In summary, empowering software testers in a DevOps environment creates a positive ripple effect. It accelerates the feedback loop, enhances software quality, and, most importantly, serves as a cornerstone for cultivating a DevOps culture within the organization. Empowered testers are critical enablers for successfully adopting and implementing DevOps principles and practices by supporting cooperation and an initiative-taking QA approach.

7 Principles for Quality
at Speed

The term “Quality at Speed” is synonymous with today’s modern software development practices, focusing on delivering high-quality software as fast as possible. These are suggestions that (we hope) will help teams ship quality software quickly. The specific details might vary depending on which framework or methodology you are working with (e.g., Agile, DevOps), but below are seven principles for delivering top-quality software development as fast as possible.   1.Shift Left Testing: It focuses on testing earlier in the Software Development Life Cycle (SDLC) than conventional practices. It implies that testing is done concurrently with the specific requirements gathering & design phase and continues up to the development phase. The purpose of it is to detect and correct flaws as early as possible – when they are the least expensive to fix. Collaboration between developers, testers, and other stakeholders is required. This ensures everyone is on the same page and testing is built into the development process.   2.Automate as much as possible: At its core, automation is efficiency, reducing the repetition, removing hand-touches, and guaranteeing process repeatability. Beyond the ability to develop and deploy more quickly, automation improves the overall quality of software systems while at a significantly lower probability of introducing human error. It covers the automation of repetitive operations such as code compilation, testing, logging, monitoring, infrastructure provisioning, deployment, release management, etc. Employ tools like Terraform, AWS Cloud Formation, etc., to automate infrastructure provisioning, reducing manual configuration and error-prone setups.   3.Continuous Integration, Continuous Delivery, and Continuous Testing (CI/CD/CT): Continuous integration (CI) entails automatically integrating code updates from many developers into a shared repository that happens many times daily. Continuous Delivery (CD) complements CI by automating the deployment process, allowing for more frequent and dependable releases. CI/CD pipelines can include automated testing, deployment to staging environments, and automatic deployment to production if all tests work fine. Continuous testing (CT) is the practice of running automated tests at all stages of the CI/CD pipeline, including unit tests, integration tests, regression tests, performance tests, and security tests. Automated testing gives instant feedback, allowing us to test our code and ensure that recent changes do not result in regression issues.   4.Security as Code: Security as Code is a set of principles and practices that allow security to be integrated into the software development life cycle (SDLC) in a repeatable and automated way. Incorporating security as part of the software development lifecycle (DevSecOps) means that security is no longer an afterthought. Security as Code suggests how security must be treated as a first-class citizen in the SDLC and how we can implement security measures in code. Security-testing tools can analyze the source code to identify potential weaknesses and non-conformities. It allows for the automation of security actions and simplifies scaling secure operations. It provides security cost reduction as well.   5.Create a culture of quality: Quality is everyone’s responsibility. This is one responsibility not delegated by a specialized QA team but also by the entire team involved. Teams must establish a culture in which every team member is responsible for delivering quality software. Developer, tester, designer, and other stakeholders — whoever impacts the product becomes accountable for the quality. Cultivate a culture of quality. We need to define expectations, give frequent feedback, celebrate successes, and hold everyone responsible for what they deliver.   6.Empowerment and Learning: We want teams to feel responsible for what they deliver and get increasingly better at their job. Over time, it results in better quality with less error. Fail fast is the mantra; experimenting must be encouraged, with failure being seen as an opportunity to gain experience and grow. By investing in training, team members remain current with evolving technologies and better ways of doing things. It could make for greater productivity and creativity.   7.Build small, incremental modules: Agile development practices such as Scrum or Kanban can help teams build small, incremental batches. These techniques help teams break down massive projects into bite-sized tasks that can be executed. All these principles allow developers’ teams to deliver quality software at speed, adapting to changes with the requirements while meeting users’ expectations for robustness and responsiveness in a highly competitive and rapidly evolving market. Together, these practices let teams deliver high-quality software fast, evolve the product or service, and satisfy customers’ expectations of dependability and speed, which are crucial in a world that provides software faster than you can count.

Mastering Data Archival Techniques: A Comprehensive Guide

In today’s data-driven business landscape, managing vast amounts of information efficiently is critical to maintaining optimal system performance, regulatory compliance, and cost-effectiveness. Data archival, the process of storing inactive data for long-term retention, is a fundamental practice for organizations, particularly those utilizing platforms like Salesforce. Understanding the nuances of data archival techniques is pivotal to ensuring seamless operations and future-proofing your organization’s data management strategy. The Essence of Data Tiering & Tiering Pyramid Data tiering is the practice of categorizing data based on its frequency of use and importance to the organization. This categorization allows for optimized storage and retrieval, enhancing system performance. The tiering pyramid is a conceptual framework that classifies data into different tiers: Tier 1: Operational Data (Full Search & Reporting) Tier 1 encompasses real-time operational data actively used for day-to-day business processes. This data must be readily accessible for immediate search, reporting, and decision-making. Salesforce’s platform is an ideal repository for this tier due to its quick access capabilities and seamless integration with operational processes. Tier 2: Historical Data (Limited Search & Reporting) As data ages, its frequency of access decreases. Tier 2 holds historical data that is still relevant but requires limited search and reporting functionalities. This data is essential for trend analysis and long-term business strategies. Leveraging Salesforce’s platform for this tier may be feasible, albeit with specific optimizations, to effectively manage the reduced search and reporting requirements. Tier 3: Archived Data (External Platform) Archived data, while no longer actively used, holds immense value for regulatory compliance, legal requirements, and potential future references. Tier 3 involves moving this data to an external platform, such as a data lake, allowing for cost-efficient storage and controlled API access for retrieval.   Exploring Archival Approaches Effective data archival demands carefully considering the platform’s capabilities and the organization’s needs. Here are three key approaches to data archival within the Salesforce ecosystem: Approach 1 – Archiving on Platform (Using Record Archiving Indicator) Salesforce offers a built-in mechanism for archiving data using the Record Archiving Indicator. This approach involves flagging records as archived within standard or custom objects. While this keeps data within the Salesforce environment, it may impact performance due to increased data volume. Effective data partitioning and indexing are essential to ensure smooth operations. Approach 2 – Archiving on Platform (Big Objects) Salesforce’s Big Objects provide a specialized storage mechanism for large volumes of data with infrequent access requirements. This approach suits Tier 2 and Tier 3 data, allowing seamless integration with existing Salesforce processes while maintaining scalability and performance. Approach 3 – Archiving off Salesforce Platform (Data Replication to a Data Lake) For Tier 3 data, where long-term retention is essential, archiving of the Salesforce platform is a pragmatic choice. Replicating data to a data lake offers cost-effective storage and control over API access. This approach minimizes the impact on Salesforce performance and aligns with the concept of data tiering.   Crafting Your Data Archival Strategy Devising an effective data archival strategy involves deeply understanding your organization’s needs, compliance requirements, and the platform’s technical capabilities. Here’s a roadmap to guide your strategy: Assessment: Analyze your data landscape to determine what data falls into each tier and its associated requirements. Platform Optimization: Optimize your Salesforce platform depending on the chosen archival approach. Implement data partitioning, indexing, and leverage platform features like Big Objects. Archival Policy: Define a clear archival policy that outlines when data transitions between tiers and when it’s eligible for archiving. Implementation: Based on your chosen approach, implement the necessary processes and tools for data archival, whether within the Salesforce platform or an external data lake. Testing and Monitoring: Rigorously test the archival processes and set up monitoring to ensure that data is being archived correctly and can be retrieved when needed. Documentation and Training: Document your archival strategy and provide training to relevant teams. This ensures consistency in data management practices across the organization. Continuous Refinement: Regularly revisit your data archival strategy to adapt to evolving business needs, compliance regulations, and technological advancements.   When to Archive Data Instead of Migrating Choosing between archiving and migrating data is a crucial decision in data management. Here’s when archiving is the preferred option: Compliance and Legal Obligations: Archiving keeps data accessible for compliance and legal purposes without complex migrations. Historical Analysis: Data needed for historical analysis or reference is best archived to preserve insights and minimize disruption. Cost-Efficiency: Archiving is often more cost-effective than data migration, saving resources and technology investments. Minimizing Disruption: Archiving has minimal impact on daily operations compared to potentially disruptive migrations. Long-Term Retention: Archiving suits data retention over extended periods, as it’s designed for long-term storage. Data Tiering Alignment: Align archiving with data tiering to maintain efficient practices. Scalability: Archiving helps manage data growth gracefully, especially when dealing with large volumes.   Data archival is not just about storage; it’s a strategic practice that impacts your organization’s efficiency, compliance, and future readiness. Mastering the art of data tiering and choosing the right archival approach is your key to unlocking optimal performance and data governance. By implementing a well-thought-out data archival strategy, you position your organization as a thought leader in efficient data management and set the stage for continued success in the dynamic world of business technology.

How to Improve Collaboration Between Your Developers and Testers

This proverb “a Tester & Developer are not two distinct entities but have adopted separate routes towards one common objective” is true to its words. While testers and developers think differently, their collaboration improves communication and mutual understanding. Only together will developers gain a deeper understanding of the benefits that thorough testing brings to the software development process. In contrast, developers can help to inform testers of any technical constraints and provide insights into potential implementation challenges. Through collaboration and sharing knowledge and perspectives, testers and developers stand to share much.   Here are some suggestions for promoting developer and tester cooperation: Early involvement of testers: Involve testers early in the development cycle, such as during requirement-gathering and design conversations. It allows testers to be able to give good feedback and identify potential test scenarios or problems to help them better understand the system and its intended purpose. Regular connects and communication channels: Set up ongoing meetings and communication pipelines among testers and developers to discuss requirements in detail, share updates, and address issues and concerns, if needed. This fosters transparency and ensures everyone is on the same page. Partnership in test planning: Promote collaboration between developers and testers during the process of test planning tasks. Testers will provide expertise in creating test scenarios and test case development, while developers will provide expertise in identifying risk areas and gaps in test coverage. Collaborative test case reviews: Run joint Test Case Reviews where developers and testers work together, reviewing test cases and providing comments. It helps align understanding, define specs, and establish any missing scenarios. Edge conditions or corner cases could be known to developers but might not have been considered by the testers. Continuous integration and automation testing: Use automated testing and continuous integration practices to have code integrated and tested throughout the day(s). Shared responsibility for the testing process allows developers to be part of the building/maintaining the automated tests, resulting in more time in the feedback loop and less burden on testers. Pair programming and coupling sessions: Promote tester and developer participation in Pair Program/pairing sessions — for working together on a particular feature or task. It promotes the sharing of know-how, and cross-training helps you learn more about what your peers do, as well as their perspectives and struggles. Continuous feedback and retrospectives: Collaboration needs to be evaluated through retrospectives as well as regular follow-up sessions. Encourage both testers and developers to provide constructive and open feedback to identify where improvements can be made and what has been done well. It provides an iterative feedback cycle that optimizes collaborating processes and fosters a culture of constant iteration. Knowledge-sharing sessions: Arrange lunch-n-learn sessions/Knowledge-sharing sessions where testers and developers can come together and speak about new topics they learned, share their experiences, or do some interactive workshop. Learning and sharing our experiences will create a fertile ground for sharing experience/knowledge transfer across borders.   By implementing the above mentioned points, testers, and developers can collaborate more successfully and help produce high-quality software.   Now, here are some insightful lessons that each group can pick up from the other: 1.Testers can learn from developers: Code quality with performance optimization: Writing clean, performant, and easy-to-maintain code is usually something developers are good at. From Developers — Testers can learn coding best practices to write better automation scripts and create reusable test cases, which will help improve test code quality. Developers can educate testers on optimizing the application, i.e., finding slow, high-resource locations (memory), detecting and fixing bottlenecks, and using profiling tools. Performance testing info can be used by testers to create performance tests or to identify performance issues. System architecture: Developers know very well how everything works and how the pieces fit together in the system architecture. Testers can use the architectural expertise inherent in development teams to identify potential hotspots and build tests aimed at core functionality. Technical skills: Programming languages, frameworks, and design patterns are valuable knowledge a developer can pass on to a tester based on their technical expertise. It can help testing teams better understand the implementation and write tests that are much better than before. Testability: By learning how developers write testable code, they can build better test cases, which leads to more reliable and sustainable test suites. Developers should advise regarding strategies such as dependencies injection, mocking, and modular design, which aid in testing the code.   2.Developers can learn from testers: Domain knowledge: Testers know the business domain and end-user requirements very clearly. They can share their domain knowledge with programmers who help them understand how their software will run within different environments. This data can give developers a leg up on identifying what users really need from a feature and how to design it accordingly. User perspective: During testing, testers often consider how end users use the application. Developers can learn from real-world user interaction, understand their pain points, detect usability issues, and make informed design decisions catering to the user’s needs if they work closely with testers. Test design and Test automation: Testers focus on designing testing processes where fallacies come to light and the system’s functionality gets validated. Testers can train developers on test design principles like boundary value analysis, equivalence partitioning, or ad hoc/exploratory testing. Developers can use these strategies as they develop to build better unit tests, which will find problems sooner rather than later. Testers know how to generate auto-tests. Testing folks can offer developers their insights on various test automation frameworks, tools, and practices. This insight allows developers to craft Unit tests, Integration Tests, and even Auto UI tests, leading to better Test coverage during the development process. Adaptability and resilience: Testers often face evolving requirements, tight deadlines, and changing priorities. They develop resilience and adaptability to deal with these challenges. Testers demonstrate skills in dealing with uncertainty, flexibility, and the ability to deliver value in an agile or iterative context — this is something developers can learn.   Tavant is actively exploring and integrating these

Test Automation Coexists Well with Exploratory Testing

In exploratory testing, the tester analyses the software system without utilizing a formal test plan or script and instead relies on their expertise and intuition to spot any flaws. It is notably helpful for detecting brand-new, unforeseen problems as well as weaknesses that less formal testing methods can overlook. Also, it is a fantastic technique to evaluate user experience and assess the software from the viewpoint of the user. On the other hand, end-to-end automated regression testing is a more formalized method of testing that uses automated testing tools and scripts to conduct a series of pre-defined tests on the program. Ensuring that new software system additions do not negatively impact its functionality is a crucial part of software testing. After changes have been made, a series of automated tests must be run to verify that the software operates as expected. Here are the top 10 reasons we believe that reliable automated end-to-end regression testing is crucial for software testing and that, in the absence of it, exploratory testing can be jeopardized: Coverage: Automatic end-to-end regression testing can examine a wide range of situations, giving full coverage of the software’s functionality. Potential problems could go unnoticed during exploratory testing if certain conditions or components of the product are not examined. Precision: As automated end-to-end regression testing is not subject to human biases, errors, or oversights, it can produce more accurate and dependable results. Exploratory testing can be subjective and based on the tester’s perception, which might produce incorrect results or lack valuable information. Scalability: Automated end-to-end regression testing can scale up or down depending on the program’s complexity and the project’s demands. Especially for large and complicated software systems, exploratory testing cannot be scalable as it can be difficult to test all the functionality manually. Uniformity: Automated end-to-end regression testing guarantees consistency in the testing process by ensuring that the same tests are rerun. Exploratory testing relies heavily on the tester’s knowledge and judgment, which makes it challenging to conduct tests consistently. Human error: Exploratory testing is more likely to involve human mistakes, which could lead to overlooked flaws or false positives. By conducting tests regularly and accurately, automated end-to-end regression testing can help lower the chance of human mistakes. Maintenance: Maintaining test suites as the software develops without automated end-to-end regression testing might be difficult. Exploratory testing’s effectiveness may be jeopardized if it takes a lot of work to keep up with software updates. Continuous Integration and Delivery: Integrating testing into a continuous integration and delivery (CI/CD) pipeline can be problematic without automated end-to-end regression testing. Because of its nature, exploratory testing does not fit into a CI/CD pipeline, which could slow down software delivery and reduce its efficacy. Timesaving: Automated end-to-end regression testing can save time and effort by swiftly completing a substantial number of tests. Conversely, exploratory testing may take a long time and require a lot of work to find and recreate problems. Cost-effectiveness: Automatic end-to-end regression testing reduces the requirement for manual testing and lowers the likelihood of software flaws, both of which can result in cost savings. Exploratory testing may sometimes offer a different amount of coverage than automated testing and can be expensive, mainly when performed in detail. We agree that automated testing, however, might only be able to catch some potential problems and might take a lot of time and money to set up and maintain, but it is very cost-effective eventually. Risk reduction: Automated end-to-end regression testing helps reduce the risk of software failures by ensuring that new modifications do not impact existing functionality. Exploratory testing may not offer the same level of risk reduction as automated testing, but it can assist in uncovering potential problems. In conclusion, exploratory testing and automated end-to-end regression testing are two different approaches to software testing with their own unique advantages and disadvantages. While exploratory testing might offer insightful information about software problems, more is needed to replace reliable automated end-to-end regression testing. Automated end-to-end regression testing is necessary to guarantee thorough and trustworthy testing of software systems. Using both forms of testing can assist assure complete and reliable software testing.

Harnessing the Power of Salesforce Hyperforce: A Deep Dive into the Future of Cloud Infrastructure

Salesforce, a trailblazer in cloud-based customer relationship management (CRM), has revolutionized the digital sphere and debuted a groundbreaking infrastructure architecture called Salesforce Hyperforce. This innovative offering is poised to redefine how organizations utilize the cloud to boost their Salesforce applications and drive business operations to unprecedented heights. The Emergence of Hyperforce Salesforce Hyperforce signifies a profound shift in Salesforce’s infrastructure strategy. Unlike traditional models where Salesforce hosted customer data and applications in proprietary data centers, Hyperforce paves the way for organizations to execute their Salesforce applications on public cloud platforms. This flexible architecture empowers businesses to harness leading cloud providers’ scalability, security, and high-performance capabilities.   Key Features of Hyperforce Hyperforce is armed with several standout features designed to meet the evolving needs of digital businesses. Compliance: Hyperforce allows storing data locally while adhering to global compliance standards. Users can select the data storage location, ensuring compliance with regulations specific to the company, region, or industry. Scalability: Digital companies worldwide can leverage Hyperforce’s scalability to facilitate their growth. Hyperforce enables flexible infrastructure implementation, allowing users to deploy resources in the public cloud while retaining complete control. Compatibility: Hyperforce can seamlessly integrate with all existing Salesforce applications, customizations, and integrations. This ensures backend compatibility and minimizes disruptions. Security: Hyperforce prioritizes the safety of organizational data, providing robust security measures that operate in the background to ensure privacy and security. The Driving Force Behind Hyperforce Hyperforce was conceptualized to address the challenges faced by Salesforce users in storing large volumes of data due to storage limitations. By enabling users to utilize public cloud infrastructure for data storage, Hyperforce offers a solution to many scalability and geographic location issues. Global Availability of Hyperforce Hyperforce promises extensive reach, with Salesforce committing to making it available in every region through major cloud computing providers. Unraveling the Benefits of Hyperforce Hyperforce offers many benefits to Salesforce users, each designed to enhance operational efficiency and performance. Swift and Easy Resource Deployment: Hyperforce facilitates quick and straightforward deployment of resources in the public cloud, significantly reducing implementation time. Enhanced Security Architecture: Hyperforce’s security architecture restricts users’ access to customer data, safeguarding sensitive information from human error. Standard encryption ensures privacy and security. Data Localization: Customers can store data in a specific location to support compliance with regulations specific to their company and region. Wide Compatibility: Every Salesforce application, customization, and integration can run on Hyperforce, offering extensive compatibility. Benefits of migrating to Salesforce Hyperforce Hyperforce public cloud providers offer their services for various regions. It is beneficial for companies to select the region that is as close as possible to the organization, thus reducing concerns about non-compliance with regional laws and regulations. Public cloud providers not only ensure that Salesforce, through Hyperforce, always has the necessary resources to support their customers’ growth but also guarantee scalability in a sustainable way. Below are a few benefits of migration over Hyperforce: Your data will be more secure than before – Hyperforce’s security architecture implements principles such as least privilege, zero trust, and encryption of customer data. Control over the privacy of your customers’ data is guaranteed – Ensure that cloud service providers have the necessary procedures and controls to comply with legal obligations regarding the processing of private data. Accelerate the performance in the execution of your applications – With Hyperforce, all the performance and resource issues disappear since this architecture does not require Salesforce to invest much energy and effort into them. Public cloud providers ensure and meets all the running needs of organizations regardless of whether they are test, development, or production environments. Implications for Businesses Hyperforce presents businesses with new opportunities and considerations for strategic planning. Future-Proofing: Embracing Hyperforce allows organizations to future-proof their Salesforce infrastructure. They can leverage the constantly evolving capabilities of public cloud providers, ensuring their CRM platform remains innovative. Enhanced Innovation: Hyperforce enables businesses to tap into the vast ecosystem of cloud services and third-party integrations offered by their chosen cloud provider, fostering innovation. Cost Optimization: Hyperforce allows businesses to pay for their required cloud resources, leading to potential cost savings. Fueling Innovation with Hyperforce Hyperforce enables businesses to access the vast ecosystem of cloud services and third-party integrations offered by their chosen cloud provider. This fosters innovation and allows organizations to build custom solutions that extend the functionality of Salesforce to meet their unique business requirements. Cost Optimization with Hyperforce Hyperforce allows businesses to optimize costs by paying for their required cloud resources. With the ability to scale resources up or down as needed, organizations can avoid over-provisioning and only pay for what they use, resulting in potential cost savings. Conclusion Salesforce Hyperforce opens new possibilities for organizations looking to supercharge their Salesforce applications. By leveraging the power of public cloud platforms, businesses can achieve enhanced scalability, improved performance, and greater control over their Salesforce deployments. As Salesforce continues to push the boundaries of cloud innovation, Hyperforce stands as a testament to the transformative potential of harnessing the full power of the cloud. 08/17/2023 Simran Tayal

Supercharging Service Contracts for Success: The Analytics Advantage

In today’s digital age, data is continuously generated from various sources, and businesses have access to vast amounts of valuable information. However, managing and extracting insights from this data can be a daunting task without the aid of advanced technology and analytics. This is particularly true for Service Contracts, where the success of these agreements depends on understanding customer behavior, equipment performance, market trends, and more. By leveraging advanced analytics, OEMs can effectively navigate through the sea of data, gaining actionable insights to make informed decisions. The true potential of advanced analytics lies in its ability to revolutionize service contract offerings, leading to improved operational efficiency and enhanced customer satisfaction. By embracing analytics-driven service contracts, OEMs can create a win-win situation, ensuring their consumers receive fair and transparent pricing, optimized contract options, and proactive support Let’s explore some of the key analytics options and understand how they drive business value for both OEMs and their customers: • Pricing Analytics Pricing Analytics empowers OEMs to understand price elasticity and set competitive contract prices that maximize profitability. By leveraging statistical modelling, machine learning algorithms, and market research, OEMs can analyze historical data, market trends, customer behavior, and contract performance. This analysis allows them to identify pricing patterns and optimize contract prices, ensuring both profitability and value for their customers. • Portfolio Optimization Portfolio Optimization involves tailoring service contract offerings to match customer needs while maximizing profitability. Through customer segmentation, contract performance analysis, and market demand evaluation, OEMs can identify the most valuable combinations of service contracts. This ensures customers get the precise coverage they require, leading to enhanced equipment performance and reduced downtime. • Profitability Analysis for Informed Decision Making By analyzing the financial performance of service contracts, OEMs can identify high-profit contracts and optimize low-profit ones, leading to overall enhanced profitability and sustainable growth. This analytics-driven approach enables OEMs to allocate resources effectively, prioritize contract management efforts, and make data-driven decisions that impact the bottom line positively. • Internet of Things (IoT) Analytics Utilizing IoT Analytics, OEMs can proactively address equipment maintenance needs, minimize downtime, and improve equipment reliability, ultimately resulting in higher customer satisfaction. IoT-connected devices provide real-time data on equipment health, usage patterns, and potential failures, enabling OEMs to take timely and informed actions. • Data Analytics for Enhanced Insights and Decision MakingBy applying machine learning, data mining, and predictive modelling, OEMs can gain deeper insights into contract performance, customer behavior, and market dynamics. This enables them to identify trends, predict service demand, anticipate customer needs, and optimize service contract offerings for greater customer value. • Remote Monitoring and Diagnostics Efficient Equipment SurveillanceRemote monitoring and diagnostics allow OEMs to keep track of equipment health, detect issues, and provide timely support without physical presence. This reduces response time, lowers service costs, and ensures efficient resource allocation, resulting in quick problem resolution and improved operational efficiency for customers. • Service Demand Forecasting for Effective Resource Planning By proactively aligning resources with anticipated service demand, OEMs can optimize service delivery, improve customer satisfaction, and reduce operational costs. Through historical data analysis, market trend evaluation, and predictive modelling, OEMs can accurately forecast service demand and plan their resources accordingly. Benefits of Service Contracts with Advanced Analytics Impact on Revenue Generation in Service Contracts: Optimized pricing, portfolio, and profitability analysis lead to increased revenue generation for OEMs, while customers benefit from fair and competitive pricing. Enhanced Equipment Performance: IoT Analytics and remote monitoring ensure better equipment reliability and performance, reducing downtime for customers and enhancing their operational efficiency. Data-Driven Decision-Making: Advanced analytics enables OEMs to make informed decisions based on data insights, resulting in better strategic planning and resource allocation. Cost Optimization: By identifying high-profit contracts and optimizing low-profit ones, OEMs can effectively manage costs and improve overall profitability. Improved Customer Satisfaction: With proactive support, personalized service contracts, and optimized offerings, customers experience higher satisfaction levels, fostering long-term relationships with OEMs. Final Thoughts Embracing advanced analytics in service contracts is the key to unlocking operational efficiency and profitability for OEMs while ensuring customers receive unparalleled value and support. By harnessing the power of data through analytics, businesses can stay ahead in today’s competitive landscape and offer their consumers a truly transformative service contract experience.

GIS Technology: Enabling Pinpoint Precision

Unraveling the complexities of modern agriculture, it’s crucial to understand the recurring expenditures that the farming community shoulders each season. At the heart of these are the procurement of seeds and fertilizers, key expenses that can make or break a harvest. Traditional farming techniques rely heavily on manual methods, increasing the expenses braced. Its efficiency and productivity directly result from the skilled labor acquired to run a farm. This results in a best-case scenario that revolves around the farmer’s skill in uniformly applying fertilizers, pesticides, planting seeds, and more. It does not account for variability within the same field. The soil compositions, microenvironments, and microflora often differ even if they are in the same vicinity and are factors that cause this variability. This landscape diversity inevitably necessitates tailored approaches in terms of both the type and quantity of farming inputs, adding yet another layer of complexity to this age-old occupation. So how does precision farming account for this variability? GIS technology, metrological inputs, and custom software are all leveraged by precision farming to boost production by accounting for temporal and spatial variability, assisting farmers in making automated decisions to lower expenses and inputs while maximizing profit. The system cumulates multiple input points like weather data, soil data, tissue sample results, and more to create different types of prescription(s) for the fields. These inputs are fed automatically to the planter, which can apply the product using GIS technology. These systems can also display historical crop data and yields through their sensors located throughout the field.     The Role of GIS Technology in Precision Agriculture Best case scenario: these seeds are planted uniformly across the field. Variability in the soil composition and growing conditions produces variability in yield outputs from various field zones. Applying fertilizer uniformly also has the same effect. Historically farmers have studied yield maps of their fields to create management plans based on historical yield data. GIS technology ensures optimal productivity from the soil by inspecting every square unit in detail. Based on soil data, weather data, and in-season satellite imagery monitoring of plant growth, GIS technology allows a farmer to focus on the best-yielding areas within the field, ensuring optimum use of resources and helping in averaging the yield from all variability zones. The reverse is also possible, with farmers minimizing resource allocation in low-yielding zones and saving on seed and fertilizer costs.   GIS Technology use cases: Satellite images or NDVI (Normalized Difference Vegetation Index) images:  Users can see satellite images of their field showing how a crop is performing and take action accordingly Drone (Unmanned Aerial Vehicles) images: Drone images are another way of checking crop health. Users can fly drones and see high-resolution field images during the growing season Rx maps(prescription map also called variable rate prescription): Using drone and satellite imagery, users create variable rate prescriptions, similar to how a doctor would prescribe medicine, except this is for the soil, with the focus being maximized yield. Boundary management through GIS tools: User can manage their farm/field and boundary using any GIS tool (e.g., a custom tool built using open layers). Users can then draw boundaries using the GIS tool or import limitations from other devices to map out their fields perfectly. Scouting: Technology partners like Tavant can build custom applications that help take pictures of the crops and maintain notes. Enabled with predictive AI algorithms, it can detect potential diseases. Tissue sampling: The user can take tissue samples during the growing season and make result-based informed decisions. Water management: The user can place sensors in the field to turn on sprinklers based on moisture presence.   Benefits of GIS/Geo Spatial Technologies in Precision Agriculture: They help locate precise positions on a field, allowing for mapping creation. E.g., farmers can draw their fields geospatially on any map (such as Google Maps). There are open sources like Open layers, which provide Java Script libraries to display map data from different sources without requiring code change on the change of map provider. GIS tools/technologies help fetch satellite images from various satellite providers, intersect based on field boundary, display maps (such as NDVI), and more as a layer on the field. Users can see in-season images corresponding to their fields remotely. Depending on the requirements, private and Govt satellites (e.g., Landsat in US and Sentinel in Europe) are used to access these images of specific resolutions. Users can fly drones with high-resolution cameras over the field and get in-season images to take appropriate actions (E.g., a particular field area may need pesticides or any other special treatment). Going to every site to identify the insects/disease could be tedious. Identification is resolved by looking at high-resolution pictures provided by these satellites and identifying potential diseases. Custom apps are built with disease identification as the objective by feeding the image to machine learning models to determine the cause. Users can also use drones to spray fertilizers remotely with precision and efficiency. Not all areas within a field are the same, and different areas/zones may need additional treatment/seeds. E.g., we could put high population seeds in more fertile areas and other seeds in less productive areas. GIS tools (requiring custom implementation) allow users to divide fields into multiple zones/areas and write a prescription map for the entire field. Users can assign different seeds/products to various locations. This prescription map goes as input (through USB or cloud – in case the planter/combine has internet) to the GPS-enabled planter, and it automatically applies the product (along with the prescribed quantity) as per the prescription. Farmers can sit in an auto steering planter and physically see the planter driving independently and applying different seeds in different areas accurately. Users can also see the real-time output on the monitor, which applies to applications like liquid/solid fertilizer during the season. This data transfer from the planter cloud system to the precision ag application that farmers may use can also be automated. Farmers can plan to take tissue samples from different areas of the field (based on

Driving Innovation in Warranty and After Sales: The Role of Generative AI in the Manufacturing Industry

Generative AI has gained significant prominence worldwide in 2023, transforming the way researchers, enthusiasts, and software developers tackle machine learning and artificial intelligence challenges. Generative AI is an artificial intelligence subfield that can create content in the form of text, images, music, and code. A massive amount of text data is used to train these models. Let us examine some use cases of these models in the manufacturing industry. Text Generation and Summarization: Large language models can generate text in a conversational and human-friendly manner. These models support several languages and aid in use cases such as producing content for marketing and sales departments, supporting developers with code documentation, and assisting developers in understanding the code written. Long-format papers can be summarized using Generative AI models to deliver precise, context-relevant information. Summarization can be tailored to the user’s preferences. Semantic Search Systems: These models can be used to build search and knowledge-based systems that can recognize the context in user queries and return relevant information, enhancing user acceptability and search experience over traditional keyword-based search systems. Question and Answering Systems: The generative models may also answer user queries by recognizing the context of the query and generating answers utilizing knowledge learned from massive amounts of data relevant to the user inquiry. Synthetic Data Generation: Generative models, with their vast knowledge base comprising massive amounts of data, may generate synthetic data for experiments and training machine learning models in situations where real-world data is unavailable. Image Generation: Generative models can create images with various artistic styles, settings, and colors. These are useful in generating synthetic images to aid users in machine learning modeling.   Applications in Manufacturing – Warranty and After Sales Claim Process Optimization: Warranty dealers and claim processors can use Generative AI models to revolutionize question-answering systems by answering queries with interpretable and appropriate reasoning by understanding the context and semantics of queries using a large number of documents. The systems shorten the procedure and optimize it. Customer service and support: Using generative language models such as GPT3.5 and GPT4, personal assistants and chatbots can be constructed to aid customer support teams in addressing client inquiries and issues relating to warranty, claim procedures, and troubleshooting steps. These models can also help with faster claim processing and provide a better client experience. Warranty Claim Validation: Claims processors can use Generative models to analyze and validate dealer claims. These models use warranty information, product specifications, and claim information to identify patterns of fraudulent claims and make decisions to automate the validation process, prevent fraud, and speed up claim settlement. Recommendations: Using usage patterns and historical data, large language models can provide individualized recommendations to clients and dealers regarding warranty coverage and upgrades. Text Sentiment Analytics: Customer evaluations and feedback can assist warranty providers and dealers in improving their service, identifying and resolving reoccurring issues, and enhancing the overall customer experience.  Without the need for training, generative models can assist in determining the sentiment of the text. These models extract textual patterns and provide reasoning for sentiment prediction. Intelligent Search System: Generative AI models can aid in the creation of a centralized knowledge base that dealers, technicians, claim processors, and warranty providers can use to find and obtain relevant information on claims, warranties, troubleshooting common issues, service manuals, and FAQs. It lets you quickly discover root causes, potential part replacements, SLAs, and applicable resolution actions. It can return relevant search results and citations, as well as supporting content related to the context of the query. Quality Control and Defect Detection: Generative AI algorithms can analyze a large amount of manufacturing data, including sensor readings and images, and process this information to detect defects and patterns identified in the data.   Tavant is actively exploring and integrating these cutting-edge features into the highly advanced Tavant Manufacturing Analytics Platform (TMAP). This strategic initiative aims to empower customers with a distinct competitive edge by utilizing advanced Generative AI models. In our initial forays into this dynamic field, we have successfully developed compelling POCs in the domains of chatbots, personalized assistants, and smart-search systems. Leveraging warranty after-sales data, these pioneering POCs deliver unparalleled value to dealers and claim processors. Some of the modules in TMAP where we are exploring Generative AI models are: Warranty – Automate claims processing, identify suspicious information, improve dealer performance, reduce warranty spend, enhance the quality of the claim, and identify anomalies in the image. Price – Recommend optimal parts price, completive pricing analysis, evaluate the performance of pricing strategies, monitor and alert price changes, and segment customers based on their price sensitivity. Quality – Identify product quality issues, failure rates, and areas for improvement by analyzing claims, returns, and repairs. Field – Optimize services using AI Smart search, service & parts demand to forecast, and real-time insights enabling you to improve service quality and enhance customer satisfaction. Contract – Enhance contract performance, improve profitability, mitigate risks, and strengthen customer relationships through personalized contract offerings and optimized prices.   Final Thoughts By utilizing the various text content available, such as installation and warranty manuals, service guides, and safety guidelines, Generative AI can transform the manufacturing industry by enabling technicians, dealers, and manufacturers with personalized assistants, chatbots, intelligent search systems, and recommendations. This can assist dealers in providing excellent customer care, as well as business users in identifying potential issues and improving the product and after-sales services.

Unlock the Power of Financial Services Cloud: Revolutionize Your Business Today!

WHAT IS FINANCIAL SERVICES CLOUD? As businesses constantly seek innovative solutions to streamline processes, enhance customer experiences, and stay ahead of the competition, Financial Services Cloud is emerging as a trailblazing platform, revolutionizing how financial institutions streamline operations and cultivate meaningful client interactions. The financial services industry has unprecedented potential to interact with customers, and Financial Services Cloud can help you get there. Financial Services Cloud is the world’s first CRM reinvented for the financial services industry. It is intended to assist everyone from personal bankers to financial advisors in seizing the chance to earn client trust and loyalty via meaningful interactions. Financial Services Cloud continues to push innovation thrice a year based on industry leaders’ feedback. It has added new functionality such as shield encryption, analytics, and communities for partners, workers, and customers. Connect your entire institution across lines of business, geographies, and channels, from retail banking to wealth management, to place your clients at the center of every contact. This powerful tool harnesses the power of cloud computing to deliver a seamless, integrated experience that caters to the unique needs of banks, insurance companies, wealth management firms, and other financial service providers. In continuation of part 1, this blog will delve into the world of Financial Services Cloud, exploring its key features, benefits, and how it’s transforming the financial industry for the better. Unlock the full potential of your financial institution. Financial institutions may take a significant step toward eliminating silos across lines of business and collaborating as one team to support consumers along their financial life journey using Intelligent Needs-Based Referrals and Scoring in the Financial Services Cloud.   FINANCIAL SERVICES CLOUD SUB-VERTICALS FSC helps financial institutions to provide services for these sub-verticals. 1. Wealth Management — Assists their clients in growing and protecting their wealth. Personalize Wealth Client Relationships at Scale – Capture and visualize financial account information, goals, trusts, business groups, and interactions within and across clients, households, and relationship networks. Supercharge Advisor Productivity – Jumpstart every advisor’s day with a tailored list of tasks, client life events, opportunities, and access to essential client information aggregated by integrated partner solutions — all in one place. Make Smarter and Faster Client Decisions – Put artificial intelligence to work for your advisors so they can personalize every engagement with immediate insights and subsequent action recommendations.   2. Banking — Lends, holds, and invests money for customers and businesses. Know Your Customers and Their Needs – Track and visualize key customer relationships and financial information and keep context with a single pane of glass for managing customer engagements. Delight Customers with Convenience and Consistency – Provide commercial clients with a streamlined onboarding experience powered by automated task orchestration and contextual customer surveys. Unify Relationships Across All Lines of Business – Connect retail and commercial banking on the same platform for rich customer insights in-segment and bank-wide. Understand household and business financial needs and source referrals across lines of business from customers or their circles of influence.   3. Insurance — Serve the changing need of every policyholder and share risk among a group of people. Know Your Policyholders – Get always-on panoramic views of performance metrics, insights, and actions across each policyholder’s family, claims, and business milestones. Be Smarter with Built-in Analytics – Empower agents with rich analytics and real-time insights that provide recommendations for the proper coverage. Deliver Exceptional Service – Connect agents and customer service representatives with relevant insights about policyholders with out-of-the-box dashboards.   4. Mortgage & Lending Streamline Mortgage Lending – Deliver a seamless lending experience with a single view of each borrower’s loan applications, documents, accounts, and relationships. Offer step-by-step guidance and transparency and get integrations to digitize the entire process. Increase Loan Officer Productivity – Connect systems, channels, and processes to streamline handoffs. And coordinate partners like realtors, brokers, and appraisers to translate data into actionable insights. Deepen Borrower Relationships – Increase visibility into borrowers’ financial, household, and employment information to prioritize relationships and collaborate across lines of business.   FINANCIAL SERVICES CLOUD ARCHITECTURE & DATA MODEL Financial Service Cloud comes with OOB structured and pre-build data models specific to tailor each need of financial sector client. It provides insightful information at every stage of a client’s lifecycle. FSC Managed Package Data Model The above FSC managed package diagram includes the Sales & Service cloud objects, FSC standard, and package objects. FINANCIAL SERVICES CLOUD PACKAGING Financial Services Cloud functionality comes up with two packages. One is managed package that delivers most of the features, and another is an unmanaged extension package that provides the field sets. • Managed Package It includes most FSC functionality, with custom fields and objects, list views and profiles of clients and households, and administrative configurations. • Unmanaged Package The unmanaged extension package provides field sets that configure how fields display in the client and household profiles and retail banking dashboard, and the banking extension package provides the commercial banking dashboard. DATA SECURITY WITH SALESFORCE SHIELD Financial Services Cloud with Salesforce Shield assists financial services institutions in complying with industry regulations, such as the U.S. Department of Labor’s Fiduciary Rule; it can support firms with visibility into interactions between clients, advisors, agents, and teams. With a Client Data Model at the center of Financial Services Cloud, firms can easily track client relationships and follow each interaction to help achieve compliance. Salesforce shield supercharges organizational security in three ways. Field Audit – Giving financial service firms a valuable record of how their data has changed. Industry regulations require institutions like banks to record changes to track necessary fields. Platform Encryption – Encrypts sensitive data such as PII, credit card, or bank account information at rest, meaning that even when data is not being transferred anywhere, platform encryption is a must for complying with industry regulations and internal policies Event Monitoring – This feature can show what users are accessing, when they’re accessing it, and from where. It’s also essential for complying with industry regulations like FFIEC, SOX, and PCI   FINANCIAL SERVICES CLOUD USE CASE Below are a few of the industries specific use cases: • For Banking  Problem Statement – In today’s world, people love to get

Tapping into a Booming Home Equity Lending Market

Inflation Drives Consumers to Seek Alternative Forms of Credit According to a report released by the Bureau of Labor Statistics, the blistering consumer price index was 9.1 percent higher in June 2023 than it was a year ago and 1.3 percent higher than in May, revealing scant signs of progress in the fight against inflation. This has created opportunities elsewhere; financial institutions are leveraging credit cards and home equity lending to extend credit to consumers. A Resurgent Market for Home Equity Lending Over the last two years, American homeowners have spent more time at home. With so many Americans working, exercising, and attending school from home, homeowners are looking to upgrade their spaces and invest in the places where they spend most of their time. Many people who have recently purchased a new home are looking for ways to make it feel more like a home, such as purchasing a couch to fit the new living room. With the average-priced home up 42 percent in value since the pandemic began, current homeowners with mortgages have an average of $207,000 in equity and in the first quarter of 2023, 44.9 percent of the homes in the United States were considered “equity-rich,” meaning the balance of the loan on the home was 50 percent or less of the estimated market value. Acting on this knowledge is an excellent example of anticipating a customer’s needs. Customers in these circumstances are likely to qualify for a home equity line of credit (HELOC). Between January and May 2023, fixed 30-year mortgage rates increased from 3% to over 5%. According to the Mortgage Bankers Association, the average monthly payment on a new mortgage has gone up by $513 since 2008. This is because interest rates and home prices have gone up quickly. Nonetheless, HELOCs have grown significantly in popularity in the last year because they allow homeowners to withdraw cash from their homes without changing the interest rate on their entire mortgage loan. According to TransUnion, while a borrower’s interest rate on a HELOC may be higher than the interest rate on the entire mortgage, it is still likely to be lower than the interest rate on a personal loan. Targeting the Right Audience With HELOC and home equity financing more readily within reach of homeowners, lenders need to step up marketing efforts and enhance overall communication with borrowers to engage them in a conversation about the benefits of leveraging their home equity. There is a lot of opportunities available for smart lenders who have the right home equity marketing in place. Capture the Growth Potential The top home equity lenders must focus on six key actions to best position themselves, capture a market that is gradually coming back to life, and capitalize on a tremendous opportunity. Boost their digital ecosystem Integrate and optimize search engine marketing Leverage data as a strategic asset Excel at turning leads to loan applications Bring out a customer-centric fulfillment model Streamline the fulfillment process   Wrapping up: We cannot, unfortunately, predict the future. But we can prepare for it. A HELOC can give you the financial flexibility you need to deal with whatever comes your way, good or bad. Whatever the situation, you’ll be ready to seize incredible opportunities or protect yourself from the stress that life frequently throws at us. According to a recent Bankrate survey, 14 percent of millennial mortgage holders say they’d tap home equity to bankroll a vacation, compared with just 4 percent of Generation X  and 3 percent of baby boomers who believe the same. Discover all that Tavant can do for you: Tavant leverages its heuristics research, in-depth industry knowledge, and engineering expertise to provide a simple and frictionless experience to consumers tapping into the home equity market. We expanded our Touchless Lending® platform for the lending industry’s home equity line of business and offer software that enables HELOC to help users deliver a seamless channel, device, and interaction-agnostic experience across the loan application process. Over the last 12 months, Tavant has helped home equity lenders serve five times more customers than they ever served as a business, providing them with the scale to meet their borrowers’ demands. Touchless Lending® is the industry’s leading AI digital platform that maximizes the use of data-driven processes in the automation of the loan origination lifecycle. To learn more, reach out to us at [email protected]. FAQs – Tavant Solutions How does Tavant help lenders capitalize on the home equity lending market?Specialized platforms with automated valuation, streamlined application, and real-time market data integration allow lenders to assess equity and process loans efficiently. What competitive advantages does Tavant offer for home equity lending?Faster processing, accurate automated valuations, integrated credit decision engines, seamless digital customer experience, and reduced operational costs. Why is the home equity lending market booming?Rising home values, increased equity, low interest rates, and growing awareness of home equity financing for improvements and debt consolidation. What types of home equity lending products are available?HELOCs, fixed-rate home equity loans, and cash-out refinancing with varying repayment structures. How much home equity can borrowers access?Typically 80-90% of current home value minus outstanding mortgage; depends on credit, income, DTI, and lender policies.

Exploratory Testing: The Most Valuable Viewpoint for Testers

Software testing is a practice that helps to assure the quality of software products and is a decisive component of software development. The extensive topic of testing covers a broad range of techniques, strategies, and tactics. The most crucial testing technique is exploratory testing.   Exploratory testing: what is it? Exploratory testing is a strategy that strongly emphasizes the tester’s abilities, expertise, and experience. The tester uses this methodology to go deeper into the software product to find flaws and problems that may have escaped notice during previous testing procedures. In exploratory testing, test cases are developed as they go. Identifying potential problems depends heavily on the tester’s experience and understanding of the product and its users. Compared to other testing methods, this one is more adaptable and enables testers to modify their testing to the current state of the product and testing environment. In this article, we will go through what exploratory testing is and why it is the ideal viewpoint a tester needs. As a result of the many advantages it offers, exploratory testing is frequently referred to as a tester’s best friend. Exploratory testing is a tester’s best friend for the following reasons: Creativity and Innovation: It enables testers to apply their creativity and inventiveness to find problems that might not be readily apparent using a conventional testing approach. The tester can utilize their intuition to spot problems other methods might overlook because they are free to explore the software product without being constrained by preset test cases. Provides Rapid Feedback: It offers quick feedback because the tester can spot and report problems immediately. This enables developers to correct problems rapidly and raise the caliber of the software before it is made available to users. Helps Align Testing with User Needs: It can help align testing with user needs since it allows the tester to explore the software product from the user’s point of view. This can help guarantee that the software product satisfies the requirements of its target audience and offers a satisfying user experience. Increases Efficiency: It can be more effective than other testing methods because it does not need the construction of detailed test plans, which reduces costs. Instead, the tester can quickly locate and carry out tests pertinent to the software product’s current state using their knowledge and experience. While still maintaining the quality of the software product, this can help testers save time and resources. Improves Test Coverage: It can increase test coverage since the tester has the freedom to investigate the software product in several ways. This can assist in finding problems that other testing methods might have overlooked, enhancing the software’s overall quality. Not at Random: It is crucial to remember that exploratory testing is not a random or ad hoc technique, even though it is sometimes linked with a lack of organization or strategy. The main distinction between exploratory testing and traditional testing is that in exploratory testing, test designs and execution are made as they go along, depending on the tester’s insights and intuition. Not Exclusive to Agile: Due to its compatibility with agile development’s iterative and flexible character, exploratory testing is frequently linked to agile approaches. Exploratory testing can, however, be applied to any approach to software development, including waterfall, hybrid, and DevOps. Complemented with Automation: Although exploratory testing is a manual testing method, it can be supplemented by automated testing software and scripts to increase effectiveness and coverage. Regression testing is a repetitive or time-consuming process that automated tools can assist with, whereas exploratory testing can concentrate on areas that call for human insight and creativity. Conclusion: Exploratory testing is a tester’s best friend since it fosters innovation and creativity, boosts productivity, enhances test coverage, offers quick feedback, and assists in coordinating testing with user demands. These advantages can assist testers in ensuring the software product’s quality and adding value to their team and organization.

Can AI be the Key to Driving AVOD’s Success?

In the ever-evolving world of online video viewing, subscription-based streaming has long been the dominant force. However, a new player is emerging and gaining momentum: Advertising-Based Video on Demand (AVOD). This model attracts both new and existing subscribers by offering a low-cost or even no-cost streaming experience, supported by advertisements. AVOD platforms provide a selection of programs that are accompanied by targeted advertisements, making them an appealing choice for a wide range of viewers. Unlike traditional platforms that have witnessed a progressive decline in popularity, AVOD platforms have the advantage of reaching a large and diverse audience. By carefully curating the advertisements shown and avoiding excessive repetition, content distributors can ensure minimal viewer distraction and effectively reduce churn. Considering the rising costs and inflation levels, this approach not only adds value to the customer’s experience but also offers a cost-effective means of generating a high return on investment (ROI).     According to Omidia, the future looks bright for AVOD streamers. AVOD is projected to surpass linear television and generate an estimated revenue of $259 billion by 2025. This growth further solidifies the appeal and potential of AVOD as a viable business model in the fast-expanding landscape of online video streaming. The rise of AVOD in recent years has the potential to outshine SVOD (subscription video on demand). Deloitte forecasts that by 2030, a majority of online video service subscriptions will be financially supported, either partially or entirely, by advertisements. The monetization through ads offers unparalleled profitability and fosters deeper engagement with the audience. Interestingly, a survey by TiVo revealed that customers are generally accepting of advertisements when it comes to accessing free content. Overall, the AVOD market is set to experience significant growth in the coming years, further bolstered by advancements in technology like Artificial intelligence (AI) which is poised to drive the expansion of the AVOD market. What impact does AI have on the AVOD market and business outcomes? AI-Powered Predictive Analysis for Business Expansion:  AI forecasting software enables complex analysis and facilitates business planning. It provides intelligent data that helps businesses enter new geographic regions and ensures the sustainability of their business models in the long run. Through predictive analysis, content providers can identify the appropriate target audience, understand their demand and potential for growth based on title, genre, and preferences. AI enables better decision-making by offering a clearer picture of audience segmentation and their landscape. Targeted Marketing for Improved ROI:  AI solutions can identify the right audience to target and provide customized suggestions based on their behavior – such as frequently watched genres and favorite titles. By offering intuitive recommendations and personalized marketing, AI enhances the customer experience based on preferences. AI-powered insights offer valuable data on customer habits, contributing to improved marketing strategies and, consequently, better business outcomes. Enhanced Content Monetization:  Well-designed and marketed freemium AVOD content has the potential to attract and retain subscribers across various age groups. AI software analyzes data and standardizes datasets to compare performance across different AVOD platforms. This allows for determining the optimal solution to deploy, the ideal content types, and the optimal display timing. Compared to traditional approaches, AI-powered platforms will have the ability to drive content monetization significantly. AI for Identifying and Retaining Potential Subscribers:  AI-based software can also identify users that are more likely to subscribe to the AVOD model. Subscribers who are already comfortable with AVOD platforms and their offerings are more inclined to choose a paid subscription for additional benefits. Conversely, instead of canceling an expensive subscription, customers are more likely to opt for a less expensive ad-supported tier and AI can seamlessly identify such customers and ensure long-term retention. Customer Data Analysis for Personalized Recommendations: AI further utilizes customer behavioral data based on varied touchpoints, including time spent watching a TV show, start and exit times, and advertising data. This substantially improves the viewer experience and increases the amount of time spent viewing suggested content by offering the best recommendations based on preferences and interested segments.   What is the future of the AVOD market with AI as the technology engine? The future of the AVOD market, powered by AI, holds immense potential, and promises to revolutionize the media industry. As viewers increasingly turn to online platforms for content consumption, the onus is on platforms of the future like AVOD to innovate and captivate their audience. AVOD models can greatly benefit customers by reducing subscription costs or even offering free services. By leveraging AI, businesses can reshape existing content, optimize the impact of advertisements, gauge customer response, and enhance the overall viewing experience. This seamless transition to AVOD, driven by AI, has the power to disrupt the market and usher in a new era within the media industry. The stage is set for AI to play a transformative role, and the future holds exciting possibilities, as video on-demand technology continues to evolve. Reach out to us at [email protected] or visit here to learn more.

From Paperwork to Powerhouse: Technology’s Impact on Service Contracts

In today’s manufacturing industry, service contracts are utilized to provide additional coverage and maintenance services for equipment and vehicles beyond the standard manufacturer’s warranty. Customers can acquire these contracts (sometimes known as extended warranties or service agreements) to protect themselves against unexpected repair costs and assure continuing maintenance.     A Closer Look at Service Contracts  Extended warranty agreements typically provide coverage for the repair or replacement of specific components or systems that may experience failure or malfunction due to normal wear and tear. This coverage extends beyond the standard manufacturer’s warranty, which is often limited in duration or mileage. The items that can be covered include the engine, transmission, electrical systems, suspension, and other vital components. The specific terms and conditions of the service contract will vary depending on the provider and the level of coverage selected. Another type of service contract may include routine maintenance services, such as oil changes, filter replacements, and other recommended services. In some cases, service contracts can combine extended warranty coverage with scheduled maintenance services, providing a comprehensive package that includes both warranty protection and routine maintenance. Technology to the Rescue For smaller companies with a few machines, warranty management is a manageable task. For larger companies, however, managing hundreds or thousands of concurrent contracts requires an extraordinary amount of administration. So how do warranty service providers ensure they can meet their warranty contracts with no loss in service quality and reduced paperwork? The answer is technology. Today, companies are working with technology partners to create application platforms that provide warranty management services that offer extended capabilities, enabling employees, dealers, and partners to manage warranty, service contracts, and other aftersales processes with ease. Unleash the Hidden Potential – Accelerate Impact The COVID-19 global health crisis severely affected manufacturing, causing supply chain disruptions and presenting significant challenges to OEMs and third parties offering extended warranty agreements. Today’s advances in technology are improving the end-to-end service lifecycle to help OEMs save money and free up resources through data management, automation, and predictive analytics. Let’s look at some of the ways this is happening: Enhanced Efficiency: Contract management software and automation tools streamline contract creation, tracking, and management. This in turn reduces manual effort, minimizes errors, and speeds up the entire contract lifecycle management. Businesses can then respond rapidly to customer expectations and industry demands and establish new service contracts quickly and efficiently. Improved Customer Experience: Advances in online portals, self-service options, and digital communication channels apps have tremendously enhanced the end customer experience. When these tools are integrated with backend technology platforms, customers can easily access their contract information. They can also request services and receive timely updates. As a result, customer satisfaction levels are enhanced, and engagement levels increase. Real-Time Monitoring and Reporting: Sensors, IoT devices, and connectivity allow for remote monitoring of equipment or vehicles, capturing data on usage, performance, and maintenance needs. This data can impact service contracts by enabling proactive issue identification, predictive maintenance, SLA compliance, data-driven contract optimization, upselling/cross-selling opportunities, and an enhanced customer experience. Predictive Maintenance: Machine learning and data analytics are facilitating predictive maintenance in service contracts to a great degree. By analyzing historical data and performance patterns, algorithms can foresee when equipment or components have higher failure probabilities. This enables service providers to offer maintenance proactively, minimize downtime, and optimize repair schedules. Contract Analytics and Optimization: The analysis of service contract data can help service providers identify trends, patterns, and areas for improvement. Analytical tools can yield insights into contract profitability, utilization rates, customer preferences, and performance metrics. This can result in optimized contract terms, pricing, and service offerings. Streamlined Billing and Payments: A large part of the paperwork involved in service contracts has gone digital. Automated technology, such as billing systems, can quickly generate accurate invoices based on contract terms and usage data. The entire process can be streamlined and convenient when integrated with online payment platforms and digital wallets.   Value-driven Features: Revolutionizing Service Contracts  Contract management software and automation tools offer various features that help service providers streamline and enhance the entire after sales process. Let’s examine some specific technical features that can contribute to creating a unified experience. Contract Repository: A central repository for storing and organizing contract documents, allowing easy access, version control, and document search capabilities. Contract Creation and Authoring: Tools that facilitate the creation and authoring of contracts using customizable templates, standardized clauses, and pre-approved language. Administrators should be able to set up various types of contracts which apply to different types of products and models with ease. Pricing should be factored in so that contracts can be configured based on pre-defined customer preferences and priced automatically. Contract Tracking and Alerts: The ability to track contract milestones, key dates, and obligations. Automated alerts and notifications can be set up to remind stakeholders about upcoming renewals, expirations, or important tasks. Workflow and Approvals: Tools that enable the definition and automation of contract approval workflows and promote self-service (for both sales and customers) while also ensuring that the appropriate stakeholders review and sign off on contracts within defined timelines. Contract Negotiation and Collaboration: Features that facilitate real-time collaboration among multiple stakeholders during contract negotiations. These often include intuitive guides that enable users to configure, quote, and purchase a contract. Features can also include version control, document sharing, commenting, and redlining capabilities. Electronic Signature: Integration with electronic signature platforms allows for the digital signing of contracts, eliminating the need for physical signatures and enabling faster turnaround times. Contract Performance Tracking: Service providers can ensure compliance and proactive management of contract obligations by tracking and monitoring contract performance against defined metrics, including key performance indicators (KPIs) and service level agreements (SLAs). Integration with Other Systems: The ability to integrate with other business systems such as CRM, ERP, or billing systems, enabling seamless data exchange and eliminating manual data entry. Security and Compliance: Critical data security features, including user access controls, data encryption, and compliance with data protection regulations like GDPR or CCPA, to ensure confidentiality and integrity of contract data.   Innovation and the Future

Precision Agriculture: Technology to Improve Farming in Digital Era (Part 2)

The evolution of precision agriculture technology You may have heard of precise agriculture, but do you understand what it entails and how it transforms modern agriculture? Precise agriculture, also known as precision agriculture or precision farming, is an innovative approach to farming that utilizes advanced technology to optimize agricultural production. It collects and analyzes data to make smarter, more efficient, and environmentally friendly decisions in managing crops and livestock. In the past, farmers had to make decisions about planting, cultivating, and harvesting crops based on their intuition and experience. However, with the advent of precise agriculture, farmers can now make well-informed decisions based on data analysis, leading to better crop yields, reduced environmental impact, and increased profitability. Precise agriculture is a game-changer for sustainable farming practices, enabling farmers to use resources efficiently while minimizing their environmental impact. By embracing precise agriculture, farmers can contribute to global efforts to combat climate change, reduce the use of harmful chemicals, and promote biodiversity.   Continuing the previous blog (Precision Agriculture: Technology to Improve Farming in Digital Era), we now dive into the applications of Precision Agriculture and how it continues to revolutionize the Agriculture Industry. Applications of Precision Agriculture: 1. Micro Irrigation: Micro-irrigation systems allow growers to effectively plan irrigation by identifying areas with high and low soil moisture. Precision agriculture irrigation makes it possible to carry out variable rate irrigation to vary the water supply volume for different field parts. This level of control can significantly improve irrigation efficiency and result in significant water savings. One of the indexes used in Crop Monitoring is NDMI. The index shows the crop water stress level in the selected field. Growers can now quickly identify areas of the field that need additional watering, regions of flooding, or areas with excessive moisture. 2. Site-Specific Crop Management (SSCM): SCCM relies on observing, measuring, and responding to inter or intra-field crop variability. It is a modern farming technique used to make production more efficient. SCCM is a form of precision agriculture where decisions on resource application and agronomic practices closely match crop requirements as they vary within a farm or field. SSCM consists of five fundamental components: Spatial referencing Measurement and monitoring of crop, soil, and environmental attributes Attribute mapping Decision Support System (DDS) Differential Action   In SSCM, growers take large fields and then divide them into small patches so that no misapplication of products occurs. Growers who use SSCM practices use weather data, humidity, soil temperature, growth, and other factors for crop rotation. They also manage the irrigation rates so no salts accumulate on the soil surface. Some growers employ cutting-edge technology like GPS, computer-controlled tractors, and harvesters. They also use modern practices such as aerial imagery, soil sample collection, soil type, potential yield, and more to divide huge fields into tiny units to reduce waste and boost production. Sensors are also installed throughout the field to detect the slightest changes in the plant or soil. Upon noticing these changes, sensors relay the information to the centers. Centers collect data from farms and fields, process it in real time, and assist growers in making decisions about planting, fertilizing, watering, and harvesting. The sensors detect changes, and the irrigation system operates to deliver the exact amount of water required to the location where it is needed. Growers can increase production while simultaneously conserving soil by using SSCM methods. It ensures food security by enabling us to produce larger yields from the same field. 3. Soil Mapping in Precision Agriculture: Precision Agriculture is only possible with quality ground mapping. With its help, growers evaluate the soil properties, its chemical composition, the presence of the nutrient, and more. Soil mapping practice has existed for a long time, but modern technologies provide even more detailed information, making the new generation of digital maps more efficient. For obtaining data, growers use several types of precision agriculture sensors: Optical sensors that interpret data based on the coefficient of light reflection from the ground Electrochemical sensors that analyze the soil’s electrical characteristics, such as the potassium’s presence Mechanical sensors in contact with the earth determine the types and density of the elements contained in it.   4. Internet of Things (IoT) in Precision Agriculture: The Internet of Things is considered a paradigm shift in the advancement of the smart agriculture field that has enabled the development of smart wearables and connected devices, as well as automated machines and driverless vehicles on fields. IoT has enormous potential in the agriculture industry. Sensors on equipment and materials enable the Internet of Things to simplify and streamline agricultural resource collection, inspection, and distribution. When combined with image recognition technology, field sensors allow growers to monitor their crops from any location. Real-time information is sent to growers by these sensors, allowing them to make crop adjustments accordingly. This system has given growers more control over the field, with dedicated data sensors, remote control, and an IoT platform. With IoT-based precision agriculture, growers can control all the critical information: from air temperature to soil conditions. As a result, growers benefit from IoT sensors deployed in the field, which results in higher food production with less waste-which is the need of every industry today. Moreover, technology solves the problem of manually researching large farms and fields by collecting data independently. The introduction of robotics in agriculture is the shifting norm. Agricultural robotics helps improve productivity, resulting in higher and faster yields. Spraying and weeding robots are helping reduce agrochemical use. Experimentation with laser and camera guidance for weed identification and removal without human intervention has also begun. These robots can use this information as guidance to move between rows of crops independently, so fewer people are needed behind the wheel. 5. Artificial Intelligence and Machine Learning: AI in precision agriculture has redefined farming. It has introduced new intelligent tools for managing agricultural production. AI has been utilized in predictive analytics, allowing growers to make better decisions. The essential concept of AI in agriculture is flexibility, rapid performance, accuracy, and cost viability. Artificial

Transforming Service Quality Management for Automotive Suppliers Driving Efficiency and Revenue Growth

Amidst the fiercely competitive automotive supplier landscape, organizations relentlessly endeavor to enhance their service quality management processes. However, tier 1 suppliers often face challenges in their current Service Quality Management practices. This blog explores the pain points faced by tier 1 suppliers and highlights the importance of adopting a specialized Service Quality Management application to overcome these challenges, revolutionize processes, reduce costs, drive revenue growth, and strengthen partnerships with automotive manufacturers.   Streamlining Quality Processes and Reducing Operational Costs: Automotive suppliers face considerable challenges with manual and fragmented data collection processes. These practices lead to inefficiencies, errors, and delays in obtaining critical quality-related information. By leveraging a Quality Management application equipped with OCR capabilities, suppliers can automate data consolidation across the automotive supply chain, eliminating manual data entry and reducing errors. With seamless data integration from various sources through robust APIs, suppliers must use technology to optimize workflows, enhance data accuracy, and ultimately reduce operational costs. Robust 8D Corrective Actions Process: Lengthy and ineffective corrective action processes often lack agility and fail to respond quickly to quality issues, resulting in production disruptions, delays in problem resolution, and increased costs associated with recalls and defects. To address this pain point, suppliers must embrace a specialized Quality Management application for a robust 8D corrective actions process supported by AI/ML technology. By leveraging advanced analytics and machine learning, suppliers can become more efficient in identifying quality issues, performing root cause analysis, and implementing effective corrective actions. This leads to minimizing disruptions, recalls, defects, validating warranty claims, and associated costs, resulting in significant savings and improved product quality. AI/ML-Driven Analytics and Timely Alerts: Another disadvantage for suppliers is a lack of visibility into quality indicators and trends. This lack of predictive insights makes proactive decision-making and identifying emergent quality issues difficult, resulting in wasted improvement and revenue development opportunities. Suppliers must leverage AI/ML-driven analytics and timely notifications to overcome this challenge. Automotive suppliers must capitalize on growing possibilities and maximize their income streams by employing robust predictive analytics capabilities that provide deep insights and projections of market demands and potential obstacles. Furthermore, automated notifications based on specified criteria or quality trends ensure prompt actions, increasing customer satisfaction and revenue potential. Specialized Features for Service Campaigns: Effective management of service campaigns is critical for suppliers to maintain brand reputation, minimize customer impact, and build stronger partnerships with automotive manufacturers. However, challenges in campaign planning, resource coordination, and progress monitoring can hinder the success of these initiatives. Hence, suppliers must excel in their preparation and execution of service campaigns. Wrapping up For tier 1 automotive suppliers, embracing a specialized Service Quality Management application tailored to their unique needs is crucial for driving efficiency, reducing costs, and fueling revenue growth. Industry statistics demonstrate that technology-driven solutions can significantly enhance quality management processes. By addressing the pain points of manual data collection, ineffective corrective actions processes, limited visibility, and service campaign management challenges, suppliers can leverage the benefits of OCR capabilities, a robust 8D corrective actions process, AI/ML-driven analytics and alerts, and specialized features for service campaigns and warranty claims. These improvements enable them to optimize workflows, reduce operational costs, minimize defects, and strengthen partnerships with automotive manufacturers. What’s next? How can you revolutionize your quality management processes, reduce costs, drive revenue growth, and forge stronger partnerships? Look no further; contact us today to learn more about how Tavant’s specialized Service Quality Management application for Tier 1 automotive suppliers can empower your organization and position you for sustained success in the competitive automotive industry.

The Buy Now Pay Later Frenzy: Let’s Decode

The Steady Ascent of BNPL Usage and Acceptance The rise of the buy now, pay later (BNPL) concept is transforming the digital world and revolutionizing how we shop online. This innovative payment method has disrupted the traditional credit system and has given consumers greater flexibility and control over their spending habits. According to a recent study by The Ascent, 60% of US consumers have used Buy Now, Pay Later services at least once, and 30% have used them within the past year. The same study found that the US’s most popular BNPLs are PayPal Credit, Afterpay, and Klarna. Additionally, the global BNPL market is expected to reach $4.7 billion by 2025, with a compound annual growth rate of 9.4%. With Buy Now, Pay Later, consumers can make purchases without paying the full amount upfront. Instead, they can spread the cost over several installments, often interest-free. This approach has proven popular with shoppers needing more means to pay for an item outright or prefer to manage their finances more efficiently.     BNPL, as we know it today, began to take shape in the early 2000s when several companies started offering installment plans for online purchases. These plans allowed customers to break up their payments into smaller installments, making it easier to afford expensive items. However, these plans were less flexible than modern BNPL plans and often came with high interest rates and fees. New BNPL businesses started to appear in the late 2000s and early 2010s, offering more flexible payment schedules with no interest or fees. These companies used technology to make applying for and using BNPL easier and more convenient. They also partnered with retailers to offer BNPL as a payment option at checkout, making it a popular choice for online shoppers. BNPL has become a popular alternative to traditional credit cards and installment plans. It has expanded beyond online shopping and is now offered in physical stores, and some companies are even partnering with banks to provide BNPL as a feature on their credit cards. The popularity of BNPL is expected to grow as more people seek affordable and flexible ways to pay for their purchases. “BNPL services offer a financial safety net, allowing you to have your cake and eat it too.” BNPL services can be a lifesaver for those who need to make a purchase but don’t have the cash on hand, providing them with a financial safety net to fall back on. One of the significant benefits of BNPL is the convenience it offers. Consumers can purchase items on a whim without having to worry about the immediate financial impact. This has increased sales for merchants and allowed them to reach a wider audience. There are several benefits of using Buy Now Pay Later (BNPL) services for consumers: Flexibility: BNPL services allow customers to spread the cost of their purchases over time, making it easier to afford expensive items. No interest or fees: Many BNPL services offer zero-interest and no-fee options for customers, making it a cost-effective alternative to credit cards. Quick and easy: BNPL services are often integrated into online shopping platforms, making it easy to apply and use at checkout. Improved credit score: Customers that use BNPL services and make on-time payments while using credit responsibly can raise their credit scores. Transparency: BNPL services provide customers with clear information about the terms and conditions of their payment plan, making it easier to understand and manage their finances. No impact on credit score: Unlike applying for a credit card or a loan, BNPL does not require a hard credit check, which means it does not affect the customer’s credit score. Wrapping up In conclusion, Buy Now, Pay Later (BNPL) services have exploded in popularity in recent years, providing consumers with a convenient and flexible way to make purchases without the need for upfront payments. While BNPL can be an excellent tool for budget-conscious shoppers, it is essential to understand the potential risks involved. Late fines, interest charges, and an impact on your credit score might result from non-payment. As with any financial product, it is crucial to research your options and use BNPL responsibly. Ultimately, BNPL can be a valid payment option for those who are able to manage it carefully. Still, it is not a solution for those struggling with debt or financial instability. What’s Next? How Is BNPL exploding market, and what does it mean for the Future of credit cards? Hemanthkumar Jambulingam, Senior Director of Product Management, Tavant, will be joining this power panel at #FinovateSpring on May 25!

Lending 2.0: How Digital Transformation is Reshaping the Financial Landscape

Digital transformation has brought significant changes to the lending landscape, providing borrowers with easier access to credit, faster loan approvals, and lower costs. Moreover, the pandemic has accelerated the adoption of digital lending. In response to the epidemic, several lenders digitized face-to-face operations, such as mortgage applications, e-verification of income and assets, drive-by and automated appraisals, and hybrid closings. This reduced expenses, increased margins, and illustrated that lenders are incentivized to respond to changing customers’ needs. It’s also a wise approach, given that customer demand for digital mortgage experiences has skyrocketed since the pandemic. According to a survey by the National Bureau of Economic Research, there was a 6% increase in the use of online lenders in the US during the pandemic. This surge in online lending was likely due to several factors, including the closure of traditional lending firms and the increased need for access to credit because of the economic downturn caused by the pandemic. However, some challenges still need to be addressed to ensure that everyone benefits from these advances. Digital Disconnects in Lending • The average loan processing time remains two months. According to a survey by McKinsey & Company, borrowers are willing to pay higher interest rates for faster loan processing times. They want faster, more convenient service, transparency, control, and prompt information. Full-scale digital transformation is non-negotiable in the face of competitive pressure to operate profitably in a crowded marketplace and technically competent non-bank lenders. For most, the next stage is to rebuild the back office and focus on removing the biggest impediments to growth. • Siloed Working The legacy infrastructure underpins the newly digitalized customer-facing processes, and the systems and technology that drive mid and back-office functions need to integrate better with the solutions used. This misalignment between modernized customer-facing operations and largely manual, human-driven mid- and back-office processes can lead to inefficiencies and delays. Errors in manual back-office procedures cause multiday delays that slow down the entire origination process. • Poor CX Consumers are increasingly prioritizing convenience over price, and this tendency is already infecting the mortgage business. Positive word-of-mouth recommendations concerning service standards are almost as crucial to borrowers as low rates when selecting a loan. Sometimes, borrowers will penalize lenders for irregular contact, even if the loan is closed on time. To meet borrowers’ expectations across the customer journey, mortgage lenders must smooth out any flaws in the loan origination process and change to a customer-centric strategy. A Paradigm Shift in Lending The digital age has resulted in a fundamental shift in how financial services are provided and consumed. The transition from traditional lending to digital involves implementing digital technologies to automate lending processes, reduce costs, and improve customer experiences. This paradigm shift has brought about several critical changes, one of the most important being the democratization of lending. Borrowers now have access to more lender options than ever before, thanks to the proliferation of online lending platforms. These platforms include crowdfunding sites and peer-to-peer lending websites. This has resulted in cheaper interest rates and costs for borrowers due to increased competition in the lending industry. The application of digital technologies to simplify and expedite the loan process is another critical shift that has taken place. Automating the underwriting and credit scoring processes on online lending platforms through algorithms and machine learning has led to reduced expenses and a speedier approval process for loans. Borrowers can now evaluate the interest rates and terms offered by numerous lenders before making a choice, which has also contributed to improved openness in the lending industry. Artificial intelligence (AI) is being used to automate underwriting and credit scoring, resulting in faster loan approvals and reduced costs. According to a Boston Consulting Group analysis, AI-powered underwriting and credit assessment might result in up to 10% lower default rates and up to 40% reduced underwriting expenses. The lending industry is highly competitive, and businesses that don’t embrace digital transformation risk losing their competitive edge. Unlocking the Power of Digital Transformation: Revolutionizing the Way We Access Credit and Transforming the Future of Lending Faster Processing Time: Using digital technology in lending makes the process faster, and the turnaround time for loan approvals is shorter. This speed is a significant factor in customer satisfaction and retention. Better CX: Using digital technology enables lenders to provide better customer experiences. Digital lending allows for self-service options, giving customers greater control over their lending needs. Cost Reduction: Using digital technology in lending helps lenders save money on paper-based procedures like printing, scanning, and storing. Accurate Risk Assessment: Digital transformation in lending enables lenders to conduct more precise risk assessments using data analytics and machine learning algorithms. This improves the accuracy of lending decisions, reduces the risk of default, and helps lenders maintain a healthy loan portfolio. Increased Accessibility: Digital lending makes credit more accessible to underserved and unbanked communities. Using digital technology, lenders can reach out to these communities and provide them with the capital they require to expand their businesses or meet their financial objectives. Adapting to a New Landscape Digital transformation in lending has its challenges. Here are some of the major difficulties lenders face when implementing digital transformation that can be easily overcome. Data Security Concerns: Digital lending transformation involves using sensitive customer data. Lenders need to take extra precautions to ensure the security of this data. Integration with Legacy Systems: Many lenders have legacy systems that must be compatible with modern digital technologies. Integrating these systems with new digital platforms can be challenging but not daunting enough. Regulatory Compliance: Lenders must comply with the regulatory frameworks when implementing digital transformation initiatives. Compliance requirements can be complex and time-consuming but easily achievable. The Road Ahead for Lending Organizations Digital lending is an evolving space and provides a tremendous opportunity for fintechs to make further inroads. Due to the use of digital technology, the lending business has seen a significant upheaval in recent years. We find new age fintech players to be primarily focused on personal loans, including Buy Now, Pay Later (BNPL) business loans and supply

Lending in the Age of Intelligent Automation: Leveraging AI to Enhance CX

The lending industry has always been data-driven, and lenders have been relying on analytics and technology to make informed decisions. With the advent of intelligent automation and AI, lenders can now leverage the power of machine learning algorithms to streamline their operations, reduce costs, and improve customer experience.   The Growing Importance of AI and automation in Fintech The importance of AI in fintech cannot be overstated. According to a report by CB Insights, AI in fintech has grown from $1.2 billion in funding in 2014 to $4.7 billion in 2019. This growth is expected to continue, with AI in fintech projected to reach $22.6 billion in funding by 2025. This shows the tremendous potential of AI in fintech and how it is becoming an essential tool for fintech companies Enter the Trojan Horse: AI can help unstick the stagnation in financial services innovation. One of the key areas where lenders are adopting intelligent automation is in the loan origination process. Using AI-powered underwriting tools, lenders can process loan applications much faster and more accurately than ever. These tools analyze borrowers’ creditworthiness, income, and other relevant data to determine their ability to repay the loan. They can also identify potential fraud or credit risks that may have gone unnoticed. Another area where intelligent automation is making a significant impact is loan servicing. Lenders now use chatbots and virtual assistants to provide quick and efficient customer support. These bots can answer customer queries, provide payment reminders, and offer personalized financial advice based on the borrower’s financial profile. AI is also being used to detect and prevent fraud in the lending industry. By analyzing vast amounts of data, AI algorithms can identify patterns and anomalies that may indicate fraudulent activity. This can help lenders prevent losses and protect their customers from fraudsters. From Traditional to Digital: Why Lenders Should Embrace Intelligent Automation The adoption of intelligent automation and AI is transforming the lending industry, enabling lenders to streamline their operations, cut costs, and enhance the customer experience. As these technologies continue to evolve, we can expect to see even more innovation in the lending space in the coming years. The Fintech industry is rapidly evolving, and many lenders are now adopting digital finance to keep up with the pace. AI and Machine Learning (ML) offer significant advantages over traditional statistical models, especially when it comes to scalability and cost reduction to support growth. By using AI/ML models, lenders can reduce the need for manual intervention to adjust to changes and outliers in data. This leads to increased efficiency and performance, as well as improved transparency. For example, AI can comprehend mortgage application information more precisely and quickly than optical character recognition (OCR) technology. One of the key benefits of AI in the mortgage industry is that it eliminates human errors and improves accuracy through machine learning. This saves time and resources while ensuring that calculations and judgments are error-free. Furthermore, AI-powered chatbots can help lenders quickly answer borrowers’ questions and guide them through the loan application process, leading to an improved customer experience (CX). Intelligent automation also plays a significant role in improving efficiency in the Fintech industry. For example, AI can produce expenditure reports faster and with fewer errors than humans. It can assist workers in tracking and automating tasks like compliance, data entry, fraud, and security. Finally, AI-powered customer service interfaces like chatbots and virtual assistants are becoming increasingly popular, as they can engage with clients on a constant basis and cut front-office and help-line costs. The Road Ahead Tavant can help mortgage lenders diversify businesses and unlock savings by leveraging next-generation digital technologies. RPA and Intelligent Automation in Mortgage Lending Businesses must now respond swiftly to market shifts and client expectations. Using our deep automation and domain expertise, Tavant’s consulting-driven approach to automation enables mortgage lenders and banks to considerably boost efficiency and enhance client experiences. We deliver organization-wide transformation through RPA, ML, and AI by harnessing the power of industry tools and accelerators to tackle your most essential business concerns. For more information, please get in touch with us at [email protected] or visit our website. FAQs – Tavant Solutions How does Tavant use intelligent automation to enhance customer experience in lending?Tavant deploys AI-powered automation for personalized loan recommendations, instant approvals, proactive customer service, and predictive analytics that anticipate customer needs, creating superior lending experiences. What AI-enhanced customer experience features does Tavant provide?Tavant offers conversational AI interfaces, predictive customer service, automated loan monitoring, personalized financial advice, and intelligent workflow optimization that continuously improves the customer journey. How does AI improve customer experience in lending?AI improves lending CX through instant responses, personalized product recommendations, predictive service, automated problem resolution, 24/7 availability, and continuous learning from customer interactions to enhance service quality. What is intelligent automation in financial services?Intelligent automation combines AI, machine learning, and robotic process automation to create self-improving systems that can handle complex tasks, make decisions, and adapt to changing conditions while enhancing customer interactions. Can AI provide better customer service than humans?AI excels at instant responses, consistency, and handling routine inquiries, while humans provide empathy, complex problem-solving, and relationship building. The best customer experience combines both AI efficiency and human touch.

Precision Agriculture: Technology to Improve Farming in Digital Era (Part 1 of 2)

Precision agriculture is a farming methodology that analyzes temporal and geographical variability to increase agricultural production sustainability. Precision agriculture employs cutting-edge technology such as satellite images and field mapping to aid in yield optimization, crop management, and crop quality and profitability. Precision agriculture differs from conventional agriculture in that it manages fields by watching, measuring, and reacting to inter and intra-field variability in crops rather than as a unified block. The goal is to define a decision support system for whole farm management with the intent to optimize returns while conserving resources, thus contributing to the development of sustainable agriculture, allowing it to solve both economic and ecological problems while ensuring profitability and environmental protection. Importance Of Precision Agriculture Precision Agriculture enables farmers to make better use of crop inputs such as fertilizers, herbicides, tillage, and irrigation water. It greatly enhances crop efficiency and reduces financial costs while increasing output. Growers usually are aware that their fields have variable yields across their landscape. These variations can be traced to farm management practices, soil properties and environmental characteristics. Soil characteristics that affect yields include texture, structure, moisture, nutrient status, organic matter, and landscape position. However, Environmental factors include weather, insects, weeds, and diseases. For a grower, it was difficult to treat the site specifically based on land variability and soil characteristics. Without technology and information, growers couldn’t easily implement strategies to enhance their production. However, with the advancement of precision agriculture and technology, growers can now make strategic decisions based on the information available, allowing them to maximize crop yield, reduce production-related expenses, and continue to be good stewards of environmental resources. As a result, Precision Agriculture can automate and simplify data gathering and processing. It advises growers, allowing them to make management decisions quickly and efficiently, and to implement them in small areas within large fields. Precision Agricultural Technologies and Methods: Precision Agriculture’s various technological features make use of real-time data and software analytics, as well as hardware and software comprised of ground, aerial, and satellite equipments. 1. Variable Rate Technology (VRT): This technology enables growers to apply fertilizer, pesticides, seeds, and other farm inputs at various rates over a field based on their needs, without having to manually change rate settings on equipment or make several passes over an area. VRT is used to address spatial variability between paddocks or zones. VRT is classified into two categories.: Map-based: a map of application rates is produced for the field prior to the farm operation. Real-time control: decisions about what rates to apply in different locations are made using information gathered during farm operation. This requires sensors to detect necessary information ‘on-the-go’ and is usually designed for a specific job such as herbicide application in fields. 2. Digital Mapping Technology: The maps are used to capture the geographical and topographical features of a field in the form of virtual images. GPS and satellite remote sensing equipment are used for this, which creates maps that display all the field nuances and harvest states. 3. Weather Modeling: In this, weather sensors are used to gather detailed information on local climate factors, which in turn, model the probability of future disease and pest development on any field. 4. Guidance Technology: This utilizes a satellite-based positioning system to help automatically guide agricultural machines and equipment. 5. Drone Technology: Drone technology is used to take aerial images and videography of fields. Watch this space for Part two of this blog on Applications of Precision Agriculture.

Top Five Trends in Software Testing

The rapid changes and multiple ups and downs in software application development necessitate that development teams and quality engineers aim to improve their skills continuously. Every organization today strives to get its apps to market as soon as feasible. Organizations are embracing best practices such as Agile + DevOps + QAOps to minimize time to market and are also investing in technologies such as Machine Learning (ML) and Artificial Intelligence (AI). Software testing is an essential component of the SDLC and is critical to delivering high-quality products. Furthermore, the Internet of Things (IoT) is becoming increasingly popular in various industries, resulting in high demand for testing solutions and automation.   Let us look at the top five software testing trends that we believe will dominate in the future: 1. Continuous testing with test automation Every software development company aims to offer the finest quality software in a fast-paced Agile development environment. To do so, they must ensure their product is bug-free. There is no denying that problems can arise at any step of the software development life cycle (SDLC). As a result, test automation is essential for releasing products faster by shortening the test execution cycle, increasing efficiency, and finding regression errors early. Consequently, every firm recognizes test automation as a critical software testing life cycle component. Although the trend of DevOps with CI/CD began long ago, it was undoubtedly accelerated by the COVID-19 pandemic, which forced everyone to work from home. Continuous testing, which means testing at every stage of the SDLC with test automation, is an essential component of CI/CD pipelines that deliver high-quality software quickly to market. As a result, adopting this practice can assist organizations in providing their highest quality product well ahead of schedule. 2. IoT Testing and Automation Due to the confluence of digital and physical worlds, IoT is growing more intelligent by the day, and it is increasingly being employed in industries such as automotive, healthcare, energy, and utilities, etc. As the number of IoT-enabled devices grows, an effective testing strategy and test automation are required. When you focus on automating their microservices, the complexity of testing a massive IoT architecture decreases dramatically. It enables test automation to be completed quickly and with less risk. We should learn about these advanced technologies and improve our abilities to test their functionality, performance, and security. The Internet of Things testing market was valued at approximately US$ 1.56 billion in 2021, with total revenue expected to grow at around 29.6% from 2022 to 2029, reaching nearly US$ 12.48 billion. 3. LC/NC Test Automation Low-code/No-code test automation solutions combine Machine Learning, visual modeling, and Artificial Intelligence processes to produce stable results, allowing users to automate tests with little or no coding skill set/experience. Typically, the most used features/utilities are already built-in via GUI, allowing users to select and sequence the required actions.  This eliminates the complexity of manually performing the test cases while also speeding up the whole process by shortening the time spent conducting the regression test suite. Here are some of the benefits of low-code/no-code automated testing: Low learning curve – While technical experience is advantageous, it is not required. Most capabilities, such as remotely executing test cases, integration with test management tools, CI/CD are available as ready-made solutions. Since the test scripts are created without any code or low code, they benefit non-programmers such as product owners, business analysts, etc. 4. Using QAOps to Shorten Delivery Cycles To create a highly effective and cohesive process, the QA team, development, and IT operations teams must work together closely. In contrast to DevOps, QAOps focuses on the problems of QA engineers and the importance of integrating software testing into the DevOps workflow. QAOps is essential for groups that automate their CI/CD pipelines as it enables them to obtain quick results without compromising quality. After its integration into the CI/CD pipeline, this process helps teams save both time and money on product evaluation. The increasing popularity of QAOps illustrates that quality is often overlooked during software creation. Most businesses are embracing it to reap the following benefits: Because the QAOps process adheres to the shift-left testing approach, it accelerates issue fixes early without sacrificing time and allows the application to be deployed sooner. CI/CD testing identifies issues at an earlier stage, providing a reliable application with the highest quality. Because testing is ongoing, the chances of an improved customer experience increase as application quality and delivery improve. By running QAOps operations continuously, the IT operation team avoids any delays. The QA team can now test new apps/features without slowing down. This adoption has gathered a great deal of attention in recent years, and this interest will only increase in the coming years. 5. Accessibility Testing According to WHO, nearly three-quarters of the world’s population will access the internet solely through smartphones by 2025, and over 1 billion people, or 20% of the population, are likely to have some form of disability. In this age of digital transformation, mobile and web applications must be easily accessible to differently-abled people. As a result, accessibility is no longer an afterthought but a requirement that every software development company employs accessibility testing. This type of testing validates application usability experiences. It ensures that the application is usable by children, the elderly, left-handed users, and people with various disabilities. Final Thoughts Exciting times are ahead for the testing industry as we discover new ways to optimize software testing using augmented intelligence. Based on the above software testing trends, we can foresee a positive future for quality engineering. What’s Next Tavant Continuous Quality Engineering Services help organizations engineer quality into their process by incorporating a whole gamut of services, tools, and techniques to elevate the end-user experience. To learn more, visit here or reach out to us at [email protected].

Go Touchless: The game-changer that Saves 60% of Time on Appraisal Report Analysis

Collateral Management in an Uncertain World When conducting a formal appraisal review, many tasks are repetitive and don’t require high expertise. Underwriters spend time manually locating and importing files from multiple sources, which increases costs and decreases productivity. Technology can automate the labor-intensive parts of the appraisal quality control workflow, and machine learning can identify potential risk areas that require deeper evaluation by the underwriter.     Collateral Automation – the Time is Now The process of manually reviewing appraisals involves two primary steps that are time-consuming. The first step involves gathering information, including importing files such as MLS photos and collecting loan details. Although these tasks are simple, they take a lot of time and only offer some additional benefits when done manually. The second step is the actual review of the appraisal, which can take upwards of two hours, depending on the length and complexity of the document. The average appraisal is over 30 pages long and contains hundreds of data points, dozens of photographs, and addenda. According to the 2022 Cost to Originate Survey by Freddie Mac, lenders incur a fully loaded hourly cost of $132 for personnel involved in processing and underwriting. This cost translates to an average of $99 per loan file for the appraisal quality control process. Therefore, it is crucial for lenders to consider the expenses incurred in 45 minutes. The Advanced Collateral Management in a Nutshell: Enhanced efficiency – Create and easily manage collateral and appraisal across numerous business lines. Increased compliance – Recommended practices for handling perfections, manual evaluations, renewals, and releases that can be configured. Improved opportunities – Maximize your understanding of current and future loan performance by automating collateral data capture. Simplified environment – All parties benefit from an intuitive view of interrelationships that coordinates and simplifies access. Streamlines the time-consuming process of manually analyzing assessments, allowing underwriters to concentrate on more important concerns. Better control while reducing risk – Proactive and efficient collateral capture and management in a single global deployment for enhanced risk control. The Future of Collateral Management   Collateral Analysis, the second new addition to Tavant’s Touchless Lending® platform, automates the time-consuming process of manually reviewing appraisals, freeing up underwriters to focus on other vital issues. This feature includes GSE and private investor guideline checks, validation of appraisal information, analysis of home images and appraiser comments using AI/ML techniques, and FEMA, USPS, and flood zone information authentication. Collateral Analysis also offers various features, such as validating appraisal information across the loan file, including title, sales contract, flood, homeowners’ insurance policies, etc. It uses various AI/ML techniques to automatically analyze home images and appraiser comments to identify and escalate issues. It offers authentication checks for FEMA, USPS, and Flood Zone. Touchless Lending Collateral Analysis leads to faster and more accurate decision-making and closing by eliminating manual work and identifying and addressing issues automatically. This tool benefits all mortgage businesses and exceeds the capabilities of current industry-standard appraisal review tools. It reduces the time it takes to analyze an appraisal report by 60%. It simplifies the time-consuming assessment review process. The technology automates the underwriters’ tedious job, allowing them to focus on other important matters that require quick responses. Touchless Collateral is intended to help every mortgage channel of business and goes much beyond the conventional appraisal review tools that are currently accessible. What’s Next? For more information on Touchless Lending® Collateral Analysis, click here or contact us at [email protected] to schedule a demo.  

Why Salesforce Manufacturing Cloud is a Game-Changer for the Industry

In today’s fast-paced manufacturing environment, manufacturing organizations are constantly searching for ways to streamline their operations and increase efficiency. One solution that has gained a lot of attention in recent years is Salesforce Manufacturing Cloud. Salesforce Manufacturing Cloud is a cloud-based solution that caters specifically to the needs of manufacturers, providing them with a unified platform for account planning and forecasting. This platform offers enhanced transparency and collaboration across the manufacturer’s entire ecosystem.     Here are some of the ways that Salesforce Manufacturing Cloud can help transform the manufacturing industry: Real-Time Visibility One of the biggest benefits of Salesforce Manufacturing Cloud is that it provides manufacturers with real-time visibility into their operations. This means that they can monitor every stage of the production process, from raw materials to finished goods, and identify any issues or bottlenecks before they become major problems. With real-time data, manufacturers can make more informed decisions, optimize their processes, and respond quickly to changing market conditions. Increased Efficiency Salesforce Manufacturing Cloud also helps manufacturers increase efficiency by automating many of the manual processes that are involved in production planning, scheduling, and inventory management. By automating these processes, manufacturers can reduce the risk of errors, improve accuracy, and free up their teams to focus on more value-added tasks. Improved Collaboration Another key benefit of Salesforce Manufacturing Cloud is that it enables manufacturers to collaborate more effectively with their teams, customers, and partners. The solution provides a centralized platform for sharing data, communicating with stakeholders, and tracking progress. This makes it easier for manufacturers to work together with their teams and partners to solve problems, make decisions, and achieve their goals. Better Customer Service Salesforce Manufacturing Cloud also helps manufacturers improve their customer service by providing them with a complete view of their customer’s needs and preferences. With this information, manufacturers can personalize their offerings, provide more accurate delivery schedules, and respond quickly to customer inquiries and issues. This can lead to higher customer satisfaction, repeat business, and referrals. Scalability Finally, Salesforce Manufacturing Cloud is designed to be scalable, which means that it can grow and adapt with your business. Whether you’re a small manufacturer just getting started or a large enterprise with complex operations, Salesforce Manufacturing Cloud can be customized to meet your specific needs and requirements. The Manufacturing Cloud is also vital to Salesforce’s Customer 360 suite, empowering manufacturers to provide intelligent field services, lifecycle marketing, channel management, B2B commerce, and other capabilities in a unified view, spanning their entire business. Salesforce Manufacturing Cloud is a game-changer for the manufacturing industry. It provides manufacturers with the real-time visibility, automation, collaboration, customer service, and scalability they need to succeed in today’s fast-paced market. If you’re a manufacturer looking to streamline your operations and increase efficiency, then Salesforce Manufacturing Cloud may be the solution you’ve been looking for. If you’re looking to implement Salesforce Manufacturing Cloud and need expert guidance and support, Tavant is an implementation and consulting partner that can help you on your journey. Tavant is a trusted partner of Salesforce, with years of experience helping manufacturers implement and optimize the Salesforce platform. Their team of experts is well-versed in the nuances of the manufacturing industry, and they can help you tailor the Salesforce Manufacturing Cloud solution to your specific needs and requirements. Tavant’s approach to implementation is thorough and collaborative, with a focus on understanding your business processes and aligning the solution to your goals. We can help you with every aspect of the implementation process, from project scoping and planning to data migration, configuration, and testing. In addition to implementation, Tavant also offers ongoing consulting and support services, helping you get the most out of your Salesforce Manufacturing Cloud investment. We can provide training for your teams, help you optimize your workflows, and provide insights and recommendations for improving your operations. Partnering with Tavant for your Salesforce Manufacturing Cloud journey will ensure a smooth and successful implementation and give you the tools and support you need to transform your manufacturing operations. For more information on how Tavant can help, email us at [email protected].

Personalizing the Financial Services Experience with Salesforce Financial Services Cloud

In today’s highly competitive financial services industry, providing a personalized client experience is crucial for building long-term relationships and driving growth. Salesforce Financial Services Cloud provides financial advisors with the tools to deliver a personalized experience that goes beyond the traditional client-advisor relationship. Here are some strategies for leveraging Salesforce Financial Services Cloud to provide a personalized client experience.   Create a 360-degree view of the client  Salesforce Financial Services Cloud allows advisors to consolidate all client data into a single, unified view. This includes contact information, financial account details, investment history, and communication history. By having a complete view of the client, advisors can better understand their needs and preferences and tailor their services accordingly. Utilize personalized client segmentation  Segmenting clients based on their investment goals, risk tolerance, and life stages allows advisors to deliver a more personalized experience. With Salesforce Financial Services Cloud, advisors can create custom client segments and tailor their outreach and service delivery based on those segments. For example, advisors can send personalized investment recommendations based on a client’s investment goals and risk tolerance. Leverage automation for proactive outreach  Salesforce Financial Services Cloud offers powerful automation capabilities that allow advisors to automate routine tasks, such as sending follow-up emails and scheduling meetings. With automated workflows, advisors can stay top-of-mind with clients and ensure that they’re providing timely and relevant advice. Use insights to provide personalized recommendations  Salesforce Financial Services Cloud includes powerful analytics tools that allow advisors to gain insights into client behavior and preferences. By analyzing client data, advisors can provide personalized recommendations that align with a client’s investment goals and risk tolerance. Provide a self-service portal  Salesforce Financial Services Cloud offers a self-service portal that allows clients to access their financial information, view investment performance, and update their personal information. By providing a self-service portal, advisors can empower clients to take control of their financial well-being and provide a personalized experience. Offer a personalized digital experience  Salesforce Financial Services Cloud enables advisors to provide a personalized digital experience through custom-branded web portals and mobile applications. With a branded digital experience, advisors can reinforce their brand and provide a seamless experience across all channels. FINANCIAL SERVICES CLOUD FEATURES & BENEFITS  Intelligent Referral Routing  Advisors must monitor leads and referrals diligently. FSC encompasses numerous referral elements designed to assist advisors and bankers in managing all their referral activities effectively. With AI-powered referral scoring and routing, you can build a smooth communication flow to track and convert referrals after a referral pipeline is developed. Stay on top of your client goals and referrals and get actionable insights about your book of business with visualized dashboards. Client Relationship Map  Utilizing the customer relationship map to bring customers’ networks to life, and showing much-needed context to Advisors. Explore the client relationship layers and related records with the Actionable Relationship Center (ARC) to organize all the information and create records for clients’ financial accounts and their underlying holdings, assets, liabilities, and financial goals. Combine Advisor Analytics, with Einstien AI predictions, and a 360-degree view of your customer to collaborate/communication easily. Financial Accounts & Rollups  FSC offers several kinds of financial accounts such as, Bank Accounts, Insurance Accounts, and Insurance Policies and their underlying holdings, assets, liabilities, and financial goals. This helps financial advisors in formulating investment plans that meet the financial objectives of their customers and get a complete insight into their clients’ accounts and assets to make the best product and service recommendations. Stay Compliant with Industry Regulation  Financial services is a highly regulated industry where the regulations are ever-changing. The financial services cloud helps customers to stay compliant. It comes up with the following complaint-related features. Compliant Data Sharing – Allow the complaint manager to config advanced data sharing rules to follow the compliance policy and regulations. Intelligent Document Automation for Consent and Disclosures – Allow you to manage consent and disclosure documents, generate authorization request forms, and track user responses. Deal Management – The deal team can manage deal-related information and take advantage of compliant, role-based data-sharing options. Advisor Analytics & BI  FSC helps financial advisors to provide their clients with better-informed decisions with the help of the analytics dashboard. Pre-built templates and models customized exclusively for the financial services industry produce instant insights at your fingertips. FSC is more accurate for making smarter financial decisions because the analytics are powered by innovative Artificial Intelligence technology. In conclusion, leveraging Salesforce Financial Services Cloud to provide a personalized client experience is critical for success in the financial services industry. By creating a 360-degree view of the client, utilizing personalized client segmentation, leveraging automation for proactive outreach, using insights to provide personalized recommendations, providing a self-service portal, and offering a personalized digital experience, advisors can deliver a customized experience that builds trust and loyalty. FAQs – Tavant Solutions How does Tavant integrate with Salesforce Financial Services Cloud to deliver personalized experiences?Tavant seamlessly integrates with Salesforce Financial Services Cloud to create unified customer profiles, enable personalized loan recommendations, and provide tailored communication strategies. Their integration leverages Salesforce’s CRM capabilities while adding specialized lending functionality for comprehensive, personalized financial services. What personalization capabilities does Tavant enable through Salesforce Financial Services Cloud integration?Tavant enables personalized product recommendations, customized communication preferences, tailored user interfaces, individualized pricing strategies, and adaptive customer journeys through their Salesforce integration. This creates highly relevant, engaging experiences that improve customer satisfaction and conversion rates. How does Salesforce Financial Services Cloud enable personalization?Salesforce Financial Services Cloud enables personalization through comprehensive customer data management, AI-powered insights, automated workflow customization, and integrated communication tools. It creates 360-degree customer views that support tailored experiences across all financial service touchpoints. What are the benefits of personalized financial services?Benefits include increased customer satisfaction, higher conversion rates, improved customer retention, enhanced cross-selling opportunities, better customer lifetime value, and competitive differentiation. Personalization helps financial institutions build stronger customer relationships and drive business growth. How can financial institutions implement effective personalization?Financial institutions can implement personalization through customer data integration, AI-powered analytics, behavioral tracking, segmentation strategies, and

Revving Up Your Product’s Life-Cycle: The Importance of Aftermarket Services and Service Life-Cycle Management

Product quality and longevity are crucial for customer satisfaction and the long-term success of any business. However, even the best products require maintenance, repairs, and replacement parts over their life-cycle to ensure continued functionality and customer satisfaction. Therefore, service life-cycle management and aftermarket services are critical for companies to offer. What is Service Life-cycle Management? Service life-cycle management (SLM) refers to managing a product or service from its inception to its retirement or disposal. It is a comprehensive approach that ensures customers receive the highest level of support throughout their product’s life, and the company can maximize the value of its products through effective maintenance, repairs, and upgrades. SLM comprises several steps: customer support, service request, service planning, service execution and field service, spare parts management, warranty management, service contract management, returns, repairs, and recalls. Each step is critical in managing a product’s life-cycle and customer satisfaction.   Benefits of SLM Effective SLM has many benefits for businesses, including improved customer satisfaction, increased revenue, and reduced costs. By offering quality aftermarket services, companies can create long-lasting customer relationships, enhance their reputation, and increase customer loyalty. In addition, providing aftermarket services can generate significant revenue for companies. Customers are more likely to buy from companies that offer comprehensive support and quality services, even if the products are more expensive than their competitors. Additionally, offering extended warranty programs, service contracts, and spare parts can help companies differentiate themselves and generate additional revenue streams. Finally, SLM can also help reduce costs by reducing product recalls and warranty claims. By providing quality services, companies can ensure their products last longer, which reduces the number of returns, repairs, and recalls needed, ultimately lowering costs. Aftermarket Services Aftermarket services refer to the services and products that companies offer after the initial sale of a product. These services include maintenance, repairs, upgrades, spare parts, warranty, and technical support. The aftermarket services industry is rapidly growing, with global revenues expected to reach $1.3 trillion by 2025. The growth is attributed to the increasing complexity of products, rising customer expectations, and the need for companies to differentiate themselves from their competitors. Benefits of Aftermarket Services Offering aftermarket services can provide many benefits to businesses, including:  Increased revenue: By offering aftermarket services, companies can generate additional revenue streams and create long-term customer relationships.  Improved customer loyalty: Providing quality services can help build customer trust and loyalty, leading to repeat business and positive reviews.  Enhanced reputation: Companies that offer comprehensive aftermarket services can establish themselves as industry leaders and strengthen their reputation.  Reduced costs: Providing quality services can help reduce the number of product returns, recalls, and warranty claims, ultimately lowering costs for the business. Service life-cycle management and aftermarket services are critical for businesses to ensure customer satisfaction and long-term success. By offering comprehensive support, companies can differentiate themselves from their competitors, generate additional revenue streams, and reduce costs. Investing in SLM and aftermarket services can help companies build long-term customer relationships and establish themselves as industry leaders. In conclusion, it’s important to acknowledge technology’s pivotal role in facilitating efficient Service life-cycle management and aftermarket services. Powered by AI and advanced analytics, Tavant’s Service life-cycle management solution is a closed-loop innovation that can help organizations provide a connected and seamless aftermarket service experience. This solution covers all aspects of the SLM process, including customer support, service request, service parts planning, service contracts, service execution and field service, warranty management, remote monitoring, and IoT capabilities. By leveraging this technology, businesses can elevate their aftermarket services to new heights, improve customer satisfaction, strengthen their reputation, and drive revenue growth, setting themselves apart from their competitors.

Revving Up Your Product’s Lifecycle: The Importance of Aftermarket Services and Service Lifecycle Management

Product quality and longevity are crucial for customer satisfaction and the long-term success of any business. However, even the best products require maintenance, repairs, and replacement parts over their life-cycle to ensure continued functionality and customer satisfaction. Therefore, service life-cycle management and aftermarket services are critical for companies to offer.

A Race Against Time: Disclose 10 loans in 3 minutes with Tavant’s Disclosure Automation Solution

Constantly changing processes means more oversight and manual intervention, resulting in slower automation and diminished ROI. Distribution processes that are frequently manual, uneven, and inefficient can be costly and risky: even a tiny error or typo supplied to a customer can lead to significant legal and monetary implications and repercussions. Also, you spend hours manually copying and pasting data from source systems or multiple spreadsheets. You send and receive information through unregulated channels like email, putting the integrity of your operation at risk. With the increasing complexity of financial regulation, companies spend a lot of time and resources on disclosure processes. Manual report assembly and review steps decrease process agility while increasing the possibility of reporting errors, while also wasting money and time. Disclosures are essential control mechanisms within the mortgage process. Automation is a prominent technique to decrease errors and risk, but it sometimes means different things to different lenders. Disclosure Automation has the potential to make things much simpler and more efficient. Disclosure Automation streamlines the process and ensures accuracy. It eliminates manual errors and helps to reduce the cost and time associated with the disclosure process. It also helps to improve the efficiency and accuracy of the process.     Some benefits of Disclosure Automation include the following: Improved accuracy: Reduces manual errors and improves the accuracy of the disclosure process. Reduced cost: Cuts down the cost associated with the disclosure process. This can help organizations save money and resources. Improved productivity: Streamlines the disclosure process and improves productivity. Increased transparency: Increases transparency and accountability. Less Manual Workload: Frees up employees to work on things that actually drive revenue   Your Disclosure Process — Made Better Bringing an end to the chaos in multiple systems The Disclosure Automation solution is a cloud-based disclosure automation platform. It helps to streamline the disclosure process and ensure accuracy. The platform is designed to be easy to use and helps to automate the entire disclosure process. It optimizes and streamlines disclosure by providing consistent validation of complaint disclosure. It helps to reduce the time and cost associated with the disclosure process. Disclosure automation helps to reduce manual errors and ensure the accuracy of the disclosure process. It helps to ensure that disclosure documents are compliant with regulations and standards. The platform helps to generate reports and analytics that can be used to improve the disclosure process. How can Tavant’s Disclosure Automation Solution help you? Instant Disclosures document to the borrower for e-signature and email notification to all the recipients (borrower & Loan Officer) Seamless configuration to validate ICE’s Mavent compliance engine and add copies of disclosures to the e-folder Supports many Disclosures such as Initial, Redisclosure, Closing, etc. Real-time tracking of borrower’s activities A highly scalable solution empowers borrowers and loan officers with a frictionless loan disclosure process Disclosure Automation is an ICE-certified solution that helps you with higher accuracy, increases productivity, and saves time and cost.   Why choose Tavant’s Disclosure Automation Service over other APIs or services? Every lender follows the same basic process to disclose loan terms, and someone at the compliance desk will most likely open a loan, review it, and then click the button to send disclosure documents to the borrower(s). These tasks require five to ten minutes of someone’s time per loan, and Tavant’s Disclosure Automation Service automates the workflow and allows loan officers to disclose multiple loans at the same time without having to log into Encompass and what businesses can achieve: Accelerated Time-to-Market: Development and approval have been truncated from weeks to days. Implemented in one month with over 100 distinct business rules. Cost Savings: Reduced team and 100% reduction in agency costs for disclosure change management High Accuracy: This eliminates human error from processes and ensures accuracy at every step of the way Improved Speed: The ability to disclose ten loans in three minutes, saving between five and twenty minutes of disclosure desk time per loan Risk Reduction: No need for legal to review each change; regulatory risk is reduced, and consistency is improved Increased Productivity: The ability to offload some time-consuming data tasks improves overall productivity   How to get started with Tavant’s Disclosure Automation Solution The lending sector will continue to be impacted by digital automation and transformation. Tavant’s Automatic Disclosure solution brings you one step closer to the finish line on your road toward digital modernization. The use of our service results in considerable cost reductions while also enabling operational efficiencies, time savings, increased accuracy, and increased production. The Disclosure Automation Solution offered by Tavant is suitable for use with Blend, in addition to Simple Nexus and any other point-of-sale system. This solution fully supports Tavant’s FinXperience and ICE’s Consumer Connect without any issues. Suppose the lender has built a bespoke POS or purchased off-the-shelf solutions from Blend, Simple Nexus, or another company. In that case, the solution can also work independently with some development effort. Get in touch with us! To learn more about Tavant’s Disclosure Automation solution, watch our recent webinar here or email us at [email protected] OR contact the Tavant team for a more in-depth discussion of solutions for your operating model and business.

Top Metrics & Measures to Determine Test Automation’s True ROI

Test automation is critical in a fast-paced agile development environment for releasing products faster by speeding up the test execution cycle, improving efficiency, and finding regression errors early. However, if we cannot assure the effectiveness of this process, test automation investments may be wasted. Test automation metrics reveal whether your approach is effective. Before diving deep into test automation metrics, let us understand what test coverage and automation coverage are. What is test coverage? Test coverage is defined as “What are we validating and how much are we validating?” It addresses both business and testing requirements. It is frequently confused with Code Coverage. Even though the fundamentals are the same, the points are distinct. Test coverage ensures that all requirements are confirmed and is a QE team pursuit. On the other hand, Code Coverage refers to unit testing procedures that must be directed at all portions of the code at least once and are carried out by developers. What is test automation coverage? In simple words, it shows how much coverage your automation suite is offering vs. how much testing is being done manually. It provides an impartial sense of your QE process that can help you identify and resolve pain points while improving your test automation performance: Test Automation Coverage = Number of tests automated/Number of total tests written Quality Metrics for Test Automation: It is critical to measure what we do and what we measure too. Though there are many metrics that we can collect for measuring how we are doing in terms of test automation, we think the following metrics are worth considering starting, and later you can add more as we make some progress on these ones: Automation Progress This metric refers to the number of automated test cases at any given time. This shows how you’re progressing toward your goal over time and whether there are any significant deviations during the automation testing process. This tells you nothing about the quality of the tests written; therefore, it is essential to ensure that automated tests are as effective as manual tests in catching defects. Automation Progress % = (Number of automated tests / automatable tests) * 100 Automation Stability This indicates how well your test automation suite runs over time. If your tests are failing (flaky failures over time), that is a decent statistic to tell if your tests are not stable. Also, in case there are false failures (false positives and false negatives), it becomes an early warning sign that your test automation suite is not dependable. Automation Stability % = (Number of failed cycles due to flakiness or false failures / Total Number of execution cycles) * 100 Automation Execution Time This indicates how long does the entire automation suite test execution take? Agile software development is all about speed, and the test automation suite should run quickly and not cause any unnecessary delays. This does not tell you anything about the quality of the tests performed. It just has to do with time. Execution Time = End Time of automation run – Start Time of automation run Automatable Test Cases This can assist you in identifying where you are prioritizing automation and what components/features might still necessitate manual validations. It is helpful in preparing the appropriate testing strategy and creating a balance between automated and manual testing. % Automatable = (Number of automatable tests / Number of total tests Written) * 100 Bottom line: Metrics are an important indicator of the health and success of an automated testing effort, but they should not be used as team performance goals. It is used to assess the tests, not the team. Since many companies have set up automation test suites to expedite their test execution cycle, selecting the right tools and contemplating useful test automation metrics are worth considering.

From Outdated to Outstanding: The Benefits of Revisiting Your Salesforce Ecosystem

Salesforce has been a powerful tool for businesses for over two decades now, and it continues to evolve and expand with new features and capabilities. As a result, many organizations have built large Salesforce ecosystems over the years, with multiple applications and integrations. However, with so many changes and updates happening all the time, it is crucial to regularly revisit your Salesforce ecosystem to ensure it is still meeting your business needs and providing maximum value.   Here are some reasons why it’s time to revisit your Salesforce ecosystem: Outdated Integrations: Over time, integrations between Salesforce and other systems can become outdated and may no longer be supported. This can cause problems with data accuracy, reliability, and security which can negatively impact your business. By revisiting your Salesforce ecosystem, you can identify any outdated integrations and replace them with newer, more robust solutions. Unused Applications: As your business evolves, you may find that some of the applications and integrations that you once relied on are no longer necessary. Keeping these unused applications and integrations in place can cause clutter, slow down your system, and create security vulnerabilities. By reevaluating your Salesforce ecosystem, you can identify and remove any unused applications and integrations, which will help streamline your system and improve overall performance. Missed Opportunities: New Salesforce features and capabilities are being released all the time, and it can be easy to miss out on these opportunities. By revisiting your Salesforce ecosystem, you can identify any new features and capabilities that could help improve your business processes and operations and implement them as needed. Compliance Requirements: Salesforce is used by businesses in many different industries, and each industry has its own unique compliance requirements. Reauditing your Salesforce ecosystem will ensure that your system is still in compliance with any relevant regulations and make any necessary updates to meet these requirements. Increased Efficiency: As your business grows and changes, your Salesforce ecosystem can become cluttered and inefficient. By revisiting your Salesforce ecosystem, you can identify areas for improvement, streamline processes, and make changes to improve overall efficiency. Improved User Adoption: Analyzing your Salesforce ecosystem can help identify any user adoption issues, such as confusing or cumbersome processes, and make changes to improve overall user satisfaction. This can lead to increased user adoption and better engagement with the system, which can drive improved business outcomes. Better Data Management: Data is a critical asset for any business, and it is essential to ensure that it is accurate, up-to-date, and secure. By revisiting your Salesforce ecosystem, you can identify any data management issues and make changes to improve data accuracy, security, and accessibility. Improved Customization: Salesforce is a highly customizable platform, and over time, customizations can become outdated or no longer necessary and can lead to a cluttered and inefficient system. By identifying and addressing any outdated customizations, improving system functionality, driving a better user experience, increasing productivity, and future-proofing your system, you can ensure that your Salesforce ecosystem remains a powerful tool for driving business success. Enhanced Mobile Experience: With the increasing importance of mobile technology, it is important to ensure that your Salesforce ecosystem is optimized for mobile use. Revisiting your Salesforce ecosystem can help identify any areas where the mobile experience can be improved, such as slow load times, difficult navigation, or compatibility issues with different devices. By making changes to optimize the mobile experience, you can ensure that your users can access and use Salesforce from their mobile devices, which can drive increased productivity and engagement. An enhanced mobile experience is a critical aspect of a well-designed Salesforce ecosystem, and can help ensure that your system is providing maximum value to your business. Future-Proofing Your System: The pace of change in technology is faster than ever, and it is essential to ensure that your Salesforce ecosystem is prepared for the future. Revisiting your Salesforce ecosystem can help ensure that your system is up to date with the latest features and capabilities, which can help prepare your system for future changes and challenges. This can involve regularly checking for new releases, attending Salesforce events and webinars, and staying informed about new features and capabilities. This can also help identify any areas where the system can be improved to better handle future growth and scalability. This can involve streamlining processes, optimizing data management, and making changes to improve overall system performance and efficiency.   In conclusion, revisiting your Salesforce ecosystem is an important step in ensuring that your system continues to deliver maximum value to your business. By identifying and addressing any outdated integrations, unused applications, missed opportunities, compliance requirements, areas for increased efficiency, user adoption issues, data management concerns, outdated customizations, mobile experience issues, and future-proofing considerations, you can ensure that your Salesforce ecosystem remains a powerful tool for driving business success. Tavant can help. As an experienced Salesforce partner, Tavant has helped global organizations achieve maximum business value through Salesforce transformation. We have enabled clients to redefine efficiency, collaboration and customer relationship with our in-depth domain knowledge, and Salesforce expertise. To learn more about how we can help you revisit your salesforce ecosystem and gain a competitive edge, visit here or mail us at [email protected].  

Remote Field Service is Here to Stay. Are you Ready?

Changes, Implications, and Impact In 2020, remote field service became an integral part of field service, and that trend is here to stay. Field service companies had to deal with remote services in the early days of the epidemic. Those that could adapt and embrace remote services were able to thrive in the shifting field service landscape. Even though the epidemic has ended, it is apparent that the influence of remote field service will be long-lasting. In fact, prior to the pandemic, organizations that invested in virtual services technology were deemed best in class and ahead of the curve. According to a recent report, the worldwide field service management market is expected to reach $29.9 billion by 2031, rising at a CAGR of 19.2% from 2022 to 2031. With the emergence of advanced technologies and evolving customer expectations, along with the impact of the recent pandemic, the field service business witnessed a drastic transformation. Organizations now want to use data from connected assets to improve service resolution as they transition from traditional and standard service contracts in which technicians and service engineers physically install, inspect, maintain, and service equipment to a model in which resolution and quality experiences can be delivered remotely. The ability to track service performance and value delivered will become more critical.     Remote Field Service- A Friend with Many Benefits Remote field service offers a number of benefits for companies, including improved customer service, increased efficiency and productivity, and reduced costs. It also enables businesses to provide customers with real-time access to service technicians and customer data, improving customer satisfaction. Additionally, remote field service can help companies reduce costs by automating manual tasks, such as scheduling and dispatching, and providing customers with real-time updates on their service requests. This can help companies improve efficiency and productivity and reduce labor costs. Also, massive volumes of data are generated by field service firms from technicians, assets, equipment, clients, and logistics. If the data is not utilized to enhance a company’s operations, it is basically squandered. Effectively harnessing and sharing field service data can enable more efficient field service operations—and, ultimately, a better customer experience. Need of the Moment Managing field service operations is a complex task. It requires strategies, tools, personnel, and resources to be effective. Legacy systems can push businesses towards obsolescence and lead to digital oblivion. Companies that don’t have a proper field service management system will inevitably struggle to keep up with their customers’ needs and may even face financial losses. With so many stakeholders involved in field service, implementing a clear adapt-and-response plan will be critical to sustaining day-to-day operations. Businesses that implement this will be able to retain revenue and satisfy KPIs. In an environment where equipment seemingly doesn’t fail or where the physical presence of an onsite technician quarterly can lead to a customer’s diminished view of the value of service, seeing is often believing. Therefore, manufacturers and service organizations must ensure they can highlight the value of service even if it takes place in the background without the customer seeing any disruption to their operations. Automating the “last mile’ Field Service Management systems automate the “last mile” between a service company and its customers, making the connection between the two more direct. It automates the job of field service technicians. However, “Field Service Management” is a catch-all phrase that masks the enormous advancements in the field service space. Field service management is essential for any business. It helps companies manage customer relationships, ensure customer satisfaction, and improve efficiency and productivity. It also helps companies reduce costs and optimize service delivery. Field service management helps companies cut costs by making processes more efficient and reducing the amount of manual work that needs to be done. It also helps companies improve the customer experience by providing customers with timely and accurate information about their service requests. Field service management also helps companies make more money by giving customers better service and delivering services more quickly. A modern, trustworthy FSM solution is one-of-a-kind in its capacity to allow service companies in any industrial sector to operate profitably while optimizing their commitments to deliver customer promises. At its core, Field Service Management enables service organizations to: Consolidate all their work from multiple systems of record such as ERP, CRM Maintain data in one central environment Contain a view of all the available resources, parts, and materials Successfully deliver the service required Increase uptime Shorten mean time to repair and improve first-time fix rates Empower field service technicians Reduce field service costs Increase customer satisfaction   Strategies for effective field service management Several strategies can help companies improve their field service management. These include: Automating manual tasks: Automating manual tasks, such as scheduling and dispatching, can help companies reduce costs and improve efficiency and productivity. Implementing customer feedback: Customer feedback analysis can help businesses identify improvement areas and adjust guarantee customer satisfaction. Optimizing service delivery: Collecting data from customer service calls and other sources can help companies optimize their service delivery and improve customer experience. Utilizing technology: Utilizing technology, such as remote field service, can help companies automate tasks and provide customers with access to service technicians and customer data in real time.   Competition is fierce in today’s changing business landscape, and customers want more. Field service operations must be agile so that your employees can deliver excellent service anytime and your customers know what to anticipate. Conclusion and Final Recommendations The field service industry is moving towards increasing customer satisfaction by improving service standards. It must be enabled by empowering service teams with a mobile on-demand solution that leverages real-time data from the field to streamline and automate processes. This gives field engineers the confidence to make data-driven and efficient decisions which can directly improve revenue growth, employee morale, customer satisfaction, competitive differentiation, and overall service quality. This will catapult real customer-focused service companies above the rest. Remote field service can revolutionize field service management and provide customers with real-time access to service technicians and customer

Touchless Documents-How Automatic Document Classification Makes Lenders More Efficient

Lack of digital documentation Mortgage loan applications in the United States typically consist of 500 or more pages of various documentation. Before applications can be evaluated, all of these documents must be categorized and the data on each form extracted. Most loan processing documentation is paper-based, from sales to origination and servicing. Aside from the obvious issue of high operational costs – including print prices, storage, and delivery – paper-based documentation can present significant challenges for lenders. Compliance with regulatory standards becomes increasingly difficult due to time-consuming procedures like integrating information from paper documents and reporting to regulators.   And this is more complex than it sounds!   Extracting data and classifying mortgage loan application packages has been costly and labor-intensive for lenders. A mortgage is often one of the essential purchases in a borrower’s life. Therefore, they don’t anticipate closing their loan in minutes or hours, as they do when applying for personal credit. However, they demand prompt service and are disappointed when paperwork delays last for weeks. A growing number of homebuyers are from a generation of digital natives who want transactions to go smoothly. To compete in the fickle millennial market, lenders must compete on experience, quality of service, and speed. Automating data capture can fast-track the home loan process from weeks to days and increase operational efficiency in the loan process. Modernize and automate processes  How can Touchless Documents change the mortgage cycle? The importance of touchless documents in the mortgage sector is straightforward: it automates document processing. Think of the significant amount of time spent on tasks such as identifying the type of document, organizing them into proper files, examining income documents, and manually inputting data. Touchless Documents automates indexing, extraction, and filing, saving lenders the time and effort of manually processing most documents. Instead, lenders only need to check documents marked as faulty by the AI system. This will benefit mortgage lenders in multiple ways: Increase operational efficiency in the loan process: Automate the historically time-consuming lending process, making it more streamlined and seamless for all stakeholders. Faster time to close: Most lenders who use next-gen digital technology with AI can expect a 30% decrease in document processing times. This automation alone reduces the average origination cycle by several days. Improved staff utilization: Most lenders struggle to keep their senior processors and underwriters focused on the most crucial credit decisioning activities. Paper procedures continue to take up an undue amount of time in their daily operations. Loans usually include at least 20 different types of documents totaling hundreds of pages. Specialists have more time to focus on their core, high-value work when AI extracts routine data from papers. Less human errors: New AI models for document processing are exceedingly accurate, with error rates for structured documents often falling below 5%. This eliminates the possibility of costly human data extraction errors, which slow down the underwriting process. Human mistake rates rise during particularly busy periods as staff strains to keep up. Regardless of volume, a machine-driven process produces consistent outcomes. The most recent AI engines have feedback loops that minimize mistake rates as the model refines itself. Touchless Documents: Work Smarter & Faster While lenders have had to undergo lengthy processes in the past manually, Tavant has introduced a machine-oriented approach that successfully increases workflow by up to 80% by automating and regulating the processes of loan application and disbursal. Tavant’s Touchless Documents instantly recognize documents, automating document classification, indexing, splitting, categorization/subcategorization, pairing with borrowers, and data extraction with the highest accuracy. Additionally, the Touchless Lending platform integrates seamlessly with existing lender systems, including CRMs, Point-of-Sales, LOS, and document management systems, to optimize document-related workflows, organize and process documents faster, and deposit the results of the document classification and data extraction back into the system of record. Processing broker-submitted loan paperwork was time-consuming and labor-intensive prior to the implementation of Touchless Documents, necessitating the use of a pool of human resources and turnaround times that could be overnight. What used to take hours is now done in minutes after integrating Tavant’s Touchless Documents. Brokers can receive quick feedback on their file uploads and provide direct advice to their borrowers on document upload requests. Touchless Docs has helped businesses process nearly 500 loans and over 90,000 pages of loan documentation in just one month, with a document classification success rate of about 92%. Tavant has been the top Fintech software and solutions provider for over 20 years, anticipating customer demands and adjusting accordingly to provide the correct customizable solutions. Tavant’s Touchless Lending® product suite maximizes data-driven decision-making to address even the most complex lender and borrower issues. How you begin is how you win! As the leading Fintech software and solutions provider for more than 20 years, Tavant proactively anticipates customer needs and adjusts accordingly to provide the right configurable solutions. Tavant’s Touchless Lending® product suite maximizes data-driven decision-making to solve even the most complex lender and borrower challenges. Reimagine Your Mortgage Experience Using Touchless Lending® Request a demo or visit here to learn more. FAQs – Tavant Solutions How does Tavant implement automatic document classification for lenders?Uses AI/ML to identify, classify, and extract data from loan documents like pay stubs, bank statements, tax returns, and IDs, processing documents in seconds. What efficiency gains do lenders achieve with Tavant touchless document processing?70-80% reduction in review time, 90% fewer errors, cost savings, faster approvals, and improved customer satisfaction. What is automatic document classification in lending?AI-powered identification, categorization, and data extraction from loan application documents without human intervention. How accurate is automated document processing?95-99% accuracy, continuous improvement via ML, handles various formats, flags uncertain cases for review. What documents can be automatically classified in lending?Income statements, bank statements, tax returns, IDs, appraisals, insurance policies, employment letters, credit reports, and supporting docs.

AI is the future of test automation- Are you Ready?

Traditional QE Isn’t Working Anymore Traditional tried-and-tested methods of testing and quality need to catch up in today’s changing environment. A siloed approach: Typical QE teams are separated from development teams. This structure concentrates on optimizing the subcomponents and deviates from the true purpose of enhancing the user experience. Slowing overall engineering velocity: Traditional quality engineering has been chiefly manual, impeding rapid development and operations procedures. Expensive: Traditional QE requires significant engineering resources and costs 30%–40% of the overall expenditure. An afterthought: For decades, the testing strategy has been put off until the end of a product cycle, which is too late and can cause release delays and budget overruns. THE NEW DIGITAL ERA REQUIRES INCREASED SPEED & AGILITY DevOps and intelligent automation, as well as the proliferation of digital applications, have considerably challenged traditional techniques for application testing in recent years. Delivery times have gone from months to weeks, and nowadays, testing has moved to the left and right of the software development lifecycle. Agile and DevOps have combined development and testing into a single, continuous process. Quality engineering has changed from testing to starting with the planning of the first application. It creates a constant feedback loop that lets you plan for the unexpected and act on it. However, to properly comprehend the magnitude of the evolution from testing to quality engineering, we should first recognize how data has impacted software development. Data can do more than just power automation use cases and AI learning datasets for repetitive development and testing processes. The enormous amounts of data users create daily to make it more important for quality engineers to predict risk, find opportunities, increase speed and agility, and reduce technical debt. Quality engineering is changing in tandem with the ever-increasing cyber security concerns. Today’s quality engineering role must enable faster application, product, and service delivery and act as an enabler, not a barrier, to digital transformation. TAKING QE IN THE NEW As these changes in quality, technology, people, and organizations take hold, QE will grow into a role that is more pervasive, real-time, and based on insights. AI-led autonomous frameworks will support this to make sure business continuity and value. Testing will evolve away from traditional ways and toward new ideas and methodologies appropriate for the application engineering world of the future across five dimensions: data, frameworks, process, technology, and organization. FROM APPLICATION-FOCUSED TO PURPOSE-DRIVEN Today’s rapid growth of enterprise application testing environments shows no signs of slowing. As it evolves, QE’s focus on apps will be less defined by its alignment with business objectives. This means testing, monitoring, and making real-time fixes to ensure that the business “works” as well as the code. It also entails creating self-learning, self-adapting systems assisted by machine learning and advanced analytics. Tavant is actively planning for this future. We are propelling QE into the future with breakthroughs in holistic QE strategy, incorporated cognitive and machine learning capabilities, and end-to-end automation. This changes everything, from test planning and test case development to test execution and environment setup, and it helps QE reimagine its role in the future enterprise. Tavant Quality Engineering Services helps organizations engineer quality into their processes by incorporating a whole gamut of services, tools, and techniques to elevate the end-user experience. AI-Powered Next-Gen Test Automation Framework Tavant’s AI-powered next-gen test automation platform, FIRE  (Framework for Intelligent and Rapid Execution), is Tavant’s proprietary suite of solution accelerators aimed at optimizing the overall testing effort and delivering a high-quality product. It is a comprehensive tool and technology agnostic-test automation framework. This framework can orchestrate multiple automation tools and technologies, including (but not limited to) Selenium, Appium, Cypress, Protractor, Microfocus and Java, C#, F#, Python, and PHP. The test automation framework ensures speed to market and superior quality software. FIRE 5.0 accelerates the time to market while simultaneously aiding developers to enable a dual shift of the software development lifecycle to gauge the consumer experience and provide continuous feedback into the system. It offers comprehensive test automation coverage and efficiency of more than 90%. Importance of Quality Assurance & Testing in the Finance Industry The financial industry is on the verge of transformation. Mobile banking, investment, insurance, app payments, advances in technologies such as cloud, mobile, AI, agile, and DevOps, and concerns about fraud detection management, data visualization analytics, risk and compliance management, and digital lending require specialized testing. Tavant’s Quality Engineering Services have been designed with the financial industry in mind. Our quality engineering services can help you with an error-free application and accelerate your time to market at a lower cost. Visit here to learn more.

Driving Scalability with Universal Registry for TV advertising

A Massive Ad revolution is in progress Large amounts of advertising dollars previously locked up in traditional linear TV advertising have become available over time. Much of this is due to the consumer migration to digital and OTT. This move recently included the implementation of targeted, dynamic ad insertion for live, linear broadcast programming across multiple MVPDs, thus expanding the impressions available for sale by programmers But are the advertising infrastructure, procedures, and governance in place to support such a shift? This shift to targeted advertising has created the need to rethink traditional inefficient workflows and re-evaluate the scale needed to support an order-of-magnitude increase in advertisers and campaigns, including how to manage yield efficiencies. Advertising dollars are spent through campaigns, which often include multiple placement orders distributed across numerous broadcasters or publishers. Agencies typically administer these campaigns to track how much of the budget has been spent or is pending.  A complicated campaign, on the other hand, can swiftly spiral out of control as it is routed through several intermediaries and end parties. Consider the chaos that ensues when hundreds of campaigns are carried out daily. The absence of underlying rules across these several bodies for governing campaigns and creatives implies that significant inefficiencies plague the entire system. The parties go to great lengths to reconcile the execution of the campaigns. There is no doubt that advertising budgets get leaked through the cracks in the system. What are the arguments in favor of a common taxonomy and universal identifiers? ‘Johnson and Johnson,’ ‘J&J,’ and ‘Johnson & Johnson’ are all variations of the same firm. This example demonstrates the lack of uniform standards. With increased targeting and measurement becoming the norm, we need data compatibility between systems, which requires standards. The increased proliferation of programmatic inventory will necessitate the standardization of many brand and advertiser identities. Interconnected systems will also enable workflow automation for tasks such as copy instructions, RFP automation, etc. Uniform IDs/taxonomies will enable new, robust measuring and reporting methodologies, allowing for simple information consolidation in a multi-channel world. Industry-standard ID solution To address the issue of non-standardization of entity names and campaigns across the sector, a centralized registry of names should be established, where a unique ID is issued, managed, and standardized. The centralized registry would be responsible for developing and maintaining a consistent taxonomy and registry for advertisers, brands, and campaigns. Including elements like copy instructions and creative changes would lay the groundwork for a more comprehensive, centralized tool. The standardized solution leads to greater automation across ad workflow This is how a centralized repository model will impact several advertising processes: RFP – All requests for proposals come into the centralized system, which assigns a referenceable id that can then be integrated into the seller’s system instead of being handled externally as is now the case (through email or phone). Proposal – The seller creates the plan in their system and sends it to the buyer via the centralized system, which links the proposal to the RFP. The plan is communicated between the vendor and the customer in the current scenario via email or phone. With each side having its system of distinct IDs, this rapidly leads to a lot of backtracking and cross-referencing. Creative Delivery – Creatives are delivered to a centralized hub with industry-standard metadata, such as links to centralized campaigns and line-item IDs. Currently, creatives are provided to each publication with minimal standardized data attached to them. Ad standards – All ads will be pre-cleared for compliance through the central registry. In the current process, each publisher manually views each creative to see if approved. Instructions – Instructions will be provided via the standard format API. Currently, there are disparate methods and formats for providing instructions. Reporting – Advertisers and brands will be standardized, allowing for industry reporting and consolidated fulfillment info for buyers. Currently, publishers have individual advertiser and brand naming conventions. Invoicing – The invoice should display spot details and the value of each spot. Components of the Solution Building blocks of the Centralized Registry system are depicted below.       Integration through APIs Master input and update of campaigns Automatic push to Order Management Systems Integration with creative content management systems Integration with Ad servers such as GAM, Freewheel       Self Service Interaction Portal to allow advertisers and broadcasters visibility and update to creative and campaign status. Built in notification of pending flights and creative updates         Campaigns & Flights Master repository of campaigns Cross MVPD visibility to Flights. Cross Flight visibility to Campaign. Auto notification of updates/changes       Creative Management Master repository of creatives Standardization of brand and advertiser names Distribution status of creatives Integration with creative transcoding         RFP Management Input of RFPs via multiple options such as forms, import or salesforce integrations Standardization of all RFP elements RFP workflow with required alerts       System Reporting Campaign Dashboard Flight Dashboard MVPD Dashboard Critical Issues status System Logging by actor Conclusion With a massive advertiser shift underway, the time has come to introduce a unified campaign and creative control across the ecosystem. This will provide the creative identification space with the now lacking interoperability. This repository will not only serve as a single source of truth for advertisers, but it will also standardize the naming convention across different partners. This will result in ecosystem alignment and enforcement, with each player recognizing and passing the identifier to the next player in the chain. Reach out to us at [email protected] or visit here to learn more.

Get More Out of Your Field Service Operations with Service Analytics

Manufacturers strive to differentiate themselves in an era of connected products and services. And one way to make this happen is through improved service operations. With field service analytics, contractors often on location to install, maintain, or repair equipment, systems, or assets, can enable higher customer satisfaction and profits.     From Cost Center to Competitive Advantage The sheer volume of available information can do wonders for a field service operations. The data collected by a field services organization via its fleet and workforce management technology (as well as how a business uses this data) can set it apart from the competition. Warranty Management is an example of this, which has traditionally been regarded as a cost center. Manufacturers are beginning to recognize that (when combined with the right platform, technology, and partner), the volume of data accumulated can actually be used to gain a competitive advantage. Why Service Analytics is Gaining Momentum With more connected equipment and sensors than ever, today’s manufacturers have access to more potentially valuable data nuggets. A study by the Aberdeen Group found that field service organizations that adopted analytics technology saw their service profits increase by 18%, customer retention rates by 42%, and SLA performance by 44%. Let’s look at some of the ways this can happen: Ways in Which Service Analytics is Impacting Field Service Field Tools & Knowledge Repository Field Service Technicians are constantly under pressure to provide a solution or repair as soon as possible. They are sometimes given very little time to understand the nature of the problem they are called in to solve. Field service management technology provides tools and access to knowledge repositories to field service personnel on the job, allowing them to troubleshoot more quickly. With field service technicians having access to information and insights, jobs get completed faster and result in a seamless experience for the customer, dealer, and manufacturer. Machine Failure Prediction Service analytics can help make an impact on reducing machine downtime and,as a result, project downtime for your customers. Imagine being able to send an email notification to the dealer, telling them of a 40% probable engine replacement. With parts identified and ready to be shipped directly to the dealer, on their agreement. Service analytics offer manufacturers real-time actionable insights to increase machine uptime, reduce part failures, and save on cost and effort. New Opportunities Manufacturers are drowning in data as IoT devices and sensors, connected machines, and other technologies proliferate. With the addition of smart learning models becoming more accurate, manufacturers can now use service analytics to drive decision-making. The integration of field service analytics with the sales CRM system enables product cross-selling and up-selling. This, in turn, may reveal opportunities to boost aftermarket revenue. Integrated View Service was frequently viewed as an afterthought by many manufacturers, with service prices discounted or given away to promote product sales. Sales teams are pressured to sell products or equipment with little regard for the service team’s ability to execute. This typically leads to a compromised customer experience as the service leader begins to reallocate resources to meet customer needs. These operations can be handled more efficiently with service analytics. Field service operators can get a complete picture of all assets, products, and customer information in one location. As a result, they are better able to advise customers, resolve issues more quickly, and increase productivity. The Future of Field Service is Seamless With an increasing demand for personalized, actionable customer support, service analytics can play a significant role. An entire ecosystem surrounds the customer, with field services is at the forefront. Putting analytical tools in their hands can empower personalized and quick service resolution. By using field service data, businesses can create more lifetime value for their customers while improving business processes and practices across the service life-cycle. The only question that remains is, how soon?

Code-based versus Low-Code/No-Code test automation solutions: Which one to Choose?

Concerns about the quality of software test automation solutions are growing every day, and we face an array of challenges in addressing them. One of the challenges is that we have several test automation solutions to automate our test cases (Web, API, Mobile, etc.). Some test automation solutions in the market require exceptionally good programming knowledge, whereas for a few, intermediate programming knowledge is enough, and we have a few where we can automate things with almost no coding experience. To top it all, there is pressure to deliver faster to the market   What is a Code-based Test Automation Solution? Coded solutions, like traditional automation systems, necessitate a very trained workforce with an in-depth understanding of certain tech stacks. A team capable of writing custom code from scratch is required in this case. These solutions are intended to be developed and used by technical users such as SDET, developers, etc. What are Low-Code/No-Code test automation solutions? Low-code test automation solutions allow users to automate tests with some or little coding skill set/experience. Most of the automation testing happens without actual programming. Typically, the most used features/utilities are already built-in through GUI so that users can select the required actions and combine them into a sequence. However, coding expertise is necessary when achieving anything complex to interact. No-code test automation solutions allow users to automate tests in the application with almost no coding knowledge and experience. These solutions are intended to be used by non-technical users such as product owners, business analysts, etc., where we would mostly need to select, click, enter text, scroll, or drag and drop. Difference Between Code-based Vs. Low-Code/No-Code Test Automation solution Category Code-based Solutions Low-Code/No-Code Solutions Coding Need High Low or None Complexity Overly complex Less complex Flexibility Extremely flexible Less flexible Primarily Servers Technical users (Developers and SDET) Anyone who is a part of the project can contribute. Security Concerns Quite Low High Execution Speed Low to Medium (depending on the test case count and test steps). High Automation  Design Robust Tightly coupled   In today’s world, where new test automation solutions are frequently released, enterprises are looking for ways to expand and accelerate their software delivery processes. Even low-code/no-code solutions now have all the necessary built-in qualities that make them simple to implement with little to no coding knowledge/expertise. The question is if it is winning hearts! Code based Solutions Pros Cons Design and workflow flexibility – Design in accordance with your company’s existing workflows, expertise, and skillset. Ease of Use – Understand your intended users and their skill sets. Create the framework to match up. Need a new feature? – Decide the priority of that feature and implement it, what features your framework should have, and to what extent each feature should go. Something not working? – Find the root cause and go ahead and fix it. Reporting or Dashboard requirement – You have complete access to your execution results and can create whatever report/ dashboard format you want. Pricing – The long-term cost-per-run is much lower than any low-code/no-code test automation solution. Time to build – Creating a stable solution takes time. Depending on the AUT, it could take a lot of time. Need to provide your own DevOps/SecOps ecosystem No Outside assistance – When you develop your own solution, you have only yourself to hold accountable when things go south.   Low-Code/No-Code based Solutions Pros Cons Almost no ramp-up time, it is a ready-made solution – no need to build your own. No maintenance for hardware and no need to involve DevOps/SecOps. Outside assistance – You have someone to assist you (based on your support contract) in case you have any queries or need help. Limited scalability- Need a new feature or integration with another tool? – the solution does not yet support a feature. You must wait for a feature and support ticket (you do not have any control over deciding priority) Support wait time – Response time depends on your subscription. It can be anywhere between minutes or hours to days at times. Pricing – it varies, but in the long-term, cost-per-run is significantly higher than using any traditionally built code-based test automation solution. Limited customization- Inflexible reporting/dashboard – Most of these solutions will not give you a choice to have “out-of-the-box” customization. Have any queries? – You are dependent on the solution maker to help you out. Helpless- Waking up after a few months of solution implementation to realize that you cannot increase automation coverage due to the solution’s lack of support. The automation solution company is sunsetting the tool due to any reason.”   Since each organization works on different objectives, to deal with the question of which approach to use, here are the top few items to mull over: Who (Tech/Non-Tech/SDET, etc.) will create and maintain these automation test suites? What is getting automated – APIs/Web/Responsive/Desktop/Mobile apps? How complex are the test cases and business situations to be automated? What is the skill set/expertise within the team for creating and maintaining this test automation suite? Is this a new or an existing project where we have already done some automation? Is the test automation suite meant to be integrated with other tools like Test Management, bug tracking, CI/CD, etc.? The test automation suite is meant to be executed at what size? What is the budget and time duration required to complete this project? Final Thoughts It is imperative to realize that there is no silver bullet. As shown above, each choice has its own pros and cons. The key to success is choosing the right solution that balances your team’s skill sets and expertise and simultaneously meets your organization’s objectives. Until then, happy test automation!

7 Ways to Get the Most Out of an Industry Conference

Conferences play a significant role in just about every industry by providing a setting where like-minded people can connect through networking, share ideas from different aspects of their work, and learn from each other. Although conferences were forced to adjust how they function as a result of the Pandemic, year 2022 saw a return to normalcy as a more traditional business schedule resumed. Tavant took full advantage of the departure from virtual events and excitedly jumped right back into the fray. Our FinTech team represented Tavant at 25 industry conferences this past year. We know times have changed, and because of that, we want to share our top 7 tips to help you be as prepared as possible for all the conferencing that will take place in 2023. 1: Preparation is key. Preparing is an essential tip for any conference attendee, rookie, or veteran. Having an idea of what information you are looking for and reviewing the agenda allows you to plan accordingly, so you don’t waste any time and can get the most out of the event. Finding out who’s going, if possible, and figuring out with whom you want to connect enhances the possibility of you creating new connections. Also, it is important to make a plan that works for you. If you are better at networking and more of a social butterfly, maybe setting up more meetings or going to frequent get-togethers will help you get the most out of the conference. Regardless of your sensitivity to social interactions, it wouldn’t hurt to have an “elevator pitch” ready in case the opportunity presents itself. Lastly, make sure you bring any items you might need, like chargers, business cards, or anything else you might want to help you get through a long day. No one wants to leave a conference with their strongest memory being a missed opportunity. 2: Gold star attendance. This may seem like a no-brainer but attending the sessions is one of the best opportunities that you will have to learn new aspects of the industry and potentially what your competitors are up to. Take notes, bring a recorder, or whatever you need to do to help you gather and retain as much information as possible to benefit you and your company. 3: Networking is essential. Networking is a big part of conferences – it leads to new relationships and opportunities that can have incredible value for your individual professional career or the success of your company (preferably both). If you have been to conferences before, reach out to past or potential colleagues in the industry beforehand instead of wasting time trying to find them during the conference. This will save you time and allow you to take in more of what the conference has to offer. Try to connect with a speaker if you can, but this might require a backstage pass and some luck. Don’t forget: a lot of networking is done outside the venue, so attend those after-parties and other extracurricular events in order to maximize success. After the conference, reach out and chat with those you met to help build those new relationships. This allows the connection to grow and shows your appreciation for having met them, which feels good for whoever is on the receiving end. 3: Be active on social media. One aspect that people might overlook with everything going on during a conference is being active on social media. LinkedIn is the easiest way to connect with like-minded professionals and stay in contact with people. Twitter is also a solid option depending on how public-facing your industry or company tends to be. Scrolling through the newsfeeds of your relevant social media accounts before a conference may give insight into who is attending and the topics that might be covered. Furthermore, checking for any conference-specific hashtags is always a good idea. This will also make it easier to anticipate and understand the thoughts and interests of those attending, specifically those who are excited enough to post about it online and share it with their colleagues. If you are short on content, try diversifying the type of content you share on social media by taking more pictures and videos while at the conference, particularly of yourself with people you’ve met. Remember: a photo is a lasting memory, making it a great way to connect with people and develop strong professional relationships. Not to mention, if you are one of the first people to post about a conference you are attending, then you are far more likely to maximize the size of your audience for that content, thereby bringing more brand awareness to your company. Be sure to ask for the consent of all parties involved before taking pictures to avoid any unwanted photographic attention! 5: Stay organized. Conferences can be overwhelming – there is lots of information being exchanged with not always enough time to process everything. This is where staying organized comes into play. Having a well-thought-out organizational system will help you stay on top of your schedule during the conference and make it easier to keep track of names, session notes, business cards, and other information after the conference has finished. Whether it’s labeling information properly right away or taking time to collect everything necessary at the end of the day, the small efforts you make could be the difference between new opportunities and missed ones. Whatever you come up with, make sure you have a plan that works for you. 6: Divide and conquer. While this applies more if you attend a conference with coworkers, it is essential that you don’t all attend the same sessions, as it limits how much your company could have gotten out of the conference. Instead, spread out and take advantage of as many opportunities as possible to learn more and connect with more people. Also, make sure to set up a meeting time with your coworkers before and after the events to plan or recap the day. Divide and conquer

QAOps – Shift in the QA paradigm

What is it? Is it a specialization or a new team role? – The answer is No. QAOps, also known as Continuous Quality (CQ), is a process of including quality engineering (QE) in Continuous Integration and Continuous Delivery (CI/CD). So, instead of being an isolated process, software testing is integrated into the CI/CD pipeline. It requires solid collaboration between the QA team, development, and IT operational teams to build a highly effective and cohesive process. In contrast to DevOps, QAOps emphasizes QA engineers’ problems and the importance of integrating software testing into the DevOps workflow. How to Implement QAOps: We can successfully implement QAOps by implementing Automation Testing, Parallelization, Scalability Testing, and Integration of Dev and IT Ops with the QA team. Let us look at each of these briefly: Automation Testing. Automated testing forms the base for the QAOps. It involves performing tests with the help of scripts, tools, etc., to certify test cases by repeating pre-defined actions that require very minimum human-centric efforts. To make it happen, SDET engineers must work on building a solid automation framework. Once we have an automation framework in place, QA engineers select the tests that can be automated which saves time and tests functionalities well. QA engineers should focus manual effort on testing only those functionalities that cannot be automated and/or exceptional testing use cases that are not good candidates for automation. Although it is impractical to automate every test due to tool and technology stack limitations, we should strive for high automation coverage by automating as many tests as possible. The best way to approach QAOps is to integrate automation testing into the CI/CD pipeline. Parallel Testing (Parallelization) Parallel testing entails running multiple tests concurrently rather than sequentially. It allows you to run tests in various browsers and platforms at the same time, drastically reducing testing costs, effort, and time. In the QAOps framework, your tests should run quickly because if the execution happens slowly, then it will impact the entire delivery process. We should run our tests parallelly instead of sequentially to achieve speed. Additionally, it improves test coverage. Parallel testing necessitates a good infrastructure to run the tests concurrently, but the results are impressive with no impact on the delivery pipeline. Scalability Testing Test scalability comes into play once the application goes live and begins to gain popularity and gives you the desired results, which is when you must scale it judiciously. When the application scales, the testing of that application must necessarily scale as well. Scalability helps in determining the application’s performance under varying load conditions. With the result of scalability testing, we can conclude the response of the application with respect to the differential loads. As a standard QAOps practice, the QAOps team must have access to the scalable infrastructure and framework to perform testing and increase the speed of tests when needed. Integrate Dev and IT Ops in QA The final and most crucial step toward the framework’s success is incorporating all QA activities into the CI/CD pipeline. Applying a shift-left testing approach to integrate the QAOps framework can help to avoid launch delays. When the QA engineers collaborate with Developers and IT Operations teams, it helps in testing new features without any lag from the team. This collaboration between the different teams makes the development and testing process more effective. Here are a few responsibilities (not only limited to) of an engineer who performs QAOps work: Building an automation test plan Developing and maintaining the QA automation framework and scripts Configuring remote automated test execution (including parallel run) Reporting and distributing the results via communication channels such as Slack, MS Teams, email, etc. Communicating and collaborating with the Operations and Development team (from development start to deployment into live environments) Few tools/technologies stack that the QAOps team use for different streams to add value to the QAOps process: Functional Automation: Selenium, Appium, Cypress, Playwright, Protractor, WebdriverIO, and others. Performance Testing Tools: JMeter, LoadRunner, NeoLoad, and others. CI/CD Tools: Azure DevOps, AWS CodeBuild, Jenkins, Git workflow, and others. Cloud infrastructure: Azure, AWS, Docker, and others. Remote browser execution: BrowserStack, SauceLab, Pcloudy, and others. Reporting: Extent, Allure, Report Portal, and others. Benefits Of QAOps: As this process demands collaboration between the QA, Development, and IT Operations Teams, it allows them to enhance their skills in a variety of areas. As the QAOps process abides by the shift-left testing approach, this accelerates issue fixes early without sacrificing time and deploys the application sooner. CI/CD testing allows issues to be identified at an earlier stage, providing a reliable application with the utmost quality. Because testing occurs on a continual basis, the chances of an improved customer experience increase since the application quality and delivery are improved. IT operation team avoids any delays by having the QAOps operations run constantly. This permits the QA team to test new apps/features without being slowed down. In Conclusion QAOps is critical for teams that automate their CI/CD pipelines because it emphasizes speed without sacrificing quality. Once implemented, this process in the CI/CD pipeline saves time and money on testing products. The rise of QAOps highlights the problem that quality is frequently overlooked in software development.

Why Data Modernization Should be a Priority for all Lenders and Bankers

Before the pandemic, mortgage companies were already under attack from fintech and nontraditional lending organizations. Most businesses didn’t have much to say about how the changing competitive landscape would impact their lending programs, so it seemed like the mortgage and banking industries were happy with how things were going. They mainly relied on the relationships they had built with their customers. However, in a post-COVID world, digital technology has completely transformed the financial industry. Mobile banking has gradually replaced brick-and-mortar banking. The cadence of transaction processing has shifted from periodic batching to real-time processing, posing a significant challenge to financial institutions; legacy IT systems are obsolete and incapable of providing a real-time digital banking and lending experience. This is challenging because customer expectations are not just sky-high – they are stratospheric. The demand for consistent real-time digital lending has made the financial services industry more competitive, and banks and fintech mortgage lenders are struggling to meet the needs of modern customers with their legacy, rigid IT systems. Data Modernization: The Foundation for Digital Transformation   Data modernization, a process of migrating siloed data from legacy databases to modern cloud-based databases, enables organizations to be more agile by eliminating the inefficiencies, bottlenecks, and unnecessary complexities associated with legacy systems. Until recently, the processes for implementing loan origination hadn’t changed for decades. In many organizations, the process is still “informal” and carried out manually, often with paper documentation sent from department to department. The pandemic has revealed flaws in nearly every company’s data management practices. Organizations have recognized the urgent and critical need for a modern data infrastructure that manages data to make it highly accessible, practical, compliant, and valuable. Fintech mortgage lenders benefit from near-term cost savings and powerful analytics that extend personalization and optimize forecasting when they have a modern data backbone and digital mortgage solutions. As a result, mortgage companies have started shifting their focus from optional to critical digital transformation. The first step toward modernization is to create automated flows for this overall process, employing RPA, artificial intelligence (AI), or machine learning (ML) technologies to reduce human involvement, reduce errors, and automate adjustments where necessary, all in support of human activity where desired. Second, reviewing the data needs for this process and making the data better and more complete can help people make better decisions and grow the credit market. Data becomes even more powerful when it is smartly combined with intelligent process automation (a combination of Robotics Process Automation (RPA) and Artificial Intelligence (AI), or more precisely, a mix of tools and techniques such as OCR, speech recognition, Machine Learning, and Natural Language Processing (NLP) techniques. Loan forgiveness and mortgage forbearance are not new elements of loan servicing, but those areas have reached a scale hitherto unknown in the mortgage industry. Therefore, additional data and analytics are needed to make better decisions about loan modifications and their potential impact on the business’s risk and capital. AI analytics will only be helpful if the mortgage companies have the additional data to make a meaningful decision. A real-world example of the value of alternative data came from 2016 when severe flooding affected homes owned by a regional bank. Rather than waiting for homeowners to default on flooded or destroyed homes, the bank enlisted the assistance of a mapping and analytics firm to confirm flood-stricken homes against the bank’s mortgages. As a consequence of this, the bank was able to make use of the data and achieve a significantly improved comprehension of the threat that this event posed to its portfolio. And the organization was given the tools it needed to proactively reach out to the customer to arrange forbearance or provide other assistance to the homeowner. Why is Data Modernization an opportunity for lenders and bankers? Process efficiency: Reducing the “time to yes”  The underwriting process’s inefficiency occurs in preparing the credit proposal, outlining detail for the credit committee what all these risks are, and calculating their likelihood and impact. Automation, data insights and analytics, and underwriting platform-based digital mortgage solutions are key levers that significantly impact the underwriting value chain. These technologies influence risk assessment and proactive risk monitoring and thus aid in risk prevention. Next-gen modern technologies automate manual processes and integrate legacy applications, such as policy administration systems, to eliminate information duplication. Other interventions, such as agent/customer portals, intelligent workflow, and real-time process visibility, allow agents and underwriters to work closely together. Subsequently, it reduces sales cycles, bringing “time to yes” down to five minutes. Raising the standard: transparency, consistency, and auditability Modern loan origination systems help standardize the credit underwriting process by ensuring that the best method for managing operations is used. Individual lending organization differs in some details, but most follow a consistent pattern in credit underwriting, and this process can be improved if everyone involved uses the same platform. Having instant shared access to the information required to complete the underwriting process improves efficiency while also increasing transparency and lowering the operational risk of critical information remaining in the hands of a few key personnel. It’s all about the data  Banks and financial organizations generate massive amounts of data, and the vast majority of them are terrible at managing it. Data can now be found everywhere. Data modernization allows for more informed decision-making by reliably extracting data from various disparate systems. It facilitates the identification of high-value data combinations and integrations. It also enables people to identify opportunities at the moment quickly, allowing them to capitalize on something that would otherwise have gone unnoticed, eventually generating more revenue. Furthermore, data modernization reduces the risks associated with data security and privacy compliance. In its process, it looks for sensitive information so that you can limit user access to data in a precise and more efficient way. How can your organization take advantage of digital lending modernization?  Consumers have shifted dramatically toward online channels during the pandemic, and businesses and industries have responded in kind. To help financial institutions achieve operational efficiencies, credit process optimization and automation of low-end credit processes to

How Fintech Automation is Changing the Face of the Lending Industry

Understanding the Changing Landscape in the Mortgage Industry The mortgage industry is under enormous pressure to perform in the face of fierce competition, increased due diligence for loans and borrowing (due to the COVID-9 pandemic and its economic ramifications), crunching timeliness, and ever-growing data. According to Gartner, a human error in the financial sector results in 25,000 hours of pointless rework per year, costing up to $878,000. Our only stumbling block, we believe, is fintech automation.   The global fintech industry is estimated at $65,88,780mn in 2021 and is projected to reach $1,66,52,680mn by 2028 at a CAGR of 13.9% for the forecasted period. Fintech automation can define even the most unstructured data and support lending process automation to deliver a resource, cost, and time-efficient process. Mortgage and lending have already been reimagined using automated technology such as chatbots and digital assistance. Given the massive amount of data, the need for real-time, data-driven strategies for effective customer UX and UI, and loyalty retention in the mortgage lending industry, this shift is inevitable. Customer onboarding to Know Your Customer (KYC), legal processes, due diligence, credit checks, and form fill-ups have previously been observed to require 50-75% of the onboarding process cost. However, well-integrated lending automation, combined with optical character recognition and natural language processing, assists mortgage lenders in shortening the lending cycle and lowering costs. “Generative AI enables bank CIOs to offer technology solutions to the business in pursuit of revenue growth,” according to Moutusi Sau, VP Analyst at Gartner, “while autonomic systems and privacy-enhancing computation are long-term solutions that provide new options for business transformation in financial services.” We can cite several reasons for the rapid adoption of automated technology as a core business process in lending process automation across verticals. Customer expectations have risen dramatically in terms of complete transparency, customer-centric, highly personalized interactions, and maximum participation. As a result, maximum fintech implementation can be seen in customer relationship management (CRM), accounts payables, mortgage automation, risk management, payment arrears, reconciliation requirements, insurance premium calculations and settlements, back office, and front office, among other areas. Aside from process integrations, the lending industry requires fintech capability to eliminate cyber fraud risks and identity thefts, as well as a digitally secured infrastructure to protect customer data related to mortgage and lending. Customers feel empowered with access to secure omnipresent, omnichannel, digital transactions, and payments when lenders provide mortgage lending automation with simplified tasks. Self-service in CRM with chatbots and instructive guidelines, according to Deloitte’s Finance 2025 report, creates a better customer interface. Furthermore, mortgage lenders can use fintech automation to absorb data from borrower application forms, extract information from borrower payroll applications, and automatically upload loan data into respective portals. More importantly, automated technology enables credit decision-making systems and microdata inspection with low error rates for seamless loan approval and disbursement. Considering this massive shift, organizations have been striving hard to develop deeper hyper-automation processes or at least implement partial automation, machine learning, and artificial intelligence to attain optimum operational efficiency. Categorically, RPA tools have also matured from traditional desktop automation to enterprise solutions. This has profusely helped in managing complex processes like strategic decision-making, cognitive learning capability, user interfaces, and so forth.  Fintech automation is undeniably booming, and competition is heating up. Companies are planning both organic (diversification, geographical expansion, etc.) and inorganic (mergers and acquisitions) strategies to gain a competitive advantage and remain sustainable. Gartner estimates that banks and investment firms will spend $623 billion on technology products and services by the end of 2022. The major investments will be in generative AI, autonomic systems, and threat-nullifying technologies. Final Thoughts During our research, we have found that CEOs across the globe believe that cloud-based ERP, cognitive technologies, and hyper-automation will radically simplify lending processes and accelerate the lending industry as a whole and not in silos. In fact, hyper automation is already in its nascent stage of enterprise adoption. Apart from this, the banking and investment services will also witness larger use of generative artificial intelligence, generative adversarial networks (GANs), and natural language generations for fraud detection, predictive analysis, synthetic data generation, artificial intelligence-backed follow-ups, and risk-factor modelling. With the help of algorithm-driven and interactive AI and robots, new service models will emerge. This will not only diversify the financial workforce, but will also link the entire organization into a real-time, digitally connected workplace. What’s Next? Tavant’s consulting-driven approach to automation helps mortgage lenders and banks significantly improve productivity and enhance customer experiences using our deep automation and domain expertise. By combining the power of industry tools and accelerators, we drive organization-wide transformation through RPA, ML, and AI to solve your most important business challenges. To learn more, visit us here or reach out to us at [email protected].

Realizing the Goal of Fully Automated Lending

Borrower expectations are still not being met by the mortgage industry. While many lenders have provided a seamless mortgage application experience by digitizing the front-end platform, the industry’s digitization remains incomplete. Many origination and servicing processes remain slow, manual, labor-intensive, and fragmented, making them vulnerable to disruption—mortgages close in 51 days on average, which is far too long in today’s fast-paced world. Underwriters and processors do not have the tools to complete their tasks efficiently and effectively. The mortgage industry has been embracing technology to streamline the mortgage application process to make the consumer experience smoother and faster. Touchless lendingTM is quickly becoming the industry’s standard operating system for large-scale automation of mid and back-office mortgage operations. How does Touchless LendingTM change the game? Despite the amalgamation of multiple technologies into the mortgage origination process, the cost of originating a loan has steadily increased over the years, reaching a peak north of $10,000. Tavant wanted to create a product that was completely, directly, and only focused on solving this problem. The vision of Touchless LendingTM is to eliminate the many humans-in-the-loop embedded in the mortgage process, to phase out the rivers of paper that flow through each loan in the application intake and decisioning process, and to knock out the need for multiple thrashes and iterations between the borrower, loan officer, processors, and underwriters, which result in an increased cycle time of anywhere from 45 to 60 days to close a loan. Touchless LendingTM targets these underserved middle and back-office associates, allowing them to make a clear-to-close decision in as little as five days, handle five times as many mortgages at once, and save more than 75% on processing and underwriting costs per mortgage. To solve the complex problem of using a machine to do the work of a senior processor and an expert underwriter, the Touchless LendingTM platform prudently employs AI and Machine Learning techniques. We combine computer vision and natural language processing with procedural rules processing to provide the best technical solution for straight-through processing, automated loan decisioning, automated loan processing and automated underwriting. The automated lending platform is LOS-independent and will work with any CRM and POS platform in the mortgage industry. The platform employs Digital Ledger Technologies to ensure that all operations on loan are immutable and can be tracked from its inception to its closure/funding, reducing repurchase risk and allowing investors to perform their due diligence when purchasing the loan more efficiently. Delivering an Exceptional Mortgage Customer Experience Touchless LendingTM is an AI-powered lending-as-a-service platform that offers straight-through mortgage processing and automated underwriting as part of the mortgage manufacturing pipeline from start to finish. Instead of relying on physical documentation and manual data entry, loan officers, processors, and underwriters use Touchless Lending’s optimized workflows to engage with data and make decisions faster. This one-of-a-kind automated mortgage software solution enables lenders to originate more mortgages more quickly while lowering costs and repurchasing risks. Touchless Lending seamlessly integrates with your existing systems, such as CRM, POS, and LOS, and automates the loan production process. Each service provided by the Touchless LendingTM platform is unique in that it includes embedded innovation that provides a true business and operational lift to that service. Touchless Documents, for example, uses a multi-OCR strategy to extract the best possible classification and data extraction from a paper document via an intelligent selection among a network of best-of-breed OCR providers. From Chaos to Order: A Perfect Mortgage CX Strategy and a Boon for Lenders First, lenders do not need to purchase the entire end-to-end platform to gain and lift their mortgage manufacturing pipeline. Individual service endpoints for Document, Income, Credit, Collateral, Asset, Title, Multi-Investor, and Fraud Analysis can be consumed independently through the platform’s API Store. Second, Touchless LendingTM services can be integrated into the Lender’s ecosystem in days or weeks rather than months, resulting in immediate benefits and an impact on the Lender’s cost and cycle times. Third, Touchless LendingTM provides: A 77% cost savings for underwriting and processing. A 4.5-fold increase in underwriting to handle more mortgages at once. Clear-to-Close decisions in as little as five days to a week. Touchless LendingTM has resulted in an 11% increase in total annual gains for lenders and significant savings in operational costs. Touchless LendingTM can accomplish this by reducing process time due to improved quality and digital loan files, lowering document processing costs, gaining warehouse line interest savings, gaining GSE interest rate arbitrage, and maximizing appraisal waiver utilization. Reinventing the mortgage customer experience: now more than ever, mortgage lenders need to focus on delivering a superior online customer experience. Lenders value the quicker time to product deployment and the seamless/intuitive integration into their existing workflows and business processes. The ROI is immediately observable and tangible and can be demonstrated through multiple real-world deployments of Touchless LendingTM services. The Touchless LendingTM platform includes an optional Underwriter’s Workstation, the most user-friendly and advanced workstation for underwriters available in any offering on the market today. Data visualization techniques, combined with AI and Machine Learning-driven insights from the borrower’s and property’s profile and characteristics, provide the quickest path to comprehending a loan’s story and thus the quickest path to loan decisioning. Although the Touchless LendingTM platform initially focuses on automating the mortgage processing and underwriting lifecycles, the platform’s goal is to automate anything and everything that can be automated in the path from the borrower’s post-application submission all the way to its destination of becoming a closed or funded loan, including post-closing activities. What’s Next To know more about Touchless LendingTM, reach out to us at [email protected] or visit us here. FAQs – Tavant Solutions How does Tavant enable fully automated touchless lending?Their AI-powered platform processes applications, verifies documents, assesses credit, and makes decisions without human intervention, integrating with multiple data sources to complete the lending process digitally. What percentage of loans can be processed through Tavant touchless lending platform?The platform can process 80-90% of standard applications without intervention, flagging only exceptional cases for manual review. What is touchless lending?A fully automated loan process using AI, machine learning, and automated workflows to process applications,

CI/CD and Security Testing Integration

Introduction CI and CD = Continuous integration & Continuous delivery OR Continuous deployment. CI/CD is the modern-day software development process in which we can release updates at any time in a sustainable way. The code changes are made frequently and dependably based on customer requests and the sprint life cycle. A CI/CD pipeline, popularly known as the DevOps pipeline, builds up code, executes tests (CI), and wisely deploys an updated application version into the following environment. It also ensures that code changes being merged into the repository are efficient to deploy into the live environment to meet the final goal, i.e., ship software with swiftness and effectiveness.     The Pros CICD is a low-risk option – as the process is completely automated. There are no manual interventions for setup or even config changes. Releases can occur in defined frequencies and with the client’s feedback. So, this ought to be a faster & optimum way. Smaller, more recurrent software releases are less disruptive and are easier to troubleshoot or roll back in case of any problem. The process with a structured manner increases productivity; a product will be released independently of other objects, and in the case of multiple series of code- we can release changes independently. This will increase development effort with productivity. A CI/CD pipeline allows teams to analyze builds and test results in detail, leaving little room for last-minute bug surprises.   The Cons Team dependencies – Infrastructure, including servers, could be managed by different teams, and when the need arises to access those, it can cause unnecessary delays. Thus, all groups need to be well coordinated with each other all the time. Procedure orientation delay– If defined for any pre-approval process in a project, like no direct access to the infrastructure, it can sometimes delay troubleshooting. New skill sets must be learned – Multiple tools to be used and vendor dependency on those require people with a different skillset in your team. This demands a severe intellectual investment to learn these tools.   Why do we need to infuse security validation in our CI/CD pipeline? Continuous integration and Continuous delivery are about speed, repetition, and automation. Development and QA teams are constantly under pressure to deliver releases as fast as possible – provide any new feature(s) or fix the critical bug(s) or an enhancement. But the need for speed repeatedly ignores the importance of security testing, which leaves you at risk of failing to secure your application. Vulnerabilities or flaws found in the live version of an application can cause a breach of confidentiality and expose the software to malicious activity, which costs time, money, and resources to fix and eventually will delay future releases. Integrated security testing makes life simpler for software development teams. That is why DevOps teams habitually embrace the concept known as DevSecOps, which promotes security integration into core DevOps practices. To lessen the chances of vulnerabilities going unobserved during the SDLC, all organizations must add security testing to their existing CI/CD pipeline. Undoubtedly, adding security checks will initially slow down your development cycle. Still, we all need to understand that these steps will improve the security of your organization’s CI/CD pipeline and adds another layer of oversight to ensure security for the end-users. Velocity is the key for every business, where security testing integration is a terrific cream over CI-CD. Thus, it is important to introduce security best practices throughout the build/release pipeline. Conclusion: It is not a secret that security is hard to get right. Still, security is the key in this technologically fast-moving world; therefore, performing security testing is no longer a preference. It should be performed frequently, especially with all critical releases, and should be added to the build/release pipeline for top results. With strong CI/CD security in place, teams can find and fix security issues without notably slowing down the pipeline flow or having to delay/roll back releases. Securing your CI/CD pipelines at every stage and environment that comprise the pipeline should be a priority for any organization that embraces DevOps.  

Pinch me… Dreamforce 2022 is back!

Is it just me, or does this year’s Dreamforce feel like a much-anticipated reunion? Part tech conference, part homecoming is how Salesforce describes one of the most anticipated technology conferences this year. And after three years of online interactions, it’s no surprise that Dreamforce 2022 is generating tremendous excitement amongst the technology crowds. But enough about everyone else! Here are my Top 5 reasons I’m looking forward to Dreamforce 2022: Everyone who’s anyone will be there.  Like on a summer beach after the exams, the crowds are expected to be everywhere. Three days of networking opportunities with over 30000 people – meeting new leaders and connecting with old friends. That’s a lot of handshaking, so don’t forget the sanitizer. In-depth sessions, larger-than-life speakers. Do you know those cinematic flash-forward sequences where the team describes how they will break into a super secure location? Everything time-coordinated and moving to peppy music.? That’s how I imagine my approach will be to attending keynote sessions this year. I know we must pick our favorites, but frankly, between celebrities, activists, and athletes, it’s hard to decide. We are talking about over 1000 potentially thought-breaking sessions. Planning, my friend. It takes planning (and the  agenda builder on the Dreamforce webpage is just what you need). Boring Demos? No, it’s a Demo Battle  An epic game show theme where Salesforce partners get a mere three minutes to showcase their tech. It’s going to be fast and exciting. And the best part? The audience gets to vote. I don’t know about you, but I’ll carry a poster saying I WANT TAVANT! Dreamfest Fundraiser  Every year, Dreamforce organizes a fundraising event which is also a chance for Dreamforce attendees to chill. This year’s concert will benefit the UCSF Benioff Children’s Hospitals. Dreamfest will take place on Wednesday, the 21st of September, at Oracle Park in San Francisco. And this year, we will be rocking to… The Red Hot Chilli Peppers!!! That’s right; the Red Hot Chili Peppers are performing at Dreamfest! And by the way, all proceeds will benefit the UCSF Benioff Children’s Hospital. See what I did there? Dreamforce will take place in San Francisco at the Moscone Center from September 20–22, 2022, and is slated as the largest Salesforce conference of the year. Appropriately, this year’s theme is ‘Go big and come home.’ I can’t wait! ABOUT THE AUTHOR: I am Simran Tayal, Director Marketing at Tavant and I’ll be at Dreamforce with my team at Hotel Zetta, 55 5th St, San Francisco. For more information, click here.  

The Rise of Streaming Analytics in the Media Industry

Compared to a decade ago, the increase in devices and quality of connectivity have transformed how we consume media. Streaming services made it possible for us to consume content continuously without the need to upload or download an entire file. Additionally, the sudden boom in OTT applications during and post-pandemic has expanded the use of media streaming platforms worldwide. This explosion in the volume of streaming content data fueled the need to understand customer consumption faster, resulting in the need for real-time analytics or streaming analytics. For example, providing recommendations in near real-time is now required, and the ability to analyze advertising data gives providers an advantage. The Evolution of Streaming Analytics In time-sensitive scenarios, real-time analytics uses newly generated data to make predictions, ask questions, and automate decision-making in the application. While previous analytical systems could run periodically (say every 24 hours), this was insufficient for time-sensitive data. In the case of streaming information, periodic analytics would be outdated by the time it is processed. Also, as data streams have no beginning or end, they cannot be broken into batches. This continuous flow of data also requires a different processing and data architecture. Streaming Analytics Processes Data Differently Streaming analytics is the processing and analysis of data flowing continuously, and it relies on real-time data. Real-time data can be streamed from transactional databases using change data capture (CDC) or from applications using an event streaming platform such as Amazon Kinesis and Kafka to data sinks. Stream processing engines are runtime libraries that help developers write code to process streaming data without dealing with lower-level streaming mechanics. It uses event stream processing, which analyses large-scale real-time information and in-motion data. Some of the most widely used stream processing engines are Apache Spark, Apache Flink, Apache Kafka, Apache Storm, Apache Samza, AWS Kinesis Streams, and Apache Flume. Real-Time Analytics Made Real Streaming analytics aims to offer up-to-date information and keep the state of data updated with very low latency. It provides real-time insights to enable more responsive decision-making. With media and entertainment companies generating vast volumes of data with every click, analytical speed is crucial. Real-time analytics or streaming analytics can help the media industry gain an advantage over competitors in the following ways:  360-Degree Customer View  Streaming analytics enables businesses to measure data usage across multiple media platforms accurately. As a result, media providers can now aggregate data sets to develop a clear 360-degree customer view. These analytical data points can even include user viewing and engagement for companies to know how long, when, and where their viewers consume their content. Apache Flink is an open-source platform that can ingest massive amounts of continuous streaming data from multiple sources, which is processed in a distributed manner on multiple machines. Apache Flink is used by King (the creator of Candy Crush Saga) to analyze their 300 million monthly users who generate more than 30 billion events every day from different games and systems. Flink offers processing models for both streaming and batch data, enabling data scientists to access these massive data streams while retaining maximum flexibility. Anticipating Viewer Churn According to Interpret’s Video Churn Today in 2021 report, SVOD subscribers increased by 14% in the second half of 2020. During the same time period, the cancelation rate increased from 15% to 20%, and nearly 20% of subscribers switched services to gain access to exclusive content. In such a volatile and highly competitive market, streaming analytics provides operators with more accurate churn prediction models. Streaming analytics brings together both real-time and historical users (including user behavior and engagement) to identify subscriber clusters with a high churn risk. Impacting Customer Experience  Media companies must be able to introduce user activation, reactivation, and engagement campaigns that get their users to continue consuming content on their platforms. Streaming analytics uses click records from various source platforms and enriches the data with demographic information to serve more relevant content to the targeted audience. Europe’s leading media and communications company, Sky, provides TV, streaming, mobile TV, broadband, talk, and line rental services to millions of customers in seven countries, and relies on the Google Cloud Streaming analytics services to deliver customer service at scale. Sky collects diagnostic data from its millions of TV boxes. By combining this set-top box diagnostic and viewing data with streamed and batched information from reference feeds, Google Cloud Streaming analytics created a data warehouse on BigQuery, to help ensure the best possible user experience. Real-time Recommendations  Today’s media consumers demand personalized, relevant, and contextual content. But with an increase in streaming services, competition for viewership is intense. Recommendation engines driven by streaming analytics can offer more customization and personalization to keep viewers coming back for more. Based on the real-time analysis of this big data, media companies can make better decisions on content dissemination. Content Usage Insights Deep big data streaming analytics is also giving media companies deeper content insights. It helps uncover which genres are in high demand, what content is preferred at which time of the day, when they pause, or what they skip. By analyzing this live data in real-time, businesses can detect and act on strategic content opportunities. Apache Spark is an example of a streaming analytics tool that makes use of a big data processing engine to provide scalable, high-throughput, and fault-tolerant live data stream processing. Online news provider Yahoo uses Apache Spark for personalizing its news. It uses Apache Spark’s streaming analytics processing to find out what kind of news users are interested in and the kind of users who would be interested in reading each news category. Troubleshooting apps, devices, and more According to video analytics solution provider NPAW, 4.9% of video-on-demand views experience some error; for live views, the number is 7.6%. While media houses offer the same service across different devices, the understanding is that the approach cannot be the same. Netflix uses the Amazon Kinesis streaming analytics solution to monitor the communications between its applications so it can detect and fix

HELOC – The Bright Side of our Turbulent Times

Volatility is the Norm It’s been a crazy two years, for many reasons. Between February 2020 and January 2022, the mortgage industry witnessed something we never thought we’d see: 30-year fixed-rate mortgages below 3.5 percent. These rates attracted a record number of refinances, with cash-out refinances reaching $1.2 trillion by 2021. Then, in what seemed like an instant, mortgage rates skyrocketed in Q1 2022, effectively ending the refi boom. Home Buyers are getting nervous; refinances are drying up and Lenders are scrambling. As interest rates and mortgage interest rates rise, consumers are turning to home equity lines of credit (HELOCs) to access a portion of the equity they have. I mean we all still want our updated bathrooms, kitchen remodels and for the lucky few backyard pools. Why Homeowners are Seeking HELOCs? HELOCs offer flexibility. Consumers are showing a growing interest in home equity loans and home equity lines of credit as a means to access more affordable capital and take advantage of rising home values. For example, where I live in Carlsbad, CA, home sale prices have increased by 64% in the past two years – That is a lot of “equity.”(source Redfin). Homeowners don’t have to borrow the entire credit line with a HELOC and are only be charged interest on the amount they do borrow. Borrowing no more than you absolutely need during times of interest rate volatility can help keep their payments more manageable. A home equity line of credit, or HELOC, is one of the best options on the market right now for homeowners looking to tap into their home equity. Lenders Need to Navigate and Embrace Change Whether you are a lender who is seeing a flood of HELOC consumers and wish to deliver a seamless borrowing experience or a lender who is planning to add HELOC to your portfolio to seek growth in a declining refinance market and cross-sell opportunities to their existing customer base- you need to be proactive. Enter a Delightful Lending Experience with Tavant FinXperience – Advancing the Future of Lending Technology with AI-Powered Digitization Tavant’s FinXperience provides personalized and configurable journeys for HELOCs and home equity installment loans through a suite of user-friendly portals and mobile companion apps. It offers: Accelerated deployment – Standard integrations with LOSs, CRMs, PP&Es, document generation, and other third-party systems enable solutions to be deployed in 6 weeks or less. Fast approval – Getting a home equity line is often cumbersome for consumers and requires lots of paperwork. However, Tavant’s FinXperience makes the process for lenders much easier, and they can offer funds in just a few days. It can just take 5-minutes to decisioning and 5-days to funding. Touchless Lending™️– How AI-powered Touchless Lending™ Simplifies, Streamlines & Saves $$ Tavant offers a seamless loan manufacturing pipeline for HELOCs through Touchless Lending™️. Touchless Lending™️ focuses on these underutilized middle and back-office associates, allowing them to make a clear-to-close decision in as little as five days, handle five times as many mortgages at once, and save over 75 percent on processing and underwriting costs per mortgage. The Touchless Lending™️ platform judiciously utilizes AI and machine learning techniques to solve the complex problem of using a machine to do the work of a senior processor and an expert underwriter. The Bottom Line: HELOC has come back as people seek alternative ways to access the equity in their homes. The rest of 2022 could be a record year for HELOCs, just as 2021 was a record year for refinancing. Understanding the dynamics of the home equity market can help mortgage lenders identify homeowners in the market for home equity. For more information on how next-gen solutions can help Fintech companies transform their businesses, visit here or mail us at [email protected]. FAQs – Tavant Solutions How does Tavant help lenders capitalize on HELOC opportunities during uncertain economic times?Tavant provides specialized HELOC platforms with real-time property valuation, flexible credit line management, and automated risk assessment capabilities. Their systems enable lenders to offer competitive HELOC products quickly, manage portfolio risk effectively, and provide borrowers with accessible credit during economic volatility. What HELOC-specific features does Tavant offer for turbulent market conditions?Tavant offers dynamic credit limit adjustments, real-time market monitoring, automated compliance management, and flexible repayment options within their HELOC platforms. These features help lenders manage risk while providing borrowers with needed financial flexibility during uncertain times. Why are HELOCs attractive during economic uncertainty?HELOCs are attractive during economic uncertainty because they provide flexible access to funds, typically offer lower interest rates than credit cards or personal loans, use home equity as collateral, and allow borrowers to access credit only when needed while paying interest only on amounts used. How do HELOCs work during turbulent economic times?During turbulent times, HELOCs provide a financial safety net by allowing homeowners to access their equity for emergencies, debt consolidation, or investment opportunities. Lenders may adjust credit limits based on current property values and market conditions to manage risk. What are the risks and benefits of HELOCs in uncertain markets?HELOC benefits include flexible access to funds, potential tax advantages, and lower interest rates. Risks include variable interest rates, potential property value fluctuations, the possibility of owing more than the home’s value, and the risk of foreclosure if payments cannot be made.

Cracking the Intelligent Automation Fintech Code

Financial services organizations operate in a dynamic and complex ecosystem where new threats and opportunities emerge. They are under pressure to automate processes, cut costs, and become more agile to remain competitive. Customers expect these organizations to provide hyper-personalized services in all places, consistently. Agile Fintech companies that bring niche services offer better customer intimacy than traditional financial services organizations. Today, businesses need to react quickly to market changes and customer expectations. Being nimble is critical at the moment. Unleashing the Power of People + Next-gen technology (RPA, Machine Learning, and AI) with Intelligent Automation Automation methods have gone hand in hand with the changing nature of work. RPA, artificial intelligence, and machine learning have the potential to make business processes more innovative and efficient. A recent survey by McKinsey reveals that companies that experimented with intelligent automation could automate up to 70% of the repetitive tasks that their employees were handling. Still, they could also see a 20–35 percent run-rate of cost efficiencies. AI collects data from multiple sources and feeds it to tools to enhance the value of their interactions. RPA adds value by automating structured, data-driven processes that previously required manual intervention. Each provides value on its own. Bringing the two together (i.e., IA) adds enormous value in developing solutions that use a technological knowledge base to modernize processes as well as interactions between applications. The resulting solutions are faster and more accurate, contributing to the four significant efficiencies: Better productivity: More efficient planning cycles can be achieved through the real-time integration of multiple sources of structured and unstructured data, automation of applications and processes, and decision-making and prediction. Increased precision: The combination of structured and unstructured data can ensure better decision-making; it also helps automate repetitive, manual processes and requires less human intervention, leading to more precise results. Cost savings: According to Deloitte, businesses anticipate an average cost reduction of 22% from intelligent automation. They realized, however, that organizations ramping up intelligent automation have already realized an average cost savings of 27% from their implementations thus far. Enhanced CX: Businesses that leverage digital technology can comprehend their customers’ needs, communicate more effectively, and produce higher-quality products.   Final Thoughts Intelligent automation does not refer to a single technology. In its place, it indicates various sets of automation tools that can resolve complex problems. Intelligent automation does more than automate isolated processes; it also catalyzes the actual process and workflow transformation. The payoff can be pretty substantial in terms of increased productivity, streamlined processes, and exceptional customer service. Over the last decade, the evolution of RPA (robotic process automation) has propelled us to the forefront of workforce unification through people + intelligent automation. At this stage of development, digital robots not only automate back-office processes but also complement, augment, and interact with your human workforce via human-in-the-loop capabilities such as AI, machine learning, and optical character recognition. Thus, how can businesses move from basic RPA to enterprise intelligent automation while ensuring the long-term viability of their legacy systems? What’s Next? Reality check: There’s no time for “cookie-cutter” monotony Break it with Tavant’s Intelligent Process Automation Tavant’s consulting-driven approach to automation helps mortgage lenders, banks and real estate companies improve productivity and enhance customer experience with our deep automation and domain expertise. By combining the power of industry tools and accelerators, we drive organization-wide transformation through RPA, ML, and AI to solve your most important business challenges. Key steps on the journey include: A Phase of Discovery and Planning: We begin with a maturity assessment to develop a comprehensive digital blueprint of all process activities that align with your business priorities. A Quick Automation Assessment: A quick assessment can help you understand immediate automation priorities, cost-saving opportunities, and the best-integrated automation framework for your needs. Assess and Build: Organizations must evaluate various technology, architecture, security, and governance solutions to determine which options are available to automate. We can help you decide how to use it, which technologies to leverage, and how to ensure that it is widely used throughout your organization. Optimize and Manage: By establishing a new human/digital partnership, we can simplify, standardize, transform, automate, and optimize business processes.   For more information on how intelligent automation can help Fintech companies transform their businesses, visit here or mail us at [email protected].  

Why Cloud and Data Analytics go hand in hand?

As per Gartner, adoption of data and analytics will increase from 35% to 50% in 2023, driven by industry vertical and domain-specific augmented analytics solutions. By 2024, 75% of organizations will have deployed multiple data hubs to drive mission-critical data and analytics sharing and governance. The research also highlights that nearly 70% of enterprises will use cloud and cloud-based AI infrastructure to operationalize AI systems for their businesses over the next two years. Cloud adoption has significantly accelerated post-pandemic, with enterprises increasingly focusing on the digital transformation across their business functions. One of the critical drivers in cloud adoption is the onset of data-driven strategy across industries. Cloud has helped in the paradigm shift to implementing data and analytics solutions and fast-tracked the time to market for data analytics solutions.     A recent survey by IDG Research and Tavant indicates that data analytics is a key focus area for organizations across industry verticals in the USA, where enterprises are looking at leveraging the cloud to implement data-driven systems. More than 80% of C-level survey respondents plan to leverage the cloud to drive enterprise data analytics. Thus, it is evident that cloud technology is pivotal in driving faster data analytics adoption, the emergence of next-gen SaaS products, and modern-day cCloud data platforms. Role of Cloud in Data Analytics: With the wide range of solutions focused on infrastructure and data analytics-specific services, the cloud has acted as a catalyst in driving the adoption of Data analytics. Today, Cloud Service Providers (CSPs) are accelerating the data analytics adoption with broadly two service offerings; Infrastructure services – The fundamental cloud Compute and Storage solutions help organizations implement custom solutions faster and address scalability challenges. The mere availability of computing and storage faster elasticity has enabled enterprises to adapt quickly. Modern data platforms also leveraged infrastructure services in providing cloud-agnostic services. Data Analytics services – CSPs are leading cloud providers to provide data-specific services to build Cloud-native data solutions. Examples are Hadoop on Cloud as PAAS – AWS EMR, Azure HDInsight, GCP Dataproc, and the related services to create a complete data solution. The CSPs will glue these cloud components together to build custom solutions for future enterprises.   Cloud-enabled Data Analytics solutions As organizations embark on building complex data solutions, the cloud becomes an integral component of the data architecture. Understanding the various alternatives helps select the right technology based on business context. Below is the broad category of cloud-driven solutions. Cloud infrastructure-focused data solution  The solution leverages cloud infrastructure services to deploy data analytics solutions faster. These solutions are most suited for companies that need to rehost existing data solutions from an on-premises environment to the cloud or build a custom solution from scratch. Examples include AWS S3/Azure ADLS/GCP cloud storage as the data lake and various computing services by AWS/Azure/GCP Cloud-specific data solutions  These solutions leverage cloud-native data services to build data analytics solutions. The data services and pre-built integrations across different cloud services are helpful for enterprises and CSPs to co-create custom solutions faster with data privacy and security needs. Examples include EMR, Kinesis, S3, from AWS, HDInsight, ADLS, NoSQL databases, Stream Analytics from Azure, and Dataproc, Storage, NoSQL DBs, Pub/Sub from GCP. Cloud Datawarehouse Cloud-native Datawarehouse solutions by CSPs help to deploy enterprise-grade Datawarehouse faster. Examples include AWS Redshift, GCP BigQuery, and Azure Synapse analytics, which have pParallel processing, pre-built integrations for ingesting data, and AI/ML capabilities. Modern data platform on cloud -Modern Cloud-native data platforms like Databricks and Snowflake focus on building a single platform addressing the needs of Data Analytics.   As cloud and data analytics drive the adoption of each other, it is imperative to understand the mutual dependence and leverage it while planning for cloud adoption or data analytics solutions within the organizations.

Is It Essential for Lenders and Banks to Embrace Quality Engineering to Achieve Speed and Agility?

Why is good quality engineering important in financial services? Lenders, banks, and insurance companies are increasingly replacing legacy systems and adopting improved technologies across the enterprise, which requires the highest quality engineering and software testing capabilities. Unsurprisingly, their development initiatives are centered on the need to improve efficiencies, add new functionality, and reduce operating costs. It may offer, develop, and bring products to market or incrementally replace existing platforms and solutions while minimizing any business disruption during major or minor release cycles. Quality Engineering must be part of any effective change program to proactively prevent software errors, misfires, malfunctions, and defects that can cause outages, negative client impacts, and regulatory fines. Today’s business demands are numerous and complicated. What do lenders and banks want?  A faster time-to-market, including a shorter turnaround time for application rollouts and updates that can keep up with rapidly changing market trends. To reduce costs, as they face increasing pressure to reduce the cost of IT projects and seek intelligent alternatives to reduce project costs. To keep up with technological advancements and the demands of integrated applications that support multiple operating systems and devices. Application stability, which can significantly facilitate an increase in clients and support online exposure demands with zero application downtime. This is where Quality Engineering enters the picture! As stated, “Assurance neither improves nor guarantees quality. It is too late to assure. Quality, good or bad, is already present in the product. To truly meet your customers’ expectations, you must implement a quality engineering approach that instills quality at every stage of the SDLC”. Given the high risk of financial services, quality is a business-critical requirement. As a result, lenders and bankers must adopt a quality-first approach in their software development lifecycle. Quality Engineering entails QE involvement from the start of the SDLC so that quality-related processes run concurrently with development until the final release. This is undoubtedly impossible to accomplish manually, necessitating test automation. The shift-left strategy refers to moving QE to the early stages. However, shifting to the left is no longer sufficient in today’s constantly changing customer demands and volatile financial markets. Quality should be omnipresent, necessitating a shift-everywhere QE strategy. A shift everywhere strategy and a Quality Engineering approach result in an application that scores highly on all key parameters such as functionality, security, reliability, and performance, among others. As businesses look to automate more of their business operations through technology, a well-designed QE plan should include an in-depth and broad-based performance testing plan that identifies trouble spots, recommends solutions that can then be properly implemented, and provides continuous testing. With a shorter time to market, enterprises now have less time to test.  What’s next? Tavant – An Absolute Commitment to Quality Engineering Tavant’s QE approach focuses on testing and combines industry best practices with our own methodologies and powerful proprietary tools to guide clients through an ever-changing development environment. Tavant’s Quality Engineering (QE) programs aim to improve the quality of software development and incremental release cycles while avoiding serious technology failures that could have a negative business and brand impact. Our QE experts use a quality management process to ensure that a product/service/platform meets all required specifications as well as all desired operational functionality.  Our engineers adhere to a robust process-driven strategy that facilitates and defines specific design goals concerning product/platform/system development roadmaps. Our goal is to track and resolve all bugs, blockers, coding errors, and other issues that may arise and should be addressed before they have a negative business impact.  Tavant’s Quality Engineering services are designed to address such challenges throughout the software development and delivery lifecycle. We use the CI/CD approach to ensure faster and higher-quality testing.  Rather than relying solely on DevOps for iterative QE, Tavant advises customers on how to establish a dedicated QE strategy and focused action plan that seeks to mitigate and/or eliminate identified risks, enable compliance, and minimize costs. Financial quality engineering services and banks have used QE to test technology deployments for bugs and defects and measure them against internal business and security standards and regulatory mandates through rigorous and thorough performance testing. At the same time, this may satisfy many.  Tavant fintech quality engineering services works differently and strives for excellence rather than just meeting minimum standards. We believe speed and accuracy go hand in hand. We appreciate thoroughness, accuracy, and identifying and resolving problems through a well-planned, phased, and executed testing and solution-driven schedule that includes a rigorous back-end testing component. We reimagine software testing for the age of disruption with a ready-to-use test automation platform and a suite of tools and accelerators. Through high-velocity automation, our team helps you spend less time on routine tasks while gaining more insights from data for greater innovation. We elevate testing to the next level by implementing quality engineering throughout the entire lifecycle, from code quality and pipeline quality gates to performance, resiliency, post-production coverage feedback, and everything in between. For more information, visit here or reach out to us at [email protected]. FAQs – Tavant Solutions How does Tavant implement quality engineering for lending institutions?Tavant employs comprehensive quality engineering including automated testing, continuous integration, performance monitoring, and security validation. Their approach ensures rapid deployment while maintaining high reliability and compliance standards. What quality engineering services does Tavant provide to achieve lending agility?Tavant offers test automation frameworks, DevOps implementation, quality assurance consulting, performance optimization, and reliability engineering services that enable faster time-to-market without compromising quality. What is quality engineering in financial services?Quality engineering in financial services encompasses automated testing, continuous quality monitoring, risk-based testing, performance optimization, and security validation to ensure reliable, compliant, and high-performing financial applications. Why do banks need to focus on speed and agility?Banks need speed and agility to compete with fintech companies, meet changing customer expectations, respond to market opportunities quickly, and adapt to regulatory changes in the rapidly evolving financial landscape. How can traditional banks become more agile?Banks can become more agile through cloud adoption, automation, DevOps practices, API-first architectures, continuous integration, and cultural transformation toward iterative development and customer-centric innovation.

Web 3.0 – A Game Changer for Advertisers

Advertising has been evolving by leaps and bounds over the last few decades. As a result of technological advancements, we are now witnessing the shift of advertising from traditional forms to digital avenues. While advertisers seek to increase conversions, consumers demand data ownership and transparent information usage. Web 3.0 and the metaverse have provided a solution to this long-standing demand, and they have the potential to be game changers for both advertisers and consumers. Revolutionary Web3 The current system, web 2.0, has many major flaws, such as the big tech controlling the internet with an iron fist, a lack of transparency, and the involvement of a plethora of intermediaries. The transition to Web3, the most recent version of the internet, will provide enormous benefits, with advertisers playing a significant role. Blockchain, the underlying technology of web 3.0, helps advertisers improve data transparency, eliminate intermediaries, and directly connect brands to their consumers while saving millions of dollars. International brands have already begun to adopt web3 and complementary technologies such as the metaverse by hosting events, sponsoring, and creating unique user experiences. In web3, the focus will shift from improved visibility to enhanced user experience and relevant messaging by giving advertisers complete control over their data and providing meaningful value to their users Advertising – Changing Dimensions Role of interoperability – Interoperability is an important concept in Web 3.0. The initial prototype of Web 3.0 is based on shared platform experiences. Users can carry their avatars and digital profiles across multiple applications and websites while maintaining a unified experience. With interoperability, advertisers would have unprecedented freedom to engage with potential customers. Metaverse real estates – Since its inception, metaverse real estates have experienced rapid growth. Platforms such as Decentraland and Sandbox have grown exponentially in months. As this trend continues, businesses will need to consider metaverse real estates essential to their advertising strategy. Soon, advertisers’ primary metric of campaign success will be metaverse traffic. Cross-platform collaborations – The ownership of digital rights has changed how consumers interact with segments such as gaming, entertainment, etc. Data is the next stage in this transformation. Customers can finally take control of their data and decide how it will be shared and used on the internet with Web3. A shift in digital tools – With Web 3.0 and metaverse, the tools used for advertisements are expected to evolve. Advertisements in virtual reality – VR has primarily remained a secluded medium for advertising. However, as users move away from text and video-based interactions, businesses should increase their focus on VR advertisements. In-game advertisements – The play-to-earn economy is an important part of the metaverse. Companies should explore 3D rendered advertisements within games by determining how to work on in-game ads without interfering with the customer experience. User-driven advertising – Because of the transparency of blockchain, a significant shift toward ethical marketing is required by obtaining explicit consent from users before using their data. This will allow users to receive a portion of ad revenue. User-driven sharing will enable businesses to reach their target audience without relying heavily on previous data collection models. Looking Ahead Though Web3 is still in its infancy, advertisers have already begun to see the metaverse as a profitable channel for engaging with audiences and marketing. Decentralization is fast emerging as the internet’s future. With the aggressive growth expected in Web3, advertisers are expected to explore newer ways to engage with modern audiences and capitalize on the opportunities in the Web 3.0 era.

Connected Service Life-Cycle Management – A data-driven approach to service operations

The immense potential of aftermarket services for the manufacturing industry is a no-brainer. As per industry standards. Aftermarket services comprise 25-30% of the revenues, with a profitability of up to 55% Service parts management is around 15-20% of the revenue, with profitability of up to 50% Service contracts are high margin businesses with a potential to earn anywhere between 30 to 50%     According to a recent Deloitte study, the role of aftermarket services in driving customer lifetime value (CLTV) and sustainable profits has become more profound post-COVID-19. With supply chains being disrupted, the service level expectations of customers, especially for complex products, manufacturing, construction machinery, and transport vehicles, have risen manifold. Customers are willing to pay a premium for uninterrupted services and longer-term contracts that can predict support or replacement proactively before their equipment becomes inoperative. It is a new win-win for both OEMs and customers. Deciphering the aftermarket SLM ecosystem of a manufacturer The case for aftermarket services sounds promising, but does it manifest? Does the transition to SLM translate into tangible business gains? What do OEMs need to realize the true potential of their aftermarket services? Currently, a manufacturer’s aftermarket SLM tech stack can have one or many of these components, independent of each other. Service Parts Management: Covers the spectrum of aftermarket parts sales, from direct customer sales, dealer sales, and service centers to custom programs. Warranty Management: End-to-end management of product warranty processes involving product registration, claims processing, contract management, service plans, returns control, and warranty analytics. Field Service Management (FSM): Provide resources to support products in operation at the customer’s point of use. Capabilities span asset management, mobile workforce management, customer portals, service request management, and contract management to ensure the right resources are delivered at the right time. Service Knowledge Management: Manage, collect, and report on every aspect of customer interactions, including online portals, call center operations, training programs, and product health monitoring. Service Network Management: Plan, manage, and expand service operations through organic capabilities to transform service strategies across MRO operations, component repair & exchange, product modifications, and service delivery. Technical Information Management: Technical information storage about design, bill of materials (BOM), reliability data, parts information, configuration data, maintenance data, and production data to lay the foundation for the life-cycle and performance management of a product.   On a standalone basis, these systems are certainly helping manufacturers transform their processes. Still, this siloed approach is incapable of value creation as it tends to ignore the complementarities and interdependencies across the ecosystem – OEMs, suppliers, dealers, customers, and service centres. Not only that, but the multiple system approach also leads to a growth slump, as it cripples OEMs’ ability to see complete and accurate data and deploy that data to build a seamless experience for their customers and gain a competitive advantage. As modern enterprises focus heavily on keeping track of their customers’ needs and aim for proactive service delivery to meet their satisfaction levels and drive customer lifetime value over the life-cycle, the need to implement connected SLM has become more pronounced than ever. From SLM to connected SLM – A case for manufacturing Using AI and analytics to create a 360-degree view of the service life-cycle processes for manufacturers, their channel partners, and customers Let’s look at an industrial equipment manufacturer that faced challenges across its service supply chain. The manufacturer wanted to eliminate inefficiencies and ensure maximum service parts availability across its global operations. This required evolution from a location-based inventory model to a centralized inventory management model, which could predict parts requirement, intelligently analyze parts availability, and automatically allocate resources per customer demand. The journey began with designing, building, and implementing SLM solutions to serve use-cases built around industry-specific challenges. The next step was integrating SLM with existing ERP and SAP systems and using analytics and AI to leverage real-time orders and feed them to SLM systems to ensure optimal inventories. This helped the manufacturer drive inventory turnover by 18-20%, increase parts availability by 3-5%, and save inventory costs by millions. Manufacturers must explore the integration of artificial intelligence (AI), the internet of things (IoT), and analytics tools across processes. IoT devices, or connected devices, help automate data collection from operational equipment to gauge product performance and uptime and diagnose problems. AI and analytics deliver capabilities to derive insights across system uptimes, inventory, service needs, and other functional areas. Unlocking the value of the Convergent SLM Strategy A connected SLM strategy can help build end-to-end interconnected systems that drive optimization across all manufacturing operations. A transition from a pure-play SLM strategy to a connected SLM one enables manufacturers to collect data from field assets, warranty systems, parts management systems, and FSM. This data can be utilized to implement service updates, manage complex technical information, and drive a seamless service experience for end customers. Some other benefits include: Streamlined workflows: Connected SLM solutions can enable organizations to streamline workflows with smart connected products, reduce downtimes, reduce service response times, enhance first-time fix rates, optimize price and parts availability, and reduce costs. Building new service models: Connected SLM solutions can also deliver insights into how products are performing at the customer’s point of use, which can be leveraged to build new service models. Personalizing communication: The connected SLM solutions enhance communication channels by ensuring detailed information is available and curated as per stakeholder needs to perform reactive and proactive service activities. Implementing a feedback loop across the digital thread enables manufacturers to leverage data that serves as input to increase product serviceability and reliability.   Manufacturers must explore new revenue streams from real-time engagements with end customers. Smart devices and connected SLM systems will provide capabilities for manufacturers to deliver value-added services, reduce service and parts costs, and adopt a data-driven approach to decision-making.

Are Mortgage Lenders Saving Big by Adopting Intelligent Automation and AI?

In 2020, when the pandemic hit the world, it started a wave of rapid digital changes that spread across the globe. In 2021, these changes were put into place. It took a lot of money for businesses around the world to change so that they could work from home, be more socially isolated, and do business in a way that may never be the same again. In 2022, it’s clear that those changes will stay. The technology that is easy for people to use is getting a lot of attention again. Trends are likely to become the norm in the future. AI in Fintech market size is expected to reach $17 billion by 2027, and it’s no surprise that AI and ML (machine learning), and Intelligent automation will be at the heart of this. The only question is, how do fintech companies use these tools to make digital transformation happen and make it work for them? Fannie Mae’s quarterly Mortgage Lender Sentiment Survey® conducted a research among senior mortgage executives in August 2021 to better understand lenders’ views on AI/ML technology and to see how interested they were in different AI/ML applications. The study revealed the following key findings: Most lenders (63%) say they know about AI/ML technology, but only about a quarter (27%) have used or tried AI tools for their mortgage business. Lenders expect to use some AI tools in two years. Lenders who already use AI/ML technology say they mostly use it to make their operations more efficient or improve the customer/borrower experience. People use it to apply for a loan, get a loan, and get it approved. The biggest problems for lenders who haven’t used AI or ML technology are integration issues, high costs, and not having a proven track record of success. AI/ML applications that help businesses run more efficiently are the most appealing to lenders.  Lenders found the concept of “Anomaly Detection Automation” to be the most appealing. “Borrower default risk assessment” came in a close second, though.   There are solutions, but they are task-oriented rather than holistic. In terms of customer-facing solutions, 75% of organizations say AI supports or drives one. This high figure is reached by combining distinct procedures. Next to loan applications, AI is used for documentation, marketing, and closing. Overall, 83% have at least one AI-powered back-office solution. The top three most reported sub-processes are loan servicing, title search/registration, and underwriting. Mortgage lenders are saving big by automating their manual, time-consuming cumbersome legacy systems and process; thereby increasing cost efficiency and productivity. How AI, ML, and Intelligent Automation Technologies are Game Changers in the Fintech Industry? Cost Reduction and Scalability to Support Growth Given the changing market, more lenders are turning to digital financing. AI and ML deliver a significant gain compared to utilizing only normal statistical models. This invention is at the forefront of sustaining transparency and performance. In response to changes in data and outliers, AI/ML models require less manual intervention, enhancing overall efficiency. By understanding mortgage application information more precisely and quickly, AI and automation can replace optical character recognition (OCR). AI can also read text from emails, documents, and other sources. An AI-powered support automation technology optimizes loan processing by enhancing customer satisfaction and communication between lenders and borrowers. Save Time and Reduce Errors AI eliminates human errors and uses machine learning to improve accuracy. This is huge for the mortgage business. Errors in human data entry have a high cost. AI can handle mortgage papers fast without getting tired or bored, leading to calculation or judgment errors. Enhance Customer Experience (CX) AI-powered chatbots can quickly answer borrowers’ questions and guide them through the loan application process. Mortgage lenders can use AI to quickly gather information from borrowers (for example, their credit scores or student loans). Mortgage businesses start the mortgage procedure and offer superior goods for those consumers. Based on their income and credit history, a company can predict which customers are at higher risk for defaulting, enabling them to offer different types of better loans for those individuals. Improve Efficiency through Intelligent Automation  Machine learning, data analytics, neural networks, and other AI-based technologies can greatly improve financial technology. AI is becoming crucial in lending. It is bringing new efficiency and value to Fintech. For example, AI can write expense reports faster and with minor inaccuracies than a human. Also, AI may power technologies that help human workers track and automate operations, including compliance, data input, fraud, and security, while also learning from and verifying events for anomalies. Deliver Great Customer Service Consistently Customer service is one of the most notable areas where AI has benefited Fintech. Artificial intelligence has advanced to where chatbots, virtual assistants, and other AI interfaces can consistently engage with customers. Answering basic questions can significantly reduce front office and helpline expenditures. Wrapping up: COVID-19, as a whole, is proving to be an effective catalyst, with the ability to inspire industry leaders to reinvent their digital strategy. AI adoption is growing: more businesses are catching up, familiarizing themselves with innovative tools, and starting to explore new capabilities. This is a good time to start assessing the impact of AI, ML, and intelligent automation on their mortgage business. What next? Tavant can help mortgage lenders diversify how they do business and effectively unlock savings with next-gen digital technologies. To gain more insights, reach out to us at [email protected] or visit here. FAQs – Tavant Solutions How much can mortgage lenders save by implementing Tavant intelligent automation?Mortgage lenders using Tavant intelligent automation typically achieve 60-80% reduction in processing costs, 70% faster loan approvals, and 50% decrease in manual errors. ROI is often realized within 6-12 months of implementation. What cost-saving automation features does Tavant provide for mortgage lenders?Tavant offers automated document processing, intelligent underwriting, compliance automation, and workflow optimization. These features eliminate manual tasks, reduce staffing needs, and minimize compliance penalties while improving loan quality. How much money can lenders save with automation?Lenders can save 30-70% on operational costs through automation, including reduced labor costs,

5 Questions that can help Maximize Your Customer Experience

In 2020, when the pandemic hit the world, it started a wave of rapid digital changes that spread across the globe. In 2021, these changes were put into place. It took a lot of money for businesses around the world to change so that they could work from home, be more socially isolated, and do business in a way that may never be the same again. In 2022, it’s clear that those changes will stay. The technology that is easy for people to use is getting a lot of attention again. Trends are likely to become the norm in the future. AI in Fintech market size is expected to reach $17 billion by 2027, and it’s no surprise that AI and ML (machine learning), and Intelligent automation will be at the heart of this. The only question is, how do fintech companies use these tools to make digital transformation happen and make it work for them? Fannie Mae’s quarterly Mortgage Lender Sentiment Survey® conducted a research among senior mortgage executives in August 2021 to better understand lenders’ views on AI/ML technology and to see how interested they were in different AI/ML applications. The study revealed the following key findings: Most lenders (63%) say they know about AI/ML technology, but only about a quarter (27%) have used or tried AI tools for their mortgage business. Lenders expect to use some AI tools in two years. Lenders who already use AI/ML technology say they mostly use it to make their operations more efficient or improve the customer/borrower experience. People use it to apply for a loan, get a loan, and get it approved. The biggest problems for lenders who haven’t used AI or ML technology are integration issues, high costs, and not having a proven track record of success. AI/ML applications that help businesses run more efficiently are the most appealing to lenders.  Lenders found the concept of “Anomaly Detection Automation” to be the most appealing. “Borrower default risk assessment” came in a close second, though.   There are solutions, but they are task-oriented rather than holistic. In terms of customer-facing solutions, 75% of organizations say AI supports or drives one. This high figure is reached by combining distinct procedures. Next to loan applications, AI is used for documentation, marketing, and closing. Overall, 83% have at least one AI-powered back-office solution. The top three most reported sub-processes are loan servicing, title search/registration, and underwriting. Mortgage lenders are saving big by automating their manual, time-consuming cumbersome legacy systems and process; thereby increasing cost efficiency and productivity. How AI, ML, and Intelligent Automation Technologies are Game Changers in the Fintech Industry? Cost Reduction and Scalability to Support Growth Given the changing market, more lenders are turning to digital financing. AI and ML deliver a significant gain compared to utilizing only normal statistical models. This invention is at the forefront of sustaining transparency and performance. In response to changes in data and outliers, AI/ML models require less manual intervention, enhancing overall efficiency. By understanding mortgage application information more precisely and quickly, AI and automation can replace optical character recognition (OCR). AI can also read text from emails, documents, and other sources. An AI-powered support automation technology optimizes loan processing by enhancing customer satisfaction and communication between lenders and borrowers. Save Time and Reduce Errors AI eliminates human errors and uses machine learning to improve accuracy. This is huge for the mortgage business. Errors in human data entry have a high cost. AI can handle mortgage papers fast without getting tired or bored, leading to calculation or judgment errors. Enhance Customer Experience (CX) AI-powered chatbots can quickly answer borrowers’ questions and guide them through the loan application process. Mortgage lenders can use AI to quickly gather information from borrowers (for example, their credit scores or student loans). Mortgage businesses start the mortgage procedure and offer superior goods for those consumers. Based on their income and credit history, a company can predict which customers are at higher risk for defaulting, enabling them to offer different types of better loans for those individuals. Improve Efficiency through Intelligent Automation  Machine learning, data analytics, neural networks, and other AI-based technologies can greatly improve financial technology. AI is becoming crucial in lending. It is bringing new efficiency and value to Fintech. For example, AI can write expense reports faster and with minor inaccuracies than a human. Also, AI may power technologies that help human workers track and automate operations, including compliance, data input, fraud, and security, while also learning from and verifying events for anomalies. Deliver Great Customer Service Consistently Customer service is one of the most notable areas where AI has benefited Fintech. Artificial intelligence has advanced to where chatbots, virtual assistants, and other AI interfaces can consistently engage with customers. Answering basic questions can significantly reduce front office and helpline expenditures. Wrapping up: COVID-19, as a whole, is proving to be an effective catalyst, with the ability to inspire industry leaders to reinvent their digital strategy. AI adoption is growing: more businesses are catching up, familiarizing themselves with innovative tools, and starting to explore new capabilities. This is a good time to start assessing the impact of AI, ML, and intelligent automation on their mortgage business. What next? Tavant can help mortgage lenders diversify how they do business and effectively unlock savings with next-gen digital technologies. To gain more insights, reach out to us at [email protected] or visit here. FAQs – Tavant Solutions How does Tavant help lenders maximize customer experience through strategic questioning?Tavant provides customer experience analytics tools that help lenders identify the most impactful questions to ask borrowers. Their platform includes survey integration, feedback analysis, and customer journey mapping that enables lenders to understand customer needs better and optimize their lending processes based on customer insights. What customer experience optimization features does Tavant offer?Tavant offers real-time feedback collection, customer satisfaction scoring, journey analytics, personalization engines, and predictive customer service tools. Their platform helps lenders identify pain points, measure satisfaction at each touchpoint, and implement improvements that enhance the overall borrower

How to Enhance the Online Mortgage Lending Experience

The old school: A Time-Consuming Manual Processes  A recent study on mortgage lending hints that customer experience can be quite the differentiator when choosing a lender. These surveyed first-time homebuyers didn’t base their decision solely on “who gives me the lowest interest rate?”. Unexpectedly, they reported that finding positive testimonials and online reviews from other customers helped them draw the final decision. It implies that you cannot overlook the importance of enhancing customer satisfaction as a mortgage lender. Your loan applicants should be walked through the process effortlessly and quickly before they decide to slip through the cracks, which leads to your competitor. As the world moves over to digital means, lending models, too, have shifted. It isn’t just enough to be an online mortgage provider; you have to ensure there are no leaks in your digital lending bucket. Here are 4 key strategies for how you can improve your online mortgage lending experience:  Omnichannel marketing strategy:   Your leads may be coming in from multiple avenues (mail, call, social media, etc.); however, the end-to-end digital experience of your brand has to be unanimous, consistent, and picking up from the last point. Repetitions can bore and repel. You have to make your portal a consumer-centric portal. How you can do that in mortgage lending by providing them all that they need right on their screen. Tavant VΞLOX NXT is one such intelligent AI-powered tool that allows you to streamline the process from application to sanction. It has a proven track record of 78% year-on-year growth in loan origination and helps close the loan processing cycle 52% faster. Make the Process/UI simple and approachable:  The mortgage is, anyway, a complex topic for a layman, and with all the jargon involved, you could still get a lead in making the niche consumable. There are many blogs, video tutorials, and helpful e-guides out there on the process, yet the complexity around the niche persists. Use your digital mortgage model to educate leads in the simplest way possible. It helps you build your authority as a domain expert. You should also keep your website design approachable. Too much text or CTAs would overwhelm a visitor. The need for any business today is to provide clear action points. Rather than providing your number where they can call, give them the ease of a single “click to call” button, which saves the effort of copying and dialing your number. This is just one of the many smart ways you could make the process faster and easier. Draw each customer persona:  For lenders, no two customers are alike. You may have first-time homeowners, repeat customers, brokers, corporates, etc. Furthermore, they come with a separate budget, purchasing capacities, and credit score. It is thus important for mortgage lenders to filter their leads and devise unique marketing strategies fitting for each bracket as much as possible. Automate: The verification and underwriting process that goes into loan applications may take days. However, we understand that it’s a much-needed formality, although it takes hours for lenders. This makes the process lengthy for customers and adds to unpleasant anxiety. However, some tools can now help you cut down on the underwriting process. You can use AI to fully verify the authenticity of the documents and prepare the correct reports needed. This would save you and the buyer a good 3 days. Time is a crucial factor when dealing with the modern consumer, who is used to instant gratification products. You could use Tavant’s touchless lending services, which allow you to automate processes like income and credit verification, updating the loan file application, and more. Doing so can cut down underwriting expenses by a whole 52%. Conclusion  Digital is the future, and perhaps, even the present, of the mortgage industry. To stay relevant and most chosen, even the biggest lenders have shifted to digital. Tavant can be your partner in that move and turbo-charge this process, creating unforgettable satisfying customer experiences. TAVANT VΞLOX enables lenders to thrive through digitization and helps borrowers fulfill their dream of homeownership. FAQs – Tavant Solutions What features does Tavant offer to enhance online mortgage lending experiences?Tavant provides intuitive application interfaces, real-time rate calculators, automated document collection, instant pre-approval systems, and personalized loan officer matching to create seamless online mortgage experiences. How does Tavant optimize conversion rates for online mortgage applications?Tavant uses behavioral analytics, progressive disclosure techniques, smart form optimization, abandoned application recovery, and A/B testing to maximize online mortgage application completion and approval rates. What makes a good online mortgage experience?A good online mortgage experience includes easy navigation, quick pre-approval, transparent pricing, regular status updates, mobile optimization, secure document upload, and access to human support when needed. How long should an online mortgage application take?An optimized online mortgage application should take 15-30 minutes to complete initially, with instant pre-approval and final approval within 7-21 days depending on loan complexity and documentation requirements. What documents are needed for online mortgage applications?Required documents typically include income verification, bank statements, tax returns, employment letters, asset documentation, and identification. Digital platforms often automate collection of many of these documents. How does Tavant help traditional lenders adopt fintech automation?Tavant provides comprehensive automation platforms including API-first architectures, microservices frameworks, and cloud-native solutions that enable traditional lenders to match fintech speed and efficiency while maintaining their regulatory expertise. What automation advantages does Tavant offer over pure fintech competitors?Tavant combines fintech innovation with deep regulatory knowledge, enterprise-grade security, and scalable infrastructure. They offer proven solutions that balance automation with compliance requirements for established financial institutions. How is fintech automation disrupting traditional lending?Fintech automation enables instant decisions, reduces costs, improves customer experience, and allows new entrants to compete with established banks by offering faster, more convenient lending services. What processes can be automated in lending?Automated processes include credit scoring, document verification, income validation, fraud detection, loan approval, fund disbursement, payment processing, and customer communication throughout the loan lifecycle. Will automation eliminate jobs in the lending industry?Automation will transform jobs rather than eliminate them, shifting focus from manual processing to customer relationship management, complex

The Joy, Comfort, and Stress-Reducing Power of Disclosure Automation Solution

The old school: A Time-Consuming Manual Processes  Many businesses are reconsidering how they manage their disclosure process because of digital transformation and increased demand for hyper-personalized products and services. Required disclosure distribution can account for over 60% of all communications sent to customers in complex regulatory environments. The often manual, inconsistent, and inefficient distribution processes can be costly and risky: even a minor error or typo delivered to a customer can have serious legal and monetary ramifications. Also, hours are spent manually copying and pasting data from source systems or different spreadsheets and moving files back and forth across uncontrolled channels such as email, putting the integrity of your operation in danger. Manual report assembly and review stages reduce process agility while raising the chance of reporting inaccuracy, besides squandering money and time.   Finding a Needle in a Haystack Increased compliance risk because of errors, backlogged updates, poor visibility, and low auditability are common challenges of unrefined disclosure management processes. Ineffective use of technology resources and employees Long cycle times stymie marketing efforts and limit scalability Inconsistent channel experiences throughout the customer’s journey; and Loss of speed to market Tavant’s Disclosure Automation–How Does It Work? Tavant’s Disclosure Automation is a tool developed specifically for ICE’s loan origination system, Encompass. It automates the distribution of loan disclosures and closing documents. Using ICE’s “Send Encompass Docs” APIs, this process is done automatically, saving time, ensuring that compliance disclosures and documents are validated the same way, eliminating the need for humans to do it. When an order is placed, the documents are prepared and transmitted for e-signature to either ICE’s consumer connect borrower portal or Tavant’s FinXperience platform. The disclosure is then mailed to all recipients (borrower pair(s), NBO (s), and loan officer). Tavant’s Disclosure Automation can be configured to check unsigned disclosures against ICE’s Mavent compliance engine and add copies to the e-Folder. All disclosure orders, exactly like lenders are used to seeing today, are tracked in ICE’s Disclosure Tracking. Tavant’s Disclosure Automation is fully scalable. Users can disclose multiple loans at the same time.  The rear-view mirror is clearer than the windshield, always. Businesses can achieve higher productivity by saving 5-20 minutes of disclosure desk time per loan and cutting down in disclosing. But how? Tavant’s Disclosure Automation 2.0 is a service that streamlines and automates the loan disclosure process for Encompass 360 loan origination systems. It saves time, ensures consistent validation for compliant disclosures, and reduces the need for manual processes. With a strong focus on regulatory and ESG disclosures, businesses can transform the disclosure management process by leveraging the scalable Tavant’s Disclosure Management solution that is A rules-based engine that automates disclosures based on the characteristics of an offer and a product. The ability to share content to make global changes simpler Automated Quality Assurance tools, such as Digital Compare, AI, and Machine Learning Task-based workflow for managing change requests Compliance searchability allows users to find the exact offer and disclosure requested by the regulator within and between millions of touches. Behind the scenes- Harnessing the automation for a future that works   Why choose Tavant’s Disclosure Automation Service over other API’s or services? Every lender follows the same basic process to disclose loan terms, and someone at the compliance desk will most likely open a loan, review it, and then click the button to send disclosure documents to the borrower (s). These tasks require 5 to 10 minutes of someone’s time per loan, and Tavant’s Disclosure Automation Service automates the workflow and allows loan officers to disclose multiple loans at the same time without having to log into Encompass and what businesses can achieve: Accelerated Time-to-Market: Development and approval have now been truncated from weeks to days. Implemented in one month with over 100 distinct business rules. Cost Savings: Reduced team and 100% reduction in agency costs for disclosure change management High Accuracy: This eliminates human error from processes and ensures accuracy at every step of the way Improved Speed: The ability to disclose 10 loans in 3 minutes, saving 5-20 minutes of disclosure desk time per loan Risk Reduction: No need for legal to review each change; regulatory risk is reduced, and consistency is improved Increased Productivity: The ability to offload some of the time-consuming data tasks, improving overall productivity. A Race Against Time The disclosure management process is frequently ignored in the increasingly complicated and fast-paced world of financial services. Organizations must reinvent their disclosure management role so that changes can be made quickly and efficiently to provide a top-tier customer experience, satisfy business goals, and avoid costly penalties from regulators. What’s Next? The lending industry will continue to be influenced by digital automation and transformation, and Tavant’s Automated Disclosure solution is a step closer to your digital modernization journey. Our service provides significant cost savings while enabling operational efficiencies, time savings, higher accuracy, and greater productivity. Tavant’s Disclosure Automation Solution is compatible with Blend, Simple Nexus, and any other point-of-sale system. The solution works seamlessly with Tavant’s FinXperience and ICE’s Consumer Connect. If the lender has implemented a custom POS or bought off-the-shelf solutions from Blend, Simple Nexus, etc., the solution can also work independently with some development effort. To learn more about Tavant’s Disclosure Automation solution, watch our recent webinar here or mail us at [email protected] . Reach out to the Tavant team for a more in-depth discussion of solution for your operating model and business.

7 Reasons Why Software Testing is Important

There is no denying  the fact that in software development, bugs can appear in any of the stages of the SDLC. In fact, there is a high possibility that even your final build that is ready to go live has errors of both types, i.e., design, and functionality. Furthermore, there have been numerous instances where the live demo failed miserably because no one thoroughly checked it before — oops! — now you are stressed out, and when that happens, it throws a huge blow in the entire process. That is why; each organization needs to ensure that software testing should be an integral part of the software development life cycle (SDLC). Here are the seven critical reasons, out of many reasons that make software testing important: Saves big bucks– When it comes to software testing, many companies do not see the need for it, or do not budget it properly, and at times, neglect the importance of a quality or testing process. It is always tougher to fix a mistake than to prevent it. Moreover, it is much more expensive. If a bug is discovered late in the game, then you are not just losing big bucks on the immediate cost of fixing the bug, but you are also losing money through lost prospective deals. If bugs are caught in the early stages, it costs much less to fix them and avoid any embarrassment later. Developing software without proper testing is a huge, risky bet. Onboarding testers who are technically sound and experienced is just like a smart investment that will reap us long-term benefits and it will far outweigh the cost of the service. To identify and correct mistakes– Regardless of how skilled and experienced developers we have, we all make mistakes, especially while developing an application that is huge and complex. Admit that there is no such application as a bug-free application. When a code is developed, it is important to test everything that we produce because there is always a possibility of glitches in the system and the only thing that can expose hidden errors, ensure that the system works as expected according to requirements, measure how well your software works before it is installed in a live operation, etc. is software testing. Boost Business– Making software testing an essential part of your software development life cycle lets you enhance the user experience and improves the final product outcome that ensures rock-solid brand presence, brand loyalty, and product recommendations. The well-tested product ensures that we send out the best version of our product into the market that speaks for itself, and word-of-mouth endorsement is priceless. This helps in retaining not only the existing clients but helps to onboard new clients as well. This makes software product testing even more vital. To ensure software security– One more headache that testing relieves is security. Software security is undoubtedly the most sensitive and yet most susceptible part. Cyber-attacks are quite common these days, and security is an important aspect that cannot be ignored at any cost. Notable instances have occurred where customers’ personal information has been stolen or hacked. Security testing of a product not only shields information from these hackers but also makes sure it is not lost or gets corrupted in any form. That is why we all look for trusted products that would bring confidentiality to share our personal information. Application security testing allows to identify and fix many vulnerabilities that ensure a secure product that in turn makes customers feel safe while using the product. With software security testing, we can deliver a trustworthy product to our clients that protects their critical information from Day 1. Validate the user experience– No matter the domain, the user experience is everything. The end purpose of developing any software should be to confer the best satisfaction to your users. Your application may function as required, but in the hands of the user, it could be baffling and inconvenient to know what feature is available where. Since software testing offers a prerequisite user experience, think of it as a trial run before you go live. There is nothing worse than an outraged user who paid for a product that does not work as expected. Fail to evaluate user experience, and your users will not fail to go to your competitor. If users of your application have a great user experience, they will tell their family and friends. And with the burst of social platforms such as Facebook, Instagram, Twitter, etc. positive as well as referrals can spread very quickly. Control Process– How do we know that the application works the way it is supposed to? How can we measure what all requirements are ready to deploy to production and that the quality meets expectations? How do we know how many critical issues are still open? Software development should be measured whether it goes against the requirements or not. The testing phase can help you to know the state of your product’s quality that certifies all features are ready for production. The sooner development teams receive feedback, the quicker they can address issues of both types, i.e., design, and functionality. Using this controlled process, we can build a formidable reputation and brand image, things that are important in the long term. Easy Transitions– Software applications released should be of superior quality and compatible with various OS, devices, platforms, etc. which can be achieved only if we do thorough testing. Even if we are adding a simple feature to our current application, checking compatibility is a good practice to ensure a seamless experience on the go. Ensuring this lets you maintain users and gives them a better experience without any loss in any convenience. This process enables the business to make its products stand out in the market. To Sum It Up: The benefits are noticeably clear. Any company, big or small, should test its system because achieving high quality is necessary. As stated above, software testing is an inseparable part

Make your first-party data work for you in advertising – Implementing identity solutions using cloud

The recent debates on digital privacy and several decisions by government and influential corporations have brought to focus how customer data is collected and used by companies. The broad trend is towards more transparency for users as to how their data is utilized and shared. Generally, users have shown more willingness to share data with the platforms they use. The direct use of that data for personalization and better engagement is a win-win situation for both parties. Such a scenario shifts the onus to the first-party platforms to use their consumer data judiciously, and any enrichment or extension of that data is only allowed with proper consent in place. This implies that the publishers have control over providing a meaningful and personalized advertising experience for their customers. Identity solutions built custom or otherwise are vital tools for publishers in such scenarios. What are identity solutions? Consumers can have multiple touchpoints with media companies. The service is accessed through a variety of devices, customer service interactions, social media interactions, content consumption, etc. Identity solutions assist in tying these various interactions together around a single user. Sometimes the connections between these interactions are obvious, such as a consumer’s ID and identity are easily established in this case. It isn’t always the case. Identity solutions are built around well-established databases, such as graph databases, which make indexing and searching easier.  Moreover, it necessitates the execution of specialized algorithms, such as the connected component algorithm, which generates a consistent virtual ID for a user. End-to-end identity solutions include data collection from various sources and the creation of an identity database. This identity database serves as a downstream reference for developing multi-tiered use cases that provide end-user personalization. Identity Graph Use Cases in Advertising Identity serves as the foundation for the development of numerous use cases. It is extremely useful in maintaining a low latency profiling database that can be used to feed downstream solutions. Audience segments: Instead of third parties, publishers can create customer segments themselves using an identity solution. Business rules set up for different segments help in the classification of audiences. These audience segments get auto updated as they tend to change over a period. Personalization Engine: The identity graph captures the actions of users, with respect to interests and preferences not only explicitly but also implicitly. Since all actions are in one place, giving a 360-view, the personalization engine can feed off this information. Creative optimization: Not everybody gets the same advertisement as the information available about the history of the user enables advertisers to show personalized creatives. Brand safety: If the content being consumed does not match the ad shown, the reputation of the brand can be impacted. An identity graph can provide supplementary information regarding user preferences that can protect the brand. Campaign Analytics: The performance of campaigns can be measured against audience segments. These are key metrics on which advertising is bought and sold.   Why cloud for Identity Graph implementations? Identity solutions map and unify billions of relationships and query customer data with millisecond latency. There are many purpose-built cloud databases made for identity solutions, for example: AWS Neptune, Neo4j, etc. Cloud solutions reduce the total cost of ownership by storing and querying billions of nodes and edges, with lower latency and lower costs for storage compared to other models. Usually, these solutions are quick to deploy and can be up and running without taking much time. Conclusion It has become imperative for media publishers to develop identity management solutions of their own as they ensure that first-party data is fully consolidated. Identity solutions can not only provide personalization for the publisher’s users, but also serve as data rooms for their advertisers. These solutions help publishers meet the privacy rules and also provide their users all the relevant content they require, including advertising.

From Legacy to Modernization: Connecting the Digital Dots in Underwriting

The rapid advancement of technology over the last few decades has transformed the consumer lending market, although the extent to which this transformation has occurred is widely debated. Fintech lenders have reduced the time required to process mortgage applications. Lenders have gradually moved from a traditional to a more digital lending environment.   Three key drivers have led lenders to make this move: 1) The need for efficiency: Digital lending platforms can process loans in a fraction of the time it takes for loan officers to do so. This means that customers get their loans faster and at a much lower cost. 2) The need for innovation: Lenders are always looking for ways to enhance the customer experience and improve adoption rates. Digital lending offers them an opportunity to do so by providing tools such as instant approvals, online account management, and remote check deposit. 3) Evolving customer expectations: Customers want more than just a simple loan transaction. ‘Speed to decision’ is vital to customers. Driven by the need for efficiency, innovation, and evolving customer expectations, most lenders have been moving steadily toward greater digitization. Underwriting has been a key focus area. Most lenders have actively been upgrading their underwriting capabilities with more advanced digital technology and expanded data sources. Data mining and analytics are rapidly changing the lending landscape by enabling businesses to capture and process real-time data to their advantage. According to Insider Intelligence’s Online Mortgage Lending Report, automated underwriting processes is crucial for the success of modern lenders as it can significantly reduce loan processing times and interest rates. The Impact of Automated Mortgage Underwriting Across Various Lending Stages  Manual underwriting entails interacting with disparate data sources, which results in extreme inefficiency in risk assessment. There is no central repository for data that can be gathered, segmented, stored, and accessed quickly. This results in underwriters missing critical information that could significantly affect a borrower’s risk profile. Using Artificial Intelligence, Machine Learning Algorithms, and other related technologies, automated underwriting software enables lenders to make underwriting decisions more quickly, with increased accuracy, and with minimal human intervention. It is accurate, faster, and more reliable than manual underwriting. In order to make an analysis report, the automated underwriting system automates the entire loan approval process, from extracting data from various underwriting documents to matching it with third-party data from other financial institutions, like banks, creditors, lenders, and so on. FinDecision- A mortgage industry “one of a kind” product that exceeds customer expectations Tavant’s FinDecision is an AUS automation and underwriting platform that enables lenders and loan originators to achieve operational efficiency through optimized intelligent business processes and workflow orchestration. It is a core component of its straight-through processing and automated underwriting. It enables lenders to optimize loan fungibility and execution while maintaining operational efficiency. FinDecision leverages machine learning and process automation to submit loan data to automated underwriting systems with a single click, enabling lenders to see the full scope of operational benefits available to their borrowers and thereby improving the overall borrower experience. Through automation, lenders can reduce downtime and costs while improving loan quality. FinDecision compares investor guidelines, multi-AUS response, and loan data. It also provides a list of the most common questions and answers for first-time home buyers. The platform automates the underwriting process, powered by AI and machine learning algorithms. The platform eliminates human errors, keeps data up-to-date, and provides real-time insights to lenders. This revolutionary software provides a single view, side-by-side comparison of investor guideline responses and offers 360-degree insights on the various segments of loan data (Income, Asset, Collateral, Credit, Borrower). FinDecision automated data quality check (lender, investor, and origination channel-specific) enables the loan processor to prep the loan file instantly and review data inconsistency and quality checks. It automatically updates the loan data according to the investor’s requirements (data mapping and representation). FinDecision enhances loan quality and improves the overall borrower and lender experience. It offers an automated, one-click approach to achieving loan fungibility and pricing best execution on the one hand, and operational best execution on the other, during the loan’s processing, underwriting, and secondary market stages. It is a core product within Tavant’s Touchless Lending platform and is Loan Origination System (LOS)-agnostic. Wrapping up To remain competitive, lenders should speed up underwriting transformation. Automated underwriting evaluates risk and underwrites loans using a technology known as automated underwriting systems (AUS). It has the potential to accelerate and simplify the loan approval process for both lenders and borrowers—it is not an exaggeration to say that automated underwriting brings the mortgage process into the twenty-first century. Tavant’s AUS automation and underwriting platform, FinDecision provides an intuitive way to eliminate hard-coded legacy IT systems. It compiles findings to credit conditions and compares them (existing vs. new). What Next? Schedule a demo with Tavant today, visit us here or reach out to us at [email protected].

Cloud Service Providers Accelerate Public Cloud Maturity Across Vertical Industries

The State of Cloud Adoption 2022 –  A survey-based research study by IDG Research and Tavant Tavant, in collaboration with IDG Research, conducted a comprehensive research study among 255 large enterprises’ CIOs, CTOs, and Senior VPs to identify cloud adoption trends. The study’s goal was to understand the cloud computing landscape in the United States across a wide range of industries. Automotive, discrete manufacturing, e-commerce, financial services, food and beverage, healthcare, process manufacturing, retail and wholesale, and travel were among the industries represented. Hurdles to Cloud Deployment Being Leap-Frogged Tavant and IDG Research found that businesses are increasingly embracing cloud tools, industry frameworks, and infrastructure automation tools to improve time to market and ROI. Security, legacy migration, cloud governance, and infrastructure maintenance are all common concerns that are now being addressed through improved practices such as outcome-based shared services models and industry-specific cloud-based solutions. Development, security, and operations processes (DevSecOps), which work with cloud tools, are becoming increasingly important in the development and management of cloud applications. The top cloud adoption barriers are security (52%), legacy IT infrastructure (29%), insufficient budget (28%), regulatory concerns (27%), and uncertainty about cloud benefits (27%). (23 percent ). Business agility is a key motivator for 93 percent of US organizations polled to adopt the public cloud. The secondary reasons are increasing revenue through innovation (81%) and lowering costs (8%). (77 percent ). Currently, 43 percent of respondents have a hybrid cloud or multi-cloud strategy in place. Nearly 40% of organizations use DevSecOps extensively across their entire technology footprint, while 37% use DevSecOps for specific programs. Industry Focus Makes Cloud Adoption Favorable Today’s cloud service providers continue to bring significant innovation to the banking, manufacturing, and hospitality industries, with cloud frameworks, tools, and best practices developed for specific industries. 53% of BFSI companies have a hybrid or multiple cloud strategy in place, and 58% plan to use CSPs extensively, mainly for new workloads Manufacturers are ahead of other industries in cloud adoption, with 75% having strategies in place. 78% of retail and wholesale firms and 59% of advertising, media, and entertainment businesses are actively developing their cloud strategy Cloud Innovation Becomes Industry Oriented Innovative cloud services, including, AI and analytics, are favored by hospitality, food and beverage, and travel Industries such as publishing, PR, advertising, media, entertainment, and broadcasting are banking on Cloud Data Services for expanding their end-user base and enhancing customer experience. The Agtech industry is actively looking at adopting IT service management for better business outcomes and a sustainable future. The Future is on the Cloud The advancement of business maturity in cloud implementation has resulted in a better understanding of the benefits, which include increased business agility, revenue through innovation, lower costs, and improved TCO. Click here to get your copy of the Research Report. Learn more about Tavant Evolvx, a high-touch offering that seamlessly intertwines standard data models, crosscloud connectors, workflows, APIs, and industry-specific components to meet your unique challenges.

Unlocking the Ability to Adapt with Digital Lending Solution

Long queues. Tons of paperwork. Mortgage and too much hassle. The traditional lending ecosystem is fraught with inconveniences in a world where instant gratification in consumer delivery is the new normal. It lacks agility and flexibility to meet the needs of the new consumer. Fintech organizations are far more technology-enabled. They utilize advanced technology to deliver efficient, fast, and flexible financial services to consumers. No wonder fintech is fast disrupting traditional service providers and growing at an unprecedented rate of 9.2% to nearly $158,014.3 million by 2023. The changing landscape of lending Traditionally, when borrowers in need of capital approach lenders, they are provided standard options–a folio of one-size-fits-all loan products. Without deep insights into consumer needs, these loan products cannot meet each potential borrower’s specific and unique credit needs. There is no differentiation in loan products, and all traditional lending entities are fighting for the same piece of the pie. Add to this the often-prohibitive service cost – making loans economically unviable for many borrowers in the market. Furthermore, the approval process is too time-consuming and complex, undermining the needs of credit seekers who require the loan immediately. Naturally, easy access to credit is one of the biggest challenges in the global lending ecosystem. This is where digital lenders come with an advantage. Digital lenders have a competitive advantage over traditional lenders as they have new-age technology and data capabilities that make lending far more quick, efficient, and data-driven than ever before. Paving the path to an equitable financial ecosystem Digital lending creates a more inclusive financial ecosystem and delivers loan products and services to underserved and previously excluded individuals and businesses. New-age technologies and data drive innovation in digital lending. Simultaneously, data analytics is also ensuring less risk in the digital lending space. Even policymakers are encouraging the development of new and agile loan products to serve the businesses. Fintechs, often equipped with digital lending capabilities, have changed the game for borrowers. They give remote access to credit in a brief time. Additionally, underwriting is easier and more data-enabled than ever before. This efficiency of digital lending is a game-changer in the global lending landscape, opening new routes to ease access to credit for all borrowers, irrespective of their creditworthiness in the traditional sense of the term. A wider circle – Correlation of data opening new revenue opportunities for digital lenders Digital lenders no longer rely on manual underwriting processes. Instead, they use financial transactions and FICO® credit scores to identify risk factors. There are also new and innovative risk repayment methods, from real-time payment deductions to standard mortgage paid conveniently via apps. These digital methods enable fintech organizations to collect and analyze additional data about their customers for customer-centric decisions. These include determining credit limits, holistically understanding their ‘customers’ financial needs, and delivering new and innovative financial products that serve the unique needs of each digital-savvy customer. The Way Forward Digital lending is redefining the dynamics of the credit ecosystem. With lower costs and improved reach, financial institutions can do more with less. However, digital lending also requires a robust long-term strategy to be truly safe and risk-free. The lending journey does not end with disbursement; loans also need to be collected, serviced, even restructured, and renewed as time passes. With so many moving parts, digital lending requires careful planning to avoid the risk of a false start for financial institutions. Process automation, new and intelligent technologies such as AI and ML, and intelligent data analytics are at the heart of a wholesome digital lending strategy. What next? Tavant can help lenders diversify how they do business and effectively unlock savings with next-gen technologies. To learn more, you can reach out to us at [email protected] or visit here. FAQs – Tavant Solutions How does Tavant help lenders adapt their digital lending capabilities?Flexible, modular platforms with API-first architecture, configurable workflows, and scalable infrastructure for rapid adaptation to regulations and market demands. What adaptation capabilities does Tavant offer for digital lending transformation?Rapid deployment, customizable UI, integration options, configurable business rules, real-time analytics, and continuous optimization. Why is adaptability important in digital lending?Rapid regulatory, customer, market, and tech changes require flexible systems for quick response and innovation. What makes a digital lending solution adaptable?Modular architecture, configurable workflows, API-first, cloud-native, real-time analytics, and easy third-party integration. How long does it take to implement digital lending solutions?3-6 months for basic, 12-18 months for fully-customized systems; cloud solutions deploy faster, some basic setups in weeks.

Going Up! Industry Clouds have arrived

Recent historical events have shifted global business priorities, speeding up the digital transformation process. Remote work across continents and stronger collaboration with all stakeholders, including customers and employees, have spurred innovation and value-driven cloud technology offerings. While organizations were figuring out the best cloud technology applications and their implications, a new and efficient cloud solution emerged. It’s called the industry cloud.     WHAT IS AN INDUSTRY CLOUD? An industry cloud provides cloud computing services tailored to a specific industry or business model. Industry clouds are highly curated environments that stack cloud technologies. THE DRIVE FOR INDUSTRY CLOUDS AND ROLE OF CLOUD SERVICE PROVIDERS (CSPs) Initial adoption of cloud technology was driven by cost, storage, or processing support. But as more enterprises moved to the cloud, many found that deployment and usage were often erratic or inconsistent. The need for an industry cloud arose when certain industries realized they needed a more tailored IT solution for security and compliance. And the Industry cloud was born. According to Gartner, Industry cloud ecosystems and data services are driven by increasing geopolitical regulatory fragmentation and industry compliance. Businesses today expect the same level of customization in the cloud as they do on-premises. To enable this, cloud service providers (CSPs) now offer a hybrid strategy that includes industry-specific solutions and cloud infrastructure maintenance. INDUSTRY CLOUDS… SOME EXAMPLES Today, cloud hyperscalers are partnering with industry-oriented cloud service providers to build specialized environments. Microsoft, for example, has worked with partners to develop supply chain solutions for the industrial industry through Microsoft Azure Cloud. Similarly, Microsoft Azure has now built industry clouds for financial services, retail, and other industry verticals. In the construction and real estate industries, industry clouds can comprise solutions for model management, collaboration, estimate, scheduling, site management, and more. This level of industry cloud specialization allows firms to focus more on their core business while still being able to derive the benefits of cloud computing. Salesforce introduced the ‘Revenue Cloud’ this year. The industry cloud is aimed at businesses that need to consolidate customer transactions. The revenue Industry cloud combines CPQ, billing, B2B commerce, and channel software products to offer everything from renewal to revenue recognition (PRM). BENEFITS OF THE INDUSTRY CLOUD Businesses prefer industry cloud computing over a “one-size-fits-all” cloud model for a more specialized environment. Besides niche data security, organizations want to closely align with customer priorities, for example, Banking as a Service (BaaS), Agtech Cloud (AgTech), and Health Cloud (Health Cloud). With the rise of mobile devices, the cloud market needs new and efficient apps. Customizable Offerings Industry cloud solutions are custom-built beyond security and compliance to address individual business outcomes. These solutions are also critical when integrating public cloud computing with on-premises resources in hybrid architectures. Product-Centric Approach Organizations today are becoming product-centric, agile in operations with better time to market, and composable architectures with a pay-per-use model. Leaner Footprint Industry cloud solutions are popular for SaaS deployments. As a result, many legacy IT providers can benefit from leaner data center footprints. Improved Functionality Industry-specific applications enable higher levels of technological efficiency, functionality, and performance. An industry cloud product for healthcare, for example, could securely manage electronic health records or parse medical images. Advanced Security Businesses are increasingly concerned about data security online. Industry clouds offer superior levels of security and compliance over traditional cloud offerings, allowing CIOs to rest easy. INDUSTRY CLOUDS AND THE FUTURE The future of industry clouds depends upon the cloud vendors’ ability to customize cloud technologies by business needs. According to Techaisle’s research, SMB and mid-market cloud adoption will increase by 121% increase in the US over the year. Industry clouds are ready to help organizations leapfrog their digital transformation journey and accelerate technology transformation where it is most needed. A new generation of industry cloud providers will help businesses innovate faster by providing customized applications and services.  

Six IoT Testing Challenges for Testing Experts

Introduction The Internet of Things (IoT) refers to physical objects embedded with sensors and software that can exchange and collect data over a wireless network. The Internet of Things brings many consumer benefits, like simple remote control, automation, etc. It also brings added software complexity and security risks that require significantly more testing than in the past. IoT devices have evolved to look more like traditional cloud applications, with code that runs in the device itself, as well as an array of dependencies that interact with the outside sources of data such as time or weather. These dependencies can make devices expensive, difficult, and time-consuming to test as it involves real-time sharing of data and collaboration. A study says that more than 6.4 billion Internet of Things (IoT) devices were in use by 2016, and that number will grow to more than 20 billion by 2026, which means that our planet will soon have more connected devices than the human population. Testing these IoT devices becomes quite challenging because of the variety and volume of data this system generates, the heterogeneity of the working environment, and the complexity of the number of working components involved. Challenges in IoT Testing One of the tough challenges for manufacturers and integrators is testing these devices. Let us discuss some challenges associated with the testing of IoT devices: Communication Protocol: IoT devices use various communication protocols such as MQTT (Message Queuing Telemetry Transport), XMPP (Extensible Messaging and Presence Protocol), etc. These protocols aid in the establishment of a connection between devices and servers. Tools/Tech that the testing team is planning to use should support these communication protocols so that APIs written on top of these protocols can be effectively validated which interacts with these devices. Multiple IoT cloud platforms – Azure IoT, IBM Watson, and AWS are the most used cloud IoT platforms that help connect different components of IoT devices. These devices need to be tested across the cloud platforms to ensure their effective usability. In a cloud platform, we have different IoT devices with different capabilities, these devices generate data that can be structured or unstructured and will be sent to a cloud platform.When more devices are deployed on the cloud platform, it becomes difficult to replicate a real-time environment for testing, since there can be a lot of devices that need to be tested on different platforms. IoT security and privacy threats – IoT devices are the most vulnerable to cyber-attacks. Most users think that it’s a manufacturer’s responsibility to secure their devices and, therefore, do nothing to protect them. Cyber-attacks are very common across IoT devices, and security is an important aspect today. Wired systems are much less accessible than non-wired systems. Therefore, one challenge to moving into IoT solutions is that companies open themselves potentially to more risks unless they have a perfect security strategy in place. Beside functional and performance testing, special attention should be paid to the device password policy, data protection, data encryption, regular firmware, or software upgrade testing. Device Diversity –  With so many brands, models, versions of the OS, Screen size, etc., it is a challenge to test an IoT application that works perfectly across all devices for all possible combinations that are not practical.  Each IoT device has unique capabilities and may perform better in some environments and platforms than others. As a result, they must be tested across platforms for effective usage, and it is critical that we have good test coverage across dozens of devices. There is also a challenge with the version upgrade for the IoT devices along with their software and firmware updates. It becomes critical to test the devices across the IoT platforms with their latest software to ensure all the components are working efficiently after the update. Network Availability (Always online) – Network configuration essentially affects the performance of an IoT device because IoT is all about rapid communication and that too consistently all the time. Though, at times devices experience troubles with network configurations like unreliable internet connections, hindering channels, etc., which poses a challenge of how to test it in all possible network conditions. Data Volume, Data Variety, and Data Velocity (Real-time data testing) – Sensors on all devices simultaneously generate massive data (this data is significantly intricate and unstructured that involves appropriate cleaning of it for the end processing). IoT will be dealing with that data and different varieties of data that cause significant challenges. Gathering, organizing, and evaluating this disintegrated data is not easy as the volume of data can be boosted at any time. Conclusion: There are numerous other challenges to consider in addition to the ones mentioned above. Hardware quality and safety concerns are among other challenges that the testing team faces while testing IoT applications. Building stable and quality IOT applications might seem overpowering and a huge task, but it can be made simpler by proper planning, splitting it down into separate sub-tasks, and setting up a rock-solid test environment to manage cloud and virtualization strategies.

Top Blogs and Webinars from 2021-Connected Service and Warranty

As we close the year 2021 (and what an incredible year it was), here is a list of our most popular blogs and webinars from this year – the ones our customers showed the most love to, and that answered the most pressing questions. Enjoy! Blogs  1) From Cost Center to a Competitive Advantage: Warranty Management in Manufacturing Today Once considered a cost center, warranty management is taking the front seat in creating seamless aftermarket service experience. 2) Electric Vehicles and their Impact on Automotive Warranty Management The transition to smarter electric vehicles and the potential phasing out of combustion engines is likely to be a game-changer for many. 3) Futuristic Tech: Turning the Wheels of Manufacturing Learn about the advancements in the manufacturing industry with Drones, AR/VR, IoT, and much more for better processes, performance, and protection. 4) Data Analytics: A Catalyst for Change in Service Life-cycle Management Today AI and analytics is critical in addressing service life-cycle challenges, increasing transparency, and creating a rich aftermarket experience. 5) Making Warranty Management Profitable for Manufacturers Traditionally manufacturers offered warranties to buyers to assure them of the quality and longevity of products or services. The industry has progressed a long way. Webinars 6) Connected Service Life-cycle – The Flywheel Effect Opening session at WCM Experience by ‘Women in Manufacturing’ from Tavant, on how smart and simple changes in after-service processes like customer support, service request, service planning, service execution and field service, spare parts management, warranty management, and recalls can create exponential value and set the flywheel to spin faster. 7) Panel Discussion: Service Analytics – Make that Data Work for You! Smart learning models are getting more accurate and changing the way service analytics is driving decision-making. This session explores the decisions that impact machine uptime, service parts pricing, equipment failure, and maintenance demands. 8) Panel Discussion: Service and Sustainability – Reduce, Recycle, Reuse! Timely Service data and insights can impact the bottom line in three ways – fiscal, societal, and environmental. In this session, the speakers from varied Manufacturing backgrounds discuss how service data can benefit sustainability. 9) Connected Warranty and Service in Automotive Industry A discussion on the Automotive warranty trends, the impact of innovative technologies and connected data, and how the Automotive industry is redefining service through connected and optimized service platforms. 10) The Learning machine: Transforming Customer Experience Using Data, Warranty, and Service Contracts Opening keynote by Bob Roberts, Customer solutions leader, Trane Technologies at WCM Conference. Service excellence in a manufacturing and aftermarket industry dictates decisions be made using near-real-time information. To provide a rich and seamless experience to their customers, manufacturers need to improve data visibility, the backbone for delivering cutting-edge services.

Transforming IoT Data into Actionable Insights with Time Series Insights

Time Series Insights for IoT data: Generally, IoT data typically consist of time series data, which makes sense when observed over a period of time, like a sensor’s behavioral change, etc. Billions of data is getting generated from IoT these days, and it needs to be stored in a repository. But it’s challenging to store this data in a way, where you want to use it in near real-time to be processed to derive meaningful insights when needed in machine-critical situations. So, we need to store this data in a way that it makes sense. This calls for a service that can scale massively and help operators find insights quickly, Azure Time Series Insights. Introduction to Azure Time Series Insights: Azure Time Series Insights is a serverless, fully managed data analytics solution (PaaS), that users can use to integrate with their constantly changing data like data from several sensors or machines, data from airlines, satellites, etc. Any data that can be generated on a large scale and needs to be analyzed can be used through Azure Time Series Insights. Azure Time Series Insights architecture: The above figure shows a high-level architecture of how Azure TSI can be implemented in a real-life scenario. Time series real time data can be generated by various sources like satellites, mobile devices, medical devices, sensors, etc. Azure IoT Hub or Event hubs can be used to fetch the data from these devices into the Azure environment. Further, this data can be processed using services such as Stream analytics, Logic apps and Azure functions and computed signals from the processing pipeline are pushed to Azure Time Series Insights for storing and analytics. Once in the Time series insights platform, the data can be used for visualization. The data can also be queried and aggregated accordingly. In additional, customers can also leverage existing analytics and machine learning capabilities on top of the data available in Time Series Insights platform. Data from Time Series insights can be further processed using Databricks and pre-trained machine learning (ML) models can be applied to offer predictions in real time. Components of Azure Time Series Insights: Integration: Time Series Insights provides easy integration for the data generated by IoT devices by allowing connection between the cloud gateways like IoT hub and Event hubs. Data from these can be easily consumed in JSON structures, cleaned and stored in columnar store. Storage: Azure TSI also takes care of the data that is to be retained in the system for querying and visualizing the data. By default, data is stored on solid state drives (SSDs) for fast retrieval and can be retained for upto 400 days. Data visualization: Another component of Azure TSI, data visualization helps data fetched from multiple data sources and stored in the columnar stores, to be visualized in the form of line charts or heat maps. Query Service: Time Series Insights also provides a query service using which you can integrate Time Series Insights into your custom applications.   Conclusion: Azure Time Series Insights helps you to easily connect to billions of events in Azure IoT hub or Event hubs, visualize and analyze those events to spot the anomalies and discover hidden trends in your data. It can both store as well as visualize the data. Alternatively, one can also have the capabilities to run queries against this data and obtain more simplified results.

Decoding the Future of Fintech Lending

It’s 2005, and Linda is all set to purchase her first home, her “starter home.” She knows finding that perfect home won’t be easy. On top of that she knows closing on her mortgage is going to be a time-consuming process. She is exasperated and dreading the thought of going through many cumbersome manual processes and tiresome paperwork. There is nothing much she can do but patiently wait for the process to play itself out. Fast forward. It’s 2021. The pandemic has reshaped how we work, and the world is adapting to a new era of remote working. Amid the upheaval, like many others, Linda decides to move from her ‘starter’ home to her ‘forever’ home with a backyard and home office. She found the perfect home by searching Real Estate sites on her mobile phone, yes, she found her home on her iPhone. Next step is the dreadful mortgage, but thanks to her lender’s digitalized application and closing processes, the entire home-buying process is now simplified and much quicker; Linda is pleasingly astounded by how much mortgage lending has evolved over the years. Reaching the New Wave of Borrowers With Digital Mortgage Capabilities Today’s mortgage industry is on the cusp of digital re-imagination. Driven partly by the need to meet the growing demand from tech-savvy borrowers for a quick and seamless process and partially by the pressure to cut costs and enhance efficiencies. Lenders are looking to digitize their end-to-end mortgage process. That’s the dream state for a lender. Thus far, most of them have focused on the lending process’s front end, enabling digital loan applications and consumer portals. As competition intensifies, they shift to the next stage of digital transformation by turning to Fintech lending solutions that boost efficiencies in loan production and enhance the servicing experience. Fintech Lending- The Digital Focus of New-age Lenders According to the report titled ‘The Role of Technology in Mortgage Lending,’ fintech lenders have the ability to process loan applications about 20 percent faster than other lenders. Fintech lenders process mortgages faster than traditional lenders, measured by total days from submitting a mortgage application until the closing, the report indicated. However, switching traditional mindsets and operating models to deliver digital journeys at an accelerated pace is no easy feat for a financial behemoth. But modernizing the borrower experience is the need of the moment for all lenders. Fintech is playing an increasing role in shaping financial landscapes. A fintech mortgage provides faster, more accurate, safer, and more affordable options than traditional mortgage lenders. It enables lenders to create a better relationship with borrowers with quicker and more seamless, personalized experiences. It accelerates data gathering, helps borrowers with superior communication, and reduces avoidable steps along the way. Seizing the Benefits of Fintech Mortgage Lending Enhanced efficiency: Efficiencies produced by fintech lending solutions allow lenders to close on mortgage loans faster. Automating numerous back-office operations and centralized data solutions also enable lenders to leverage customer information more efficiently than ever before. It speeds up otherwise time-consuming operations and further helps in closing the loan process faster. Delightful customer experiences: A more agile, streamlined application process indicates customers may be more likely to perform a given task that serves the lender in terms of the number of applications closed and funded. No more fragmentation: Fintech mortgages replace the fragmented siloed solutions of traditional lending with an integrated, end-to-end digital solution. It leads to greater efficiency and productivity, along with quicker loan cycle times and faster closures. To the Future: Let’s fast-forward to 2030.  Linda is in the process of refinancing her ‘forever’ home. She’s astonished by the impressive advancements in cycle times and service levels compared to her 2021 experience. Her lender leverages next-gen digital interfaces that allow her to have contextual chats in real-time. Her appraisal is done same day, by a drone. Her lender uses AI-based applications to drive intelligent decisions based to ensure that Linda meets specific credit requirements, saving her significant time and effort. Not just that, the blockchain technology is there to provide a single source of verified data such as her tax information, income, assets, property valuations, and so on, improving accuracy as well as fast-tracking the loan fulfillment process. The outcome: Linda e-closes her refinance in a couple of days, or perhaps even in a few hours, thanks to an integrated digital ecosystem. It truly is a “one-click” refinance. Are you ready for the digital future? As digitally connected millennials and Gen Z borrowers coming into the marketplace expect hyper-personalization and faster closings. Lenders seeking future-proof success have only one choice – move from a tactical to a strategic mindset, modernize processes, and embrace intelligent automation. Learn how Tavant can help you lay the foundation for an end-to-end digital mortgage; reach out to us at [email protected] or visit us here. FAQs – Tavant Solutions What future fintech lending innovations is Tavant developing?Tavant is advancing embedded lending solutions, API-first architectures, real-time decision engines, and predictive analytics for market trends. They’re building platforms that enable instant lending integration across various digital channels and ecosystems. How does Tavant prepare lenders for future fintech disruption?Tavant provides scalable cloud-native platforms, open API frameworks, and continuous innovation programs that help traditional lenders compete with fintech companies while maintaining regulatory compliance and operational excellence. What trends will shape the future of fintech lending?Key trends include embedded finance, buy-now-pay-later expansion, cryptocurrency lending, AI-driven personalization, regulatory technology integration, and the rise of neobanks offering specialized lending products. How will fintech change traditional banking?Fintech will push traditional banks toward digital transformation, force innovation in customer experience, create new partnership models, and require banks to become more agile and customer-centric in their lending approaches. What is embedded lending?Embedded lending integrates loan products directly into non-financial platforms like e-commerce sites, software applications, or marketplaces, allowing customers to access credit at the point of need without leaving the platform.

To Automate Testing or Not to Automate – The Reality of Test Automation

Organizations today face many Quality Assurance (QA) challenges – time constraints in development and test cycles, executing large volumes of test cases, testing diverse legacy applications, and mitigating the impact of ripple effects that arise from configuration changes in application modules. The best way to deal with this situation is to adopt a well-integrated and robust automation solution that can predict and simulate business scenarios. Automation testing uses automated tools or programs to execute a series of tests that check the quality of a program or product. The significant feature of automated testing is its ability to perform hundreds of tests in minutes and record outcomes with accuracy and speed. Tests run repetitively based on programmed expectations, which can often be too tedious to perform manually. Vendors today provide automation testing services/platforms as part of their quality engineering services to ensure continuous feedback into the product lifecycle. THE DEMAND FOR AUTOMATION TESTING  According to the Global Automation Testing Market report, the automated testing industry is expected to grow at 14.2% CAGR during the forecast period from 2021 to 2026. In another survey conducted by Compuware, most enterprises think that manual testing is one of the major hindrances to a business’s success. Additionally, more than 90% of respondents believe automation testing to be the most critical factor in accelerating innovation. And as a result, the demand for smart automation testing services is booming. WHY DO BUSINESSES WANT AUTOMATION IN TESTING? With automation testing, both developers and quality analysts can be sure of the quality of their products without lengthy test execution cycles. Automated testing can give organizations quick feedback on product or software performance. In May 2019, the difference between test execution efforts of manual and automation testing was recorded. The results showed that for a test case set of 1000 Full Regression, manual testing took 160 hours, while smart test automation required only 16 hours, a clear saving of 90% of test execution efforts. These results have defined the efficiency of executing automation testing in technology developments. EXPECTATIONS VERSUS REALITY IN AUTOMATION TESTING We’ve established that automation testing services are a necessity to improve your product performance effectively. But there is also another side to it. Faster release cycle with quality, notwithstanding, automation testing services also come with some limitations: Everything Cannot Be Automated Some businesses have started to consider whether every test can be automated, in other words, 100% test automation. But the belief that a higher level of automation is always desirable and achievable is a myth. While specific tests may benefit from being automated, others cannot be automated. Also remember, that automated test cases will only be as good as the programming behind them. Costs Versus ROI  Automation testing needs to be designed based on whether the automation tests can save manual effort and offer a long-term return on investment (ROI). Automation testing tends to require a higher investment initially, with potential earnings and saving realized later. Additionally, the ROI of automation testing can be dependent on the tool that is used to conduct the tests as well as the complexity of tests implemented. Staying Objective While Test Automation can have a tremendous impact on product quality and returns, it is essential to understand its limitations and set realistic expectations. To achieve significant success with test automation, teams must first define the results objectively and carefully plan the tests without bias creeping in. Our objective should be to automate 100% of the test that should be automated instead of automating every test. So, figuring out what to automate (or not to automate) should be given the utmost importance before starting any automation activity. THE FUTURE OF AUTOMATION TESTING In today’s highly competitive market, businesses are seeking faster time-to-market along with the pressure of continuing to offer a superior product or solution. As a result, companies run more tests to find bugs faster and release their products or upgrades more quickly. The role of AI in automation testing is one of the most prominent automation trends in 2021. As AI-powered tools continue to advance, businesses will manage their automation testing even more efficiently and at speed. Machine Learning and AI testing will also develop automatic research methods and use advanced analytics to track results.

Unleash the Power of Data-driven Decisions in Lending

AI-based Attribution Models – The Future of ROI-focused advertising

The current economic climate is putting advertisers and marketers under tremendous pressure to demonstrate ROI from their marketing dollars. Gone are the Mad Men days of advertising when the art of storytelling was all it took. Today, advertisers are spending in an omnichannel environment and must account for every ad dollar. It’s no longer all about reach – or clicks on online ads. The entire focus of advertising has evolved from reach or clicks to the desired outcome – usually a purchase – either online or offline. How do marketers know which of their multitude of channels and touchpoints are performing on par and responsible for consumers’ desired actions? Enter marketing attribution As the marketing ecosystem gets more complex, the platforms that need marketing dollars to increase in number, and the demand for justifiable ROI from marketing reaches unprecedented decibels, sound measurement, and attribution have become the holy grail for brands and CMOs. They are seeing an immediate need to link different marketing touchpoints in their attribution and decision-making models to truly understand the new consumers’ purchasing path. Marketers have been following simplistic attribution models such as a first click or last click or attributing credit to the first or last touchpoint in the path to purchase. However, these attribution models are hardly comprehensive or data-driven, and they rarely say anything about the consumers’ intent. AI-based algorithms now play a significant role in building sound attribution models for the new complex, non-linear buying journeys and multitude of marketing channels, platforms, and touchpoints. Custom attribution models with AI at the core We have developed four key models for AI-based attribution that are truly data-driven and cutting-edge. AI-based custom attribution models are truly compelling in the new-age marketing ecosystem. Our four attribution methods are based on all events and channels where customer touchpoints exist and can predict – to a large degree of accuracy – whether a touchpoint led to conversion or not.  What are these four methods? Here’s a quick summary: Logistical Regression – It is a well-established statistical model that takes inputs from existing touchpoint data and predicts which class the data should belong to. A non-linear function is applied to each touchpoint. Smaller the value, smaller the weightage assigned to it. Using these touchpoints, the model is trained over time. Each touchpoint becomes a variable in the logistical regression model, predicting conversion based on historical data to a reasonable degree of accuracy. Shapley Value – The Shapley Value model takes a game-theoretic approach to multi-touch attribution. The core idea is to keep or remove a channel and then ascertain the outcome. This naturally tells you your highest performing and lowest performing channels and is a fair and transparent way to attribute credit to each channel or combination of channels. Markov Chains – This model considers the sequence of the customer journey, i.e., the likelihood of each customer being exposed to some marketing tactic and the potential next step in the journey. In summary, the Markov Chains model focuses on the probability of each consumer transitioning from one exposure to the next marketing exposure and taking a desirable action in the process, such as a website visit or a purchase. The model considers all possible conversion paths. It gives appropriate weightage to each exposure on the customer’s journey to conversion. We then take away one of the channels and see the impact on conversion and subsequently ascertain the value of that channel in the attribution model. Hidden Markov Model – Hidden Markov Model, although new, is one of the most effective attribution models in marketing. It attempts to determine the state of mind of each consumer when they perform any action during the path to purchase. For example, what state of mind is the consumer in when he or she visits the website, searches, clicks on an ad, etc. This determines whether the action will lead to purchase or not. The Hidden Markov Model has had a significant impact on ML, and its impact on marketing and advertising is only beginning. It is safe to say that in the world of clicks – sometimes even inadvertent – the Hidden Markov Model can truly predict the role of each channel in bringing the consumer closer to desired actions like purchase.   The Bottom Line: AI-based custom models are the future of marketing attribution. Evidently, AI-based attribution models can track each consumer at each stage of the buying journey and understand the importance of each “moment” and “action” in the customer journey. This helps advertisers truly understand the performance of each touchpoint and channel in the buying journey and optimize media spend continuously during campaigns. AI-based attribution models are driving the next generation of ROI-focused marketing. Are you ready to up your measurement game with AI? If yes, then reach out to [email protected] or visit us here to know more.

Is cloud the keystone of composable business?

Over the past 24 months, global businesses have encountered many uncertainties, from disrupted supply chains to government-led lockdowns and remote-working measures in workplaces. With many unknowns, the existing business models built for efficiency were put to the test, and the fragile nature of businesses exposed to the new reality. Enterprises were forced to digitally transform at scale to bring agility in operations and resiliency to their business systems to overcome these challenges. Businesses need to build architectures that readily share data between business processes, analyze the data to derive insights, and build composable, modular systems that can respond and adapt to changes rapidly. This post-pandemic wave of digital transformation is meant to bring in more flexibility in enterprises and has been fueled by the cloud. With the advancement in cloud technology, companies today want rapid innovation, exploit new market opportunities, or deliver better experiences and scale efficiently with reduced technology risk. But how do we make such systems a reality? Decision-making has to be autonomous and augmentative to realize change early. Technology platforms, on the other hand, have to be modular and plug-and-play in nature to personalize application experiences for the customer. This means enterprises have to look for services that offer preassembled business capabilities and deliver role-specific application experiences. What are composable services? As per Gartner, “composable business means creating an organization made from interchangeable building blocks. The modular setup enables a business to rearrange and reorient as needed depending on external (or internal) factors like a shift in customer values or sudden change in supply chain or materials.” [1] The Gartner structure for a composable business is an organization-level construct that has implications for business strategy, vendor sourcing, technology/architecture decisions, and organizational models that redefine the relationship between business and IT. A composable business delivers business outcomes through an assembly of packaged business capabilities. [2] ” src=”https://www.tavant.com/sites/default/files/html-page-assets/composable–enterprise.png” alt=”Composable services” width=”724″ height=”515″ style=”box-sizing: border-box; border: 0px; vertical-align: middle; max-width: 100%; width: auto; height: auto; float: none; padding-bottom: 35px; padding-right: 10px; padding-top: 10px; display: block; margin-left: auto; margin-right: auto;”> To be future-ready, organizations need to have all components like customer journey, role-specific priorities, vertical domain plans, application experiences, cloud-native architectures, policy, and regulations in one place. In their cloud adoption journey, organizations should factor in composability and reusability to scale it across the organization and meet business objectives. Once these building blocks are defined and available, it becomes easy for the organization to design modular systems with cloud and create services for evolving customer needs and mitigate any disruption, be it a pandemic or a regulatory change in the market. Companies need to derive value from their cloud platforms by adopting them as a business-technology transformation. They need to invest in the business domains to increase revenue and better their margins. The business strategy and risk assessment should decide the technology selection process and the operating model developed around the cloud technology. Cloud investment priorities can vary by domain. [3] ” src=”https://www.tavant.com/sites/default/files/html-page-assets/cloud-based.png” alt=”Composable services” width=”724″ height=”519″ style=”box-sizing: border-box; border: 0px; vertical-align: middle; max-width: 100%; width: auto; height: auto; float: none; padding-bottom: 35px; padding-right: 10px; padding-top: 10px; display: block; margin-left: auto; margin-right: auto;”> Key steps to develop an effective cloud-optimized composable operating model: Implementing an agile way for application development, infrastructure, and security Leverage APIs and event streams to create application experiences that are intuitive and tailored to preferences Develop a software-defined approach to the cloud with infrastructure as code Embed reusability and composability with end-to-end automation Provision workloads on the cloud with dedicated as-a-service business platforms securely   The composable business will be custom-defined for individual organizations, based on the business model, competitive landscape, and operating markets. So, the company should start with defining its long-term vision, evaluate cloud infrastructure and application experiences, and decide the business capabilities it needs from its composable organization. Composability is key to the success of cloud-related strategies in today’s changing business environment. This will enable organizations to have a unified view of the application experience, infrastructure stack, and security needs of the technology systems. More important, it will be pivotal in building the foundational structure for the IT organizations of the future meant to deliver intended business success. Source: Accelerate digital transformations through cloud platforms Gartner Keynote -The Future of Business is Composable Gartner – Future of Applications: Delivering the Composable Enterprise, 11 February 2020 ID: G00465932

Cracking the AI Implementation Code by Operationalizing AI

AI is increasingly affecting our daily lives as more and more tools are using AI. There is no doubt that enterprises are taking a serious interest in adopting artificial intelligence and machine learning. But the knowledge of how it must be deployed to accelerate automation and transform business processes is still in its nascent stages. ” src=”https://www.tavant.com/sites/default/files/blog-image/Operationalizing%20AI%20blog.jpg” alt=”Operationalizing AI” width=”724″ height=”408″ style=”box-sizing: border-box; border: 0px; vertical-align: middle; max-width: 100%; width: auto; height: auto; float: none; padding-bottom: 35px; padding-right: 10px; padding-top: 10px; display: block; margin-left: auto; margin-right: auto;”> AI IS HERE. BUT WHO REALLY HAS IT? AI is making inroads into our lives. According to a new KPMG survey of industrial manufacturing business leaders, the next two years will see AI technology having varying impacts on industry needs: 21% for product design, development, and engineering, 21% for maintenance operations and 15% for production/assembly. In fact 61% of business leaders believe that increased productivity is the most significant potential benefit of AI adoption . Despite the interest, enterprises across industries still struggle with the process of AI implementation to achieve predicted business benefits. HURDLES TO PRACTICAL AI IMPLEMENTATION As companies race to digitize and embrace edge technologies, the transition often requires business leaders to shift their thinking from traditional software engineering expectations. There are many reasons AI and machine learning models don’t necessarily pay off. Some of these include: 1.Lack of Qualified Data Scientists Data science is an essential aspect of developing a suitable machine learning methodology. But the growth of data processing in AI has led to a demand for data scientists who can help turn raw data into business value. This shortage can be overcome by either outsourcing the ML model development or training employees already working with data in ML model programming. 2.Poor Data Quality AI and machine learning tools rely on clean data to train algorithms. And businesses that do not have control over their data quality or data management will struggle to make their AI initiatives successful. Data engineering enables enterprises to maximize the value of their data assets. By working towards enabling cleaner data sets, businesses can deploy machine learning algorithms to design accurate predictive analyses. 3.Undefined End Results What performance metrics are to be measured when developing and selecting machine learning models? Businesses often fail to know the desired level of performance before an AI project begins, leading to a mismatch between model results and expectations. Understanding the project deployment maturity levels can help leaders understand the progress needed to adopt AI successfully. 4.Difficulties in replicating ML model results Incremental data and different environments often cause ML models to perform differently. ML models need to be updated or refreshed to account for data drift, deterioration or anomalous data.  Rather than upgrading the ML model every time, businesses need to create repeatable modelling processes to ensure continuous learning happens during production. FROM EXPERIMENTATION TO EXECUTION Operationalizing AI involves combining ML learning methodologies with software engineering principles to create a production-grade solution. Using established frameworks can help companies find a starting point to formulate best practices to go forward. Atul Varshneya, VP of the Artificial Intelligence Practice at Tavant, has detailed an approach and points to consider for businesses looking to operationalize their AI initiatives. If you are looking for ways to move your machine learning projects from experimentation to execution, watch this recorded webinar. Are you looking to overcome the challenges in operationalizing AI for your business? If yes, then reach out to us at [email protected]. Source: Impact of AI on industrial manufacturing (kpmg.us)

5 Qualities of the Best Mortgage Lending Software

Other mortgage lenders are promising to complete the home loan approval process in as low as 24 hours. If you don’t understand how this is possible, your bank probably takes weeks to complete this process. What you don’t realize is that your competitors are using mortgage lending software. So, to keep up, you should also consider investing in tools like this. By now, you’re wondering how to find the perfect digital mortgage lending software. You want a tool that helps you improve the customer journey. Also, you want digital lending software that automates various processes and helps you get rid of the tedious manual work. So, how do you find this amazing tool? Continue reading this blog to learn the five qualities of the best mortgage lending software. Check the User-Friendliness of the Digital Mortgage Lending Software As a mortgage lending company, most of your employees’ area of expertise is finance. So, you need to consider this when searching for the best digital mortgage lending software. You don’t want complex software that your employees will struggle to use. That’s why you should check the user-friendliness of various digital mortgage lending tools. Ideally, you’ll want to look for software with a simple yet elegant interface. That way, it will be easy for your employees to navigate through the functionalities of this software. To find this easy-to-use software, reach out to the top digital lending solutions company. You’ll find out that this company invests heavily in research and development. It aims to learn more about the needs of its clients. So, this company understands your problems as a mortgage lender and offers software that solves them. Besides, this company has simplified the functionalities of this software to make it easy to use. That’s means you don’t need to spend any money training your employees on how to use this mortgage lending software. Go for a Customizable Digital Lending Software To get an edge over other mortgage lenders, you need to do things differently. You must be bold and look for customizable digital lending software. You want to set yourself apart from other companies that use a general digital lending platform. The reason is that general software will not fully meet your company’s needs. Also, you may be forced to change your mortgage lending processes to fit the functionalities of this software. Not only is making these changes inconvenient, but it’s also costly to your business. So, you should check the customization options of different digital lending solutions to choose the best one. You’re looking for software that you have a high degree of control over its customization. That means you can tailor this software to need your consumer lending needs. Besides, when checking software customization options, check its scalability. You want to see if the software gives room for your changing needs. For instance, if it can effectively handle the growing number of mortgage applications. Review the Security Features As a mortgage lender, you have a moral and legal duty to protect your clients’ data. Besides, you need to keep your operational information secret to maintain an edge. So, you need to ensure that all your computer resources are secure to achieve the above goals. That’s why security is one of the key things to check when searching for the best digital mortgage software. You want to check whether this software restricts data access only to authorized users. Also, you’re seeking insights into how this tool authenticates user identity before granting permission to access data. In addition, you want to know how this software stores the data and if it offers recovery options. So, the best digital lending software is the one with reliable security and data recovery features. Thus, you have no worries about a data breach when using this secure digital lending software Examine the Accuracy of the Digital Lending Platform One of the causes of a terrible customer journey is when a client’s mortgage falls through in the last minute. It’s even more frustrating to the customers when they learn they qualify for the house loan and the rejection was due to a software error. So, you need to look for ways you can prevent this from happening. On the other hand, you don’t want lending software that ignores key requirements and awards loans to people who don’t meet all requirements. The reason is that these people will have a hard time repaying the house loan. Besides, it’s costly for your business to deal with many delinquent loans. To manage all these problems, look for a digital lending platform with a high level of accuracy. With the help of this platform, you’ll ensure that only qualified people’s loan applications are approved. Besides, you’ll avoid denying house loans, people who meet all the set eligibility requirements. Evaluating the In-Built Reporting Feature One of the biggest advantages of using computers as a mortgage lender is the ease of preparing reports. With these reports, it’s quick and simple to evaluate the performance of your business. Also, you’ll rely on these reports when developing a strategic plan for your company. So, when searching for the best digital lending software, you must evaluate the inbuilt reporting function. You want to see if this software generates any kind of reports from the data you input. Also, you’re seeking details on whether you can tailor this software to generate reports that meet your needs. To get value to choose the best digital lending software that generates comprehensive reports. With these reports, you’ll quickly analyze trends and predict your clients’ needs. Get an Edge by Investing in the Best Digital Mortgage Lending Software To get an edge over other mortgage lenders, you need to get the best digital mortgage lending software. You want user-friendly software, which can be tailored to meet your needs. Besides, you should also invest in secure digital lending software to protect your business and clients’ data. Do you want to learn more about how mortgage lending software works? Then request a demo by filling out this form. FAQs

Revamping Security Paradigm in the Evolving Technology Landscape

The COVID-19 pandemic forced governments around the world to impose strict travel restrictions and encourage employees to work from home. Technology and connectivity suddenly became more important as companies worldwide scrambled to keep functioning under difficult working conditions. But the IT infrastructure of many companies wasn’t prepared for the rise in cyberattacks. While businesses were grappling with keeping their doors open, studies found that only 38% of companies had a cybersecurity policy in place. Before the pandemic, 20% of cyber-attacks used previously unseen malware or methods. After the pandemic, that number rose to 35%, with large companies such as Honda and Canon succumbing to malicious attacks. Why Cyberthreats Increased During the Pandemic  With nearly half the U.S. labor force is working from home, employees are sharing more data remotely through apps, increasing the risks for their employers. Last year the FBI reported that the cyberattack complaints to their Cyber Division rose by 400%, reaching as many as 4000 complaints every day. Let’s look at some of the key causes: Employees work from home in environments with limited or absent security IT infrastructure of businesses are not technologically prepared IT security teams is dispersed, and learning how to minimize threats remotely Hybrid Workforces and the Future of Security   One of the critical questions raised is whether businesses will go back to centralized offices or continue to leverage remote workers. The answer appears to be both, as CIOs globally gear up to manage a hybrid workforce. A Gartner survey found that 80% plan to allow employees to work at least part of the time remotely after the pandemic. 47% will allow employees to work from home full-time. As a result, many CIOs and IT leaders seek ways to manage their remote workforce’s security to be better prepared against future attacks. The Importance of Security Testing  Today’s new work scenario has raised the importance of improved security and a planned approach to managing security. Breaches can impact brands, customers and even bring in legal repercussions. Companies, therefore, need to focus greater attention and resources on cybersecurity awareness training. Security teams need to be involved during software development to safeguard applications. Security testing can help manage issues related to confidentiality, authorization, authentication, availability, and integrity at every stage of the development process. The New Normal of Cybersecurity To keep themselves and their products secure, enterprises have begun working with security testing services and ethical hackers. Both internal programs and software development can be positively impacted by applying advanced security testing services, including test automation, performance, quality engineering, and digital assurance testing. Cloud Security Testing Cloud-based security testing involves testing newly developed applications for performance, assessing the security of current operating systems and applications on the cloud, vulnerability testing and security assessments via the cloud. Application Security Testing By using a combination of testing tools and techniques, businesses can avail application security testing to ensure their software products are resistant to threats. Application security testing also helps software developers by identifying the applications’ security weaknesses and exposing vulnerabilities in the source code. These can then be rectified before they become a bigger problem. Web Security Testing Security testing for web applications looks for holes and vulnerabilities which hackers could exploit. The web security testing method uses advanced tools and techniques to explore weaknesses, technical flaws and ensure data protection. Mobile Security Testing Mobile phone usage has resulted in increasing attacks through mobile applications. Mobile Security testing exposes vulnerabilities in mobile applications by testing for activities such as data flow and leakage, storage capabilities, authentications, encryption, and regulatory compliance. Security Compliance Testing Compliance is essential to protect against threats to your products and protect your company against legal issues related to attacks on your software. Security compliance testing will focus on ensuring specific industry-based legal compliance are maintained. Network Security Testing In a hybrid work environment, network security testing is critical. Network security testing identifies and vulnerabilities across any type of electronic data network. It also helps businesses shore their defenses and eliminate any security weaknesses within the company network. Penetration Testing By simulating a threatening attack, security testing experts can help identify vulnerabilities in your applications and products over networks, cloud, and web. Penetration testing helps measure and identify system health and any compromises being made both internally and externally. According to Cybercrime Magazine, digital attacks are predicted to inflict damages totaling $6 trillion USD globally in 2021. By seeking improved security testing services, businesses can manage their cybersecurity with greater effectiveness. Companies that wish to protect their business while ensuring employee productivity will find the time to implement security testing and prevent losses due to cyber-attacks.

The Power of Digital Out of Home (DOOH) Explained

What is DOOH? DOOH (Digital Out-Of-Home) media is the term that refers to any digitized display advertising that appears in a public environment. This includes digital billboards, outdoor signage, and networked screens found in even businesses-oriented gatherings areas such as stadiums, malls, and hospitals. DOOH has been gaining popularity for several reasons. But primarily offer tremendous reach and control to the advertiser while catching the audience’s attention more effectively than static billboards. In fact, a 2015 study by Nielsen found that 75% of respondents recalled seeing a digital billboard in the month prior, and 82% of those recalled seeing advertising specifically. At a time when traditional advertising is often seen as a nuisance, DOOH could be the novelty that marketers are looking for in the advertising world. How is DOOH better than OOH The critical difference between DOOH and OOH is one word. Digital. OOH or Out Of Home is advertising that also reaches people outside of their homes in public places. But these are either static billboards (with fixed images) or electronic. In contrast, Digital Out Of Home advertising is dynamic.  This means that the content can be changed anytime to any ad or information from a networked computer. Additionally, DOOH allows for personalized advertising based on individuals viewing the displays. Real-time Messaging  DOOH offers advertisers the ability to update their messages in near real-time. This means a far greater capability of testing messages in various locations. OOH communication, however, cannot be updated as easily as DOOH ads. Vendors can also offer advertisers and network digital display network owners ad insertion capabilities by implementing client-side or server-side ad integration with third-party or in-house ad servers. Programmatic Content Programmatic DOOH advertising works similar to online advertising but for public ad spaces. The advertiser uses a platform and creates their campaign, providing targeting, scheduling, placement details. Ads are then run on public digital boards that match the advertisers’ requirements, saving tremendous time and effort. This was never possible with OOH advertising and is one of the reasons Programmatic DOOH has quickly become a leading revenue driver for overall advertising. In fact, programmatic buying accounts for 40% of all revenue, netting an estimated $4 billion in 2018. Dynamic Advertising DOOH advertisers are only just beginning to explore the range of capabilities they can achieve by combining DOOH content with technological capabilities. For example, by analyzing weather data, DOOH can be programmed to change content depending on whether it’s sunny or raining. They can also be dynamically changed based on unforeseen events. For example, if there are flight delays, restaurants can create offers. Additionally, using image recognition allows ads to switch based on sensing the demographics of the viewers! Reporting & Analytics One of the critical benefits of DOOH over OOH is that media buyers pay only for impressions and received detailed reports on the campaigns that they are running. DOOH campaigns can also generate viewership analytics, similar to online ads. This is very useful to both marketers and network operators. It offers data such as proof-of-play, report scheduled, and any incidents, which allows the advertiser to stay on top of their ad campaign. Additionally, advanced analytics technology vendors can use this data to help advertisers see real-time operational metrics through model building. In Conclusion Given the flexibility over messaging and greater control over targeting and reports, DOOH is becoming central to digital marketing campaigns. And with platforms offering easy purchase of DOOH ads, it’s not surprising to know that Upbeat predicts the DOOH market will see $8.5 billion by 2023. SOURCES:

From Cost Center To A Competitive Advantage: Warranty Management In Manufacturing Today

We have all experienced that moment… staring at a dysfunctional product and wondering what the repairs will cost us. And more importantly… is the product still under warranty? As a customer, it seems like a solid and straightforward customer service process, yet the warranty management process is viewed as a manually intensive administrative task that’s not exactly the most productive or efficient. Warranty management is a critical set of processes and activities within service life-cycle management. While manufacturers may have viewed the warranty management process to be a drain on resources and revenue, technology has changed the game. Technology: The pill to warranty management headaches In an increasingly competitive world, manufacturers are beginning to recognize the opportunity technology offers. Not only in terms of minimizing costs but also in enhancing customer experience through optimization of the warranty management process. By optimizing warranty processes, manufacturers can reap financial and operational benefits and positively impact their entire service life-cycle: increased asset reliability, better production, and improved supplier relationship management. Studies show that the market for warranty management systems is valued at $2.87 billion in 2019 and is expected to reach $6.24 billion over the next five years, growing at a CAGR of 13.8%. Let’s look at some of the issues that warranty management solutions can resolve: Problem: Multiple Stakeholders The warranty process runs across multiple partners, which can be both internal and external, to the manufacturer. It’s easy to lose sight of metrics within this network of dealers, partners, and OEMs. As a result, product performance, usage, and ultimately, the customer experience gets affected. Technology Solution: Warranty Management Systems Software solutions can radically improve connectivity across the service life-cycle. Warranty management solutions can help reduce claims processing by 70%, by bringing all stakeholders together into a seamless system. Warranty Management Systems can positively impact collaboration with suppliers, channel partners, and customers by enabling manufacturers to focus on functionality that provides visibility into a product’s warranty life-cycle. Problem: Information Siloes The lack of transparency has many manufacturers struggling to gain visibility into their customer or product usage. This is especially true in industries where products are sold through dealers, retailers, distributors, resellers such as automotive or other equipment and machinery. With so many stakeholders involved, it is apparent that information is disconnected, leading to inefficiencies, delays in new product introduction, and ultimately dissatisfied customers.> Technology Solution: IoTs and Closed Loop Collaboration The ability for warranty operations and other service processes to leverage connected product data hinges on the number of connected assets. While the data is available, legacy systems often prevent visibility across relevant teams. By connecting smart devices and sensors and the data generated by machine networks, manufacturers can access a new level of insight needed to intelligently update the warranty claims processes. Problem: Time-Consuming and Fraudulent Claims One of the reasons warranty management is considered to be a costly affair is due to the amount of time and resources needed to investigate warranty claims. Time to process a warranty claim often requires customers to wait without their functioning product leading to increased frustration. Where the processing is still manual, errors and fraud can further derail the warranty management process. Technology Solution: AI & Machine Learning AI-driven real-time analysis of warranty claims helps understand behavior based on environment and recommend efficient product usage. This technology can help improve performance, reduce failure rates, and enhance productivity. Additionally, as more data is fed into the system through IoTs, machine-learning can enable automated warranty processes to make recommendations at a much faster pace than ever before. Problem: Duplication of Efforts Redundancies in data can cause a lot of problems in warranty management processes. It is estimated that more time and money is spent that more time is spent on administrative tasks such as updated customer information than on resolving the problem. This duplication of data inadequacy can often result in errors and more siloes within the service life-cycle process. Technology Solution: Automation Automated warranty claims systems can enhance process efficiency by reducing or eliminating manual efforts in claim submissions such as parts return and payments, turnaround time, and increasing productivity. Data accuracy will also be improved human error can be circumvented, reducing errors and redundancies. The future indicates that with the increasing use of smart devices, wearables, and smart home units, adopting a warranty management solution will likely enable an unparallel customer experience. Technology partners that offer warranty and service contract management expertise, which leverage AI and ML, will provide a distinguished advantage to the manufacturers. And manufacturers that quickly move towards a system to take advantage of these capabilities can secure their business competitiveness through increased customer satisfaction and enhanced product quality. SOURCES: https://www.mordorintelligence.com/industry-reports/warranty-management-system-market

Electric Vehicles and their Impact on Automotive Warranty Management

The falling costs of the lithium-ion battery pack, coupled with the rising concern of climate change, in addition to policy incentives, rising incomes, and technological advancements have pushed the adoption and sales of electric vehicles (EV) this last decade. The Growth of the EV Segment Bloomberg NEF’s Electric Vehicle Outlook study forecasts that EVs will hit 10% of global passenger vehicle sales by 2025, growing to nearly 28% in 2030 and 58% in 2040. In the US alone, the number of brands offering EV options will grow from 16 now to at least 40 in 2025, with electric vehicles offering consumers a wide range of segments and price points. In addition to climatic awareness, governments are also driving change. US President Biden began his term with an order to electrify the federal fleet of about 650,000 vehicles, investing in over 550,000 public charging points, and taking initiatives to bolster the domestic supply chain for critical technologies and raw materials. The Impact of EVs on the Automotive Industry All of this indicates that the next decade will see a major shakeup in the automotive industry. One example is an estimation by Ford, which believes that the simplification in the assembly of EVs could lead to a 50% reduction in capital investments and a 30% reduction in labor hours, as opposed to internal combustion engine (ICE) manufacturing. Similarly, as the number of electric vehicle consumers grows, other aspects of the automotive industry including automotive design, supply chains and production processes will undergo a transformation as well. As vehicles become more computer-dependent and less combustion engine related, learning gaps are expanding for technicians and even drivers, as they learn to handle what is essentially a new operational vehicle. EV Trends and Future of Warranty Management The automotive industry is already bracing for a shake-up when it comes to repair and parts management. With the surge in Electric Vehicles across the world, we can expect the OEMs to face an increase in the volume of technical warranty requests from their dealers. And because every vehicle is different, there will be a significant shift in how to handle these claims initially. The service and maintenance of an electric vehicle are likely to be highly different from a typical combustion engine, to which the industry has so far been geared. Not only do EVs have fewer mechanical parts, but some components also (such as plugs and sockets, inverters and powerpack coolers), aren’t part of the existing automotive warranty service. How Connected EVs Change the Repair Game There are also several different types of EVs, such as hybrid and connected vehicles, which could further add more complexity to the issue of long-term service and maintenance. IoT devices are also enabling vehicles to stay connected and detect vehicle failures even before they physically reach the service center. That means connected EVs will offer vehicle owners the ability to self-service and distinguish between actual car failure and driver solvable issues. This will impact automotive servicing to a greater extent as manufacturers may need to include ability to educate the driver if vehicles are brought in with no actual failure. There are also likely to be more auto repairs in the field, as the parts get smaller and more computer-like. This could have a significant cost and operational impact on manufacturer warranties as mechanics (with computerized knowledge) travel to the customer rather than the other way around. Smarter Cars, Smarter Warranty Management To manage this transition, OEMs will need to become smarter about their warranty management processes. By using technology to examine warranty claims, OEMs can bring increased efficiency and transparency into a complex process. This will, in turn, enable OEMs to offer superior customer-centric service. The next generation warranty management software solution leverages artificial intelligence and machine learning capabilities in order to identify and understand patterns in warranty claims. Additionally, software solutions such as end-to-end warranty lifecycle management can help OEMs reduce costs in warranty management, increase supplier recovery, and improve aftermarket sales support. Warranty management solutions can also be used to handle increased volumes in claims processing easily. Through machine learning and image recognition, the system can be trained to recognize parts and models automatically, or even detect fraud claims, saving manufacturers a tremendous amount of time and money. Shifting Gears to Stay Ahead The transition to smarter electric vehicles and the potential phasing out of combustion engines is likely to be a game-changer for many.  Automotive manufacturers and suppliers are making key investment and technology decisions about the next generation of vehicle and components manufacturing, already. Forward-thinking OEMs will need to tackle their challenges by using technology solutions to enable transparent cooperation with partners to offer sourcing, supply, and maintenance benefits and future proof their business. SOURCES: The future of cars is electric – but how soon is this future? The Auto Industry and EVs: Where We Are and What’s Coming Next, After Years of Crying Wolf? Plugging Into The Future: The Electric Vehicle Market Outlook

Making Warranty Management Profitable for Manufacturers

Traditionally, manufacturers offered warranties to buyers to assure buyers of the quality and longevity of products or services. The manufacturer (or the seller) would typically cover the repair or replacement of the products within a period of time. The industry has progressed a long way from there. With the increasing complexity of production and the supply chain, warranties today include everything from product coverage to customer support and logistics. But today, with distribution and supply chains spanning continents, how do manufacturers stay on top of their warranty information and claims management? What is Warranty Management?  IDC Manufacturing Insights defines warranty management as the stages of the warranty process, including registration, claim capture, claims validation, early failure detection, recalls, parts returns, adjudication, extended warranty service, supplier recovery, and reserve optimization. Professional claims management for manufacturers often comprises the paperwork for warranties, checking validity in case of fraudulent claims, and ensuring fast, efficient and cost-effective processing while fulfilling warranty claims. Managing warranties often includes the stakeholders (and their roles)  in ensuring customer satisfaction with a product or service. If you are looking for a Warranty Management system that can help you reduce backlog, and minimize claims processing time and paperwork, check out Tavant’s solutions.  The Impact of Digitization on Warranty Management With increased digitization, more manufacturers are turning to technology solutions to optimize the warranty management process. While manufacturers have traditionally viewed warranty management as a necessary evil, studies now prove that the right technology can help manage warranty information and claims processes more effectively, which can positively impact revenue. What is Warranty Management Software? Warranty Management software allows manufacturers to optimize their warranty management process by helping them create, monitor, process, and track warranties. Warranty management software enables users to stay on top of claims, coverage, and customer requests across the entire service life-cycle. Today’s warranty software products are also AI-enabled, allowing manufacturers to use machine learning capabilities to automate time-consuming functions. This helps improve the warranty process and ensures a superior level of customer service while delivering profitability. Benefits of a Warranty Management Software In general, warranty management software helps ensure uniform standards in warranty processes while providing increased transparency and communication between manufacturers, suppliers manufacturers. Let’s look at some of the additional specific benefits: Reduce warranty costs Technology can help manufacturers streamline processes in a way that can save time and shorten the service life-cycle, and as a result, reduce costs. APCQ (American Productivity & Quality Center) found that the more digitally mature an organization’s supply chain, the lower the warranty costs. Costs are found to drop from 3.5 % of sales when decisions are made based on analyzing past actions. Improve aftermarket excellence Reduced errors, faster resolutions, and accuracy in troubleshooting are the benefits of a warranty software solution, which improves customer satisfaction and builds a reputation for aftermarket excellence. Gain real-time intelligence  Due to the digitization of many aspects of the manufacturing process, proper warranty management can offer manufacturers the ability to view in almost real-time the service experience and product usage across the entire service life-cycle. As data starts getting captured by more connected devices such as IoTs and automated warranty systems, manufacturers can analyze and avail near real-time insights into product performance and service status. Reduce fraudulent activity AI-based systems are using image recognition to identify fraudulent claims in a faster and more cost-efficient way. Machine learning algorithms are trained using thousands of images and can detect real issues against digitally manipulated images or past claims. Improve processes Warranty management software offers a closed-loop approach that can help optimize the claims procedure. By reducing operational discrepancies, warranty management can help manufacturers improve the process incrementally. Increase visibility between teams Often, teams across the service life-cycle don’t have visibility into warranty information, products, assets, and customer information.  Access to service data can help all stakeholders have clear communication and visibility, improving collaboration. Optimize revenue Warranty management systems can help reduce losses incurred due to logistic delays or fraudulent claims. Time spent analyzing claims can also be minimized, saving resources and enhancing productivity, which can make warranty management a more cost-effective process. Tavant offers manufacturers an AI-driven, next-generation warranty management solution, which has helped organizations reduce warranty costs, increase supplier recovery, and improve aftermarket excellence. Talk to us for more information. SOURCES: https://tavant.com/products/warranty-management/ https://www.sdcexec.com/sourcing-procurement/article/21196030/apqc-metric-of-the-month-reducing-warranty-costs-as-a-percentage-of-sales https://www.idc.com/

Data Analytics: A Catalyst for Change in Service Life-cycle Management

The past few years have seen manufacturers look at their aftermarket services management in a completely new way. While technology and digitization have largely driven this change, the recent global pandemic has rocketed the drive for remote yet effective service support to ensure that customer requirement are still seamlessly met. Tech Innovations and the Flood Called Data The inadvertent result of this upsurge in digitization has been the data. Data, which is often collected from disparate sources, is now becoming a big challenge and an opportunity for manufacturers. With the adaptation of technology, many manufacturers can capture and utilize data but fail to do so. Why Measurement Matters Data-driven manufacturing is in the realm of being seen as a strategic necessity that can help manufacturers compete effectively. And with the application of analytics, manufacturers, suppliers, and distributors can achieve significant value in speed and operational efficiency. The ability to measure and use data is also leading manufacturers to offer services based on usage, uptime/downtime, and create value for customers through personalization. Let’s look at some of the key uses of data analytics and how it will impact manufacturers. Manage Demand and Supply Chains Data analytics is helping manufacturers understand the cost and efficiency of every aspect of the product lifecycle, from suppliers to customer usage. By analyzing the parameters and conditions that impact the supply chain from all angles, businesses can uncover problems such as hidden bottlenecks or unprofitable production lines. As a result, they gain insight into the conditions that affect the complete profitability of an integrated supply chain and learn how best to capitalize on given conditions. Forecast Demand for Products & Services Manufacturers can combine data with predictive analytical tools to create an accurate projection of purchasing trends. Insights driven by analytics can even help manufacturers understand how well lines are operating, enabling smarter risk management decisions. The ability to analyze when warranties are expiring can also result in additional service revenue channels for manufacturers. IoT solutions for asset management offer real-time alerts, enabling manufacturers to act quickly, and minimize losses from delayed, damaged, or lost goods. Proactive System Maintenance  Predictive maintenance is helping manufacturers increase their product lifetimes while preventing downtimes. It analyzes the historical performance data to forecast potential failure and further identify the cause of the problem. This is particularly effective in field service management, where predictive maintenance can result in tremendous savings. According to McKinsey, manufacturers using predictive maintenance typically reduce machine downtime by 30 to 50 percent and increase machine life by 20 to 40 percent. Optimize Machine Efficiencies and Utilization  Data analytics can significantly improve assembly-line efficiency by identifying bottlenecks and defects. With advanced analytics, manufacturers can ensure that machines operate at high efficiency, resulting in improved quality and increased productivity. Optimize Inventory and Warehouse Costs Efficiently Advanced analytics can be applied to improve product flow management, which positively impacts inventory operations while reducing unnecessary expenditure. For example, manufacturers can assess fill rates which can reduce stock-outs. Improved insights can help manufacturers know which locations/equipment are operating at an optimized level and improve other production centers and address warehousing deficiencies if any. Final Thoughts Enhancements Across the Service Life-cycle Analytics is enabling manufacturers to scale cloud-based operational intelligence, AI-enabled monitoring, diagnostics, and asset lifecycle management. AI-enabled digital technologies are seamlessly addressing service life-cycle challenges, increasing transparency across the process and functions, and creating a seamless and rich experience for the customers. SOURCES: http://www.wonderware.es/wp-content/uploads/2017/02/WhitePaper_InvensysandMicrosoft.pdf https://www.mckinsey.com/business-functions/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability  

Futuristic Tech: Turning the Wheels of Manufacturing

As 2021 rapidly progresses, the impact of the COVID pandemic on all industries is being felt. Businesses have had to maintain employee/customer distances resulting in the growth of communication-enabled technologies. The manufacturing industry has also been similarly impacted. While technology has always been an enabler, today, we are beginning to see advancements in ways that interconnect humans with smarter machines for improved processes, performance, and protection. WEARABLES Wearables are being used in manufacturing to ensure improved safety conditions in work environments. Wearables such as wristbands, smart clothing, or headwear like Google Glasses can provide information to the wearers such as surfaces that are hot, short-circuited equipment, machine malfunction, or even hazardous spills. Wearable technology for construction workers may also soon help reduce construction worker fatalities and injuries. A U.S. Chamber of Commerce report notes 23% of contractors will be using such technology by 2021. Wearables are also helping managers gather data that can help towards improving efficiencies and optimization. For example, it may be observed that time is spent by workers using a forklift for short distances between two essential work points, which can be reduced if the floor plan is reworked or eliminated by a conveyor belt. IOTs The manufacturing industry has already deployed IoTs extensively to collect essential production data and turn it into valuable insights. IoTs have helped manufacturers improve operational efficiency and reduce delivery times. The PwC’s 2019 Internet of Things Survey reveals that manufacturers are optimistic about the benefits of IoTs with 68% planning to increase their investment over the next two years. By deploying enhanced IoTs that are sensor-enabled (audio, video, temperatures, vibration, voltage), manufacturers are now also staying ahead of potential machinery issues and can perform predictive maintenance for improved customer satisfaction. Recently a Boston-based contractor used an algorithm that analyzed photos from job sites, scanning them for safety hazards and correlated with past accident records. Construction companies can therefore potentially compute project risk and know which projects have higher threats. DRONES While there are still technological, organizational, and regulatory challenges to implementing drones, manufacturers have begun experimenting with drones in warehouse operations and inspection tasks. Drones are being used to monitor and connect the stages of the manufacturing process, such as moving components to a production line, inspection, or delivery of the final product to shipping. And when regulatory limits are removed, many manufacturers are looking to deploy drones to help them with field inspection and logistical tasks.   AR/VR TECHNOLOGY Virtual reality can help manufacturers test and enhance products digitally without creating expensive prototypes, saving time and money. Automobile manufacturers are already using virtual reality to ensure cars are tested at an initial phase of the vehicle development process, reducing the time and cost involved in ensuring tolerances and safety and altering the design features. In construction, custom workstations are being built using virtual reality to offer teams immersive design review and collaboration capabilities. Augmented reality can be used to monitor field conditions, measure changes, and help manufacturers envision a finished product. SMART ROBOTICS Industrial robots have been speeding up manufacturing operations for the past decade. In fact, in another recent PwC report, 59% of manufacturers are already using some form of robotics technology. Today, however, we are beginning to see more AI-enabled robots that collaborate with human workers. The Tesla Gigafactory uses smart self-navigating, Autonomous Indoor Vehicles (AIVs) to shift goods between workstations. Companies like Cornell Dubilier, a power capacitor manufacturer in the US, also use ML-trained robots to inspect capacitor installations, doubling its speed of labelling process from 125 parts an hour to 250 parts an hour. AI AND MACHINE LEARNING AI and machine learning make it possible for manufacturers to improve processes and products through intelligent feedback, which the algorithm can constantly learn from. According to Deloitte, machine learning improves product quality up to 35% in discrete manufacturing industries. Even the construction industry (considered one of the most under-digitized industries in the world) uses AI to predict cost overruns based on project size, contract type, and the competence levels of project managers. AI-driven cameras also help construction workers avoid spending hours walking around searching for tools, as AI can immediately recognize and locate on-site tools and equipment. Advanced machine learning systems offer smarter decision-making capabilities to manufacturers and can improve tasks such as research, development, and product line extension. 3D PRINTING While advances in 3D printing have helped streamline prototyping, one of the most encouraging outcomes is its potential for mass customization. The 3D printing industry is projected to reach USD 63.46 billion by 2025 and is offering manufacturers the ability to innovate through its introduction of new materials. Unbelievably, 3D printing also offers builders the ability to produce entire houses! Start-up 3D printing construction company, Icon says that 3D printing can reduce construction costs by up to 30% and produce a home twice as fast as traditional methods. BIG DATA AND ANALYTICS The adoption of sensors and connected devices have resulted in a tremendous increase in the data points being generated for the manufacturing industry. Only by applying advanced big data analytics, can manufacturers use this vast information to discover insights and identify patterns. When properly deployed, analytics can help manufacturers improve processes and supply chain efficiency and predict the variables that could adversely affect production. Additionally, big data and analytics can be applied to assess damages in buildings and fraud detection. Fraud analytics using machine learning to prevent contractors from making false claims by analyzing images against existing claim databases. This is particularly useful in construction such as roofing, which needs to be viewed from above and can easily be faked. Advanced analytics can automate the processing of roof condition and minimize the need for aerial imagery which be expensive, time-consuming, and unsafe. THE FUTURE IS IMPROVEMENT, NOT DISRUPTION Successful manufacturers are often the businesses that can orchestrate and align all the facets of their operations smoothly. Therefore, the use of technology in manufacturing is mainly driven by their need for better efficiency and control over

Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects

As discussed in my previous blog posts, a lot of research is being done in ad attribution and media mix modeling. Today I’ll introduce another paper that provides some interesting analysis. Fair warning, you should have a basic idea of Bayesian regression before reading this. You can find a great introduction here. Carryover and Shape Effects The authors’ most exciting contribution is incorporating carryover and shape effects in their media mix model. Carryover effects try to model the impact of media spend over a future period. Since media spend influences consumers on buying a product or service, the impact of such spending doesn’t just last for the time an advertisement is aired but for a more extended period. The authors transform the time series of media spend using a decay function for accounting for such carryover effects. They use the adstock function as described below :   wm is a non-negative weight function, and the media spend effect is the weighted average of media spend of the current period and previous L-1 periods. The authors introduce two types of weight functions, geometric decay (where media spend peaks when an advertisement is aired) and delayed adstock (where the impact of media spend rises sometimes after an ad is aired). A visualization describing the effects of the weight functions can be seen below, taken from the paper. Next, the authors discuss shape effects. Shape effects aim to capture diminishing returns on media spend. For example, it is valid to assume that for a specific medium, the rate at which media spends rises is dramatic from 0 to $50 but reduces significantly from $100 to $150. The authors use a Hill function to model shape effects. The discussion of Hill functions is beyond the scope of this blog, but the regression coefficients can be multiplied by the Hill function to get the following form : The hill function for media spend is a point transformation, as opposed to earlier discussed carryover effects. The following graph, taken from the paper, gives a visual representation of diminishing returns, given different parameter values in the Hill function : Both these transformations can be applied to media spend. Depending on individual use cases, one must decide which transformation to apply first. The authors apply the adstock transformation first and then the shape transformation. The final sales at time t, which can be described as y_t , can be modeled using the following equation : To simplify, this equation models sales as a function of some baseline sales τ in addition to transformed media spend effects of control variables, and random noise. Why Bayesian Regression A common question could be: Why estimate these parameters using bayesian regression? The answer lies in the fact that Bayesian regression lets us quantify the uncertainty in our predictions, and more importantly, allows us to set priors on our parameters. For example, it is valid to assume that media spend will never have a negative effect on sales, which allows us to set informative priors on media spend coefficients (constraining them to be non-negative values). The authors then explain their implementation of this model to real-world datasets. They use Gibbs sampling to sample from their model and implement this in STAN. However, multiple techniques for sampling from the posterior distribution and their code can be replicated easily using PyMC3. Please take a look at the fundamentals of Bayesian Regression if this isn’t making much sense. The parameter estimates obtained from the model can then be plugged into a linear optimization algorithm that conditions on a fixed media spend budget to find the best media mix given a set of channels. The linear optimization algorithm introduced by the author is beyond the scope of this post, but I might discuss it in my next one. Stay tuned!

How Cloud Technology Can Leapfrog Your Business IQ

Agility. Innovation. Intelligence. When working in tandem, these three concepts can spell success for any business. But without these capabilities, businesses falter in the eddies of unfavorable market conditions. When business leaders get together, the discussion often gravitates to how companies can nurture and empower new ideas and implement them faster because this is what keeps any business competitive.   Business Success…and the stumbling blocks called data Across the globe, enterprises are investing heavily in AI, ML, and technologies for improved efficiency, in solutions that communicate with each other seamlessly, and in strategies that empower employees to ideate and innovate. And yet, there’s still a hurdle that most companies continue to stumble over: data. A recent IDC research into enterprises showed that 50% of employees are overwhelmed by the amount of data, while at the same time, 44% say they don’t have enough data to support decision making! What cloud technology can mean to business growth Businesses everywhere are going through a period of transformation. Clunky old legacy systems are being digitally overwritten. Decades-old data silos are shrinking under a barrage of data management solutions. And all of these transformative capabilities are being held up and supported by the cloud. More than a storage system If you’ve been thinking of the cloud as an efficient way to store data, it’s time to upgrade that thinking. The cloud is a platform that can handle data and while also supporting the solutions that process and use that data. By leveraging cloud capabilities, businesses can scale their capabilities up or down with less risk and pursue their business goals while keeping costs low. No downtime Imagine a credit reporting company trying to move 18 years of customer data for 330 m American customers US from GVAP archives. By using cloud technology Tavant was able to dynamically adjust the size of the computing cluster to accommodate the changing workload without interrupting production. Remote access and security The global pandemic has put greater focus on cloud capabilities as companies are forced to maintain data security while enabling employees to work remotely. Consider the competitive advantage experienced by businesses that already had a secure, remote work environment in place before 2020. CAAS (containers as a service)  Businesses can also benefit tremendously from container applications which are now being offered by many cloud providers. As consumable services, these CAAS can be deployed by DevOps directly on top of the cloud application layer. Each app is wrapped in a standardized configuration, significantly improving security, scalability, and load times and providing an efficient alternative to virtual machines. In fact, Gartner predicts that by 2023, 70% of global organizations will be running more than two containerized applications in production, up from just 20% in 2019. Smart businesses are getting smarter with cloud technology Regardless of the business size, cloud technology now offers an easy way for businesses to innovate, respond quickly and empower employees from anywhere. For the first time ever, we have a level playing field from which companies of any size can leapfrog their way into the future and create new business opportunities for themselves using cloud technology. The only question is, who will leverage cloud capabilities efficiently to get there first? SOURCE: https://blogs.idc.com/2020/05/15/defining-the-data-native-worker-gen-d/ https://www.entrepreneur.com/article/345826

Building Trustworthy and Ethical AI is everyone’s responsibility

Whether you realized or not, Artificial Intelligence (AI) has quickly become part of our daily life. With traditional industry and businesses like fintech, media, healthcare, pharmaceuticals, and manufacturing adopting AI rapidly in recent years, concerns related to Ethics and Trustworthiness have been mounting. Today, AI ‘assists’ many critical decisions influencing people’s life and well-being, for example, creditworthiness, mortgage approval, disease diagnosis, employment fitment, and so on. It was observed that even with human oversight, complex AI systems may end up doing more societal harm than social good. Building Trustworthy and Ethical AI is a collective responsibility. We must apply fundamentals throughout the lifecycle of AI, for example, product definition, data collection, preprocessing, model tuning, post-processing, production deployment, and decommissioning phases. No doubt Government and Regulators have a role to play through monitoring and ensuring a level playing field for everyone, the same is for people building, deploying, and using AI systems. This includes executive leadership, product managers, developers, MLOps engineers, data scientists, test engineers, HR/Training teams, and users. Bias and unfairness While Trustworthy and Ethical AI is a broader topic, it’s tightly coupled with the prevention of of Bias and Unfairness. As the National Security Commission on Artificial Intelligence (NSCAI) observed in a recent report: “Left unchecked, seemingly neutral artificial intelligence (AI) tools can and will perpetuate inequalities and, in effect, automate discrimination.” AI learns from observations made on past data. It learns the features of data and simplifies data representations for the purpose of finding patterns. During this process, data gets mapped to lower-dimensional (or latent) space in which data points that are “similar” are closer together on the graph. To give an example, even if we drop an undesired feature like ‘race’ from the training data, the algorithm will still learn indirectly through latent features like zip code. This means, just dropping ‘race’ will not be enough to prevent the AI learning biases from the data. This also brings out the fact that data ‘bias’ and ‘unfairness’ reflect the truth of the society we live in. With not enough data points belonging to underrepresented sections of the society, high chances that they will be negatively impacted by AI decision-making. Moreover, AI will create more data with its ‘skewed’ learning which will be used to train it further and eventually create further disparity through its decision-making. Trustworthy and Ethical AI is important By definition, Trustworthiness means “the ability to be relied on as honest or truthful”. Organizations must ensure their AI systems are trustworthy, in absence of trust, undesired consequences may occur, including but not limited to business, reputation and goodwill loss, lawsuits, and class actions that can be potentially life-threatening for a business. On the other hand, Governments and Society must ensure that AI systems follow Ethical principles for the greater good of common citizens, one great example is UNESCO Ethical AI Recommendations. As per the European Commission Ethics Guidelines for Trustworthy AI, Trustworthy AI must be Lawful, Ethical, and Robust. Respect for human autonomy, fairness, explicability, and prevention of harm are four critical founding principles of Trustworthy AI. It’s critical that AI should work for human wellbeing, ensure safety, should be always under humans’ control, and never ever should harm any human being.. Who is driving Ethical AI? Realization of Trustworthy AI is envisioned through the following actions: Who is driving Ethical AI? Leading tech companies have already announced one or another kind of Ethical AI initiatives and governance. As there is no common ground in terms of benchmark principals, guidelines, and framework, it’s difficult to assess whether the intent is genuine or merely optics. As AI will have a profound impact on society and well being of common citizens, just ‘self-certification’ will not be enough. Governments should (some have started already) define the principles, policy, guidelines and establish an effective oversight and regulatory mechanism. This will help to ensure that common citizens are protected from intended/ unintended negative fallouts of AI. As AI evolves, frameworks and regulations should also evolve. Recently, the US Federal government signed Executive Order On Advancing Racial Equity and Support for Underserved Communities, however, more needs to be done. EU, UN & DoD have already taken the lead on this topic, with European Commission Ethics Guidelines for Trustworthy AI, UNESCO Elaboration of a Recommendation on the ethics of artificial intelligence and US Department of Defense Ethical Principles for Artificial Intelligence should be considered as baseline work towards defining a practical and mature guideline towards Trustworthy and Ethical AI. Plan of action Here we attempt to identify suggested actions for involved actors. This is in no way an all-inclusive list and should be taken as only a baseline and should be updated to support a particular case: Conclusion We all have actions to build Trustworthy and Ethical AI for the larger good of society (and humanity). With coordinated and persistent efforts, it is definitely possible.

Causally Motivated Attribution for Online Advertising

As mentioned in the previous blog post, algorithm-based methodologies for assigning credit to media channels on conversion of a user are becoming more and more popular, replacing archaic methodologies such as first touch and last touch attribution. A paper that goes beyond a regression framework to explain such attributions was presented by Dalessandro et al. which I’ll be going over in the next few sections. Attribution  Attribution and Causality Dalessandro et al. propose a counterfactual analysis to produce estimates of the causal effect of advertising channels on user conversion. There are some strict assumptions that have to be met in order to obtain causality from the data, which Dalessandro et al. state as the following: The ad treatment precedes the outcome (conversion of a user) Any attribute that may affect both ad treatment and conversion outcome is observed and accounted for. i.e., there are no unknown variables acting as confounders. Every user has a non-zero probability of receiving an ad treatment. Obviously, in real life scenarios, conditions 2 and 3 are nearly impossible to prove as true in any attribution analysis. It may be possible that an ad campaign is targeted towards a certain demographic, thus violating condition 3, and it may be very possible that confounders such as users’ biases towards certain products and services are unmeasurable quantities. One can see how this would be a challenge. In the interests of brevity, we will not dwell on the mathematical formulation of such an analysis since the practicality of it is dubious. In the next section, I will discuss an approximate causal model that Dalessandro et al. introduce, which recasts the causal estimation problem as a channel importance problem, with better application to real world data. Channel Importance Attribution Before getting into any convoluted equations, I’ll quickly introduce important notation: C={ C1, C2,…Ck }  is defined as the set of media channels that have shown ads to a group of people W is a vector of user attributes before being exposed to any ads ( for example, demographics, prior internet searches etc.) Y is a boolean indicating whether or not a user has converted, post exposure to ads (γ = Σ Y, n) is the dataset of n users who have seen the same ads by channels in C, and have the same values W = w, producing γ = Σ Y total conversions S is the set C, excluding Ck (hence a subset of C) ωS,k is the probability that set C begins with the sequence {S, Ck, ….} in some distribution Ω of possible orderings The expectation of channel Ck‘s contribution to Y, over all possible combinations of C, is given as Vk, which can be seen in the equation below:  In order to understand this better, consider an example where there are only 2 channels, C1 and C2. Attribution values for the channels can be given as : We can see in this simplified form that the attribution values are affected by how these channels serve their advertisements to the user. It is interesting to note, that in the case of observable ad campaigns, we will already know the order in which channels deliver their ads, making the ωS,k probabilities always 0 or 1. The paper discusses why this observable information can actually be harmful in providing attribution values. Let’s take a look at an example. Consider C = {C1, C2}. Further, let E[γ|{∅}] = E[γ|{C1}] = E[γ|{C2}] = 0, and E[γ|{C1,C2}] = δ >0.. Further, assume that C2 always serves its ads after C1. These assumptions tell us that the individual effects of C1 and C2 cause no conversions among users, but the joint effects of C1 and C2 do lead to some user conversions. Using the formula described above, we can get attribution values as following: Since we have observable probabilities of the sequence in which the channels serve their ads (since C2 always serves after C1), we can note that ω2,1 = 0, and  ω1,2=1, giving us the equation in the form above. What is interesting to note now, is the fact that our attribution values tell us that V1 = 0, while V2 = δ. This means, all the credit for the joint effect of C1 and C2 in our example is going to C2, simply due to the fact that C2 serves its ads after C1. This conclusion is harmful, since we can extrapolate this to a general idea that channels that serve their ads later receive greater credit for user conversions ( it basically turns into a last touch attribution model, which is pretty flawed). Dalessandro et al. recognize that using these observable probabilities lead to poor recognition of interaction effects among channels, and instead propose a different way to calculate the quantity ωS,k. The following equation is the crux of their idea : They define Ω as a uniform distribution over all possible orderings of C. They state that ωS,k can now be calculated as : To completely understand this equation would require a very good understanding of Shapley Values, which are a common concept of attribution allocation in game theory. Due to the limited scope of this blog, I will not discuss it here. But if there’s something to take away from the paper’s implementation, it is the fact that observable probability distributions of ωS,k should be ignored in favor of the equation provided by the authors in the equation above.

The forensic goldmine of smart television viewing

In the United States, the total smart TV household penetration has been increasing rapidly from 2012 onwards, from around about 9% in 2012 to 60% in January 2020. This growth comes from shifting consumer preference towards online content. The wide availability of high-speed internet and the smart features of connected TVs have also contributed to the fast growth of the smart TV market both in the United States and the world. And, added to that, the pandemic has further pushed the consumption of content up even further. Americans spend an average of 3½ hours in front of a TV each day, according to eMarketer, the market research company. With more and more consumers opting for Smart TVs, marketers and publishers today have access to a wealth of consumer information. 

The Forensic Goldmine of Smart Television Viewing

In the United States, the total smart TV household penetration has been increasing rapidly from 2012 onwards, from around about 9% in 2012 to 60% in January 2020. This growth comes from shifting consumer preference towards online content. The wide availability of high-speed internet and the smart features of connected TVs have also contributed to the fast growth of the smart TV market both in the United States and the world. And, added to that, the pandemic has further pushed the consumption of content up even further. Americans spend an average of 3½ hours in front of a TV each day, according to eMarketer, the market research company. With more and more consumers opting for Smart TVs, marketers and publishers today have access to a wealth of consumer information.   Content Recognition & Targeting Automatic content recognition (ACR) technology has the potential to capture all types of TV viewing: linear, OTT, video on demand, commercials, and video games. When tracking is active, Smart TVs can record and send out everything that comes up on the screen regardless of whether the source is cable, an app, the DVD player, or a set-top box, but without personally identifiable information. Once collected, media analytics companies consume the ACR data, then clean, compare and combine it with other data sets to make it more usable and accurate. TV advertisers, therefore, no longer need to rely on Gross Rating Points (GRPs) and have greater capabilities of showing their ads to the right person at the right time. Analytics & Advertising While the world of advertising is moving to digital, TV advertising still accounted for $84 billion in 2018 in the US alone. But data-driven methods are enabling these dollars to be spent more efficiently using automated systems over programmatic TV. Advanced advertising technology enables advertisers to have more control with end-to-end inventory visibility, audience, and demand. Using specially developed software solutions, marketers can integrate and streamline omnichannel advertising and marketing activities. Advanced analytics technology for advertising can help businesses also use ACR data to connect ad spend to business goals, like driving in-store traffic and make intelligent media advertising plans. Data-Driven Media Subscription Management Subscription rates after March 2020 grew between 3 times for digital news and up to 7 times for streaming services as published in the Covid19 Subscription Impact Report conducted by Zuora. By analyzing television viewership data in conjunction with product subscription information, publishers can manage subscription features specific to OTT platforms, such as auto-renewing and churn management. Netflix has claimed that its media recommendation solution could be saving up to $1 Billion a year by decreasing churn. AI-based Viewership Recommendations Content metadata in smart televisions are often only applicable to an on-demand video where there is time to generate it before distribution. Advance recommendations based on prior knowledge are irrelevant in cases like live sports, where viewership is based on expectations instead of prior information. For this reason, operators need to leverage AI/ ML to generate effective recommendations, even for VoD content. Recommendation engines help uncover video content for users that they would not be likely to find themselves. As a result, video and TV services can increase their content reach without having to constantly acquire new content. Tavant specializes in advanced advertising technology and media analytics to help companies gain the most from smart tv data. For more information on how we can help you write to us at [email protected]; or click here. SOURCES: https://www.washingtonpost.com/technology/2019/09/18/you-watch-tv-your-tv-watches-back/ https://dl.acm.org/doi/10.1145/2843948

Data Driven Ad Attribution Models

Marketing today relies on a variety of metrics to gain insight into its efficacy. Given the variety of online and offline channels available to marketers, understanding the impact and interaction of individual channels has become an onerous task, to say the least. Marketers rely heavily on two methods to obtain data-driven insights into the marketing process, Media Mix Modeling (MMM) and Data-Driven Attribution. MMM provides a “top -down” view into the marketing process in order to generate high-level insights into the efficacy of different marketing channels. For example, by looking at data over months or years, MMM can give marketers insight into consumers’ interaction with different marketing media. Attribution models, on the other hand, take a more “bottom-up” approach to the marketing process. These models look at an individual user’s interaction with different media. Since each user is exposed to a combination of marketing channels, the problem lies in ascertaining how much credit to give each marketing channel towards influencing a user’s choice about making a purchasing decision. Historically, marketers have used common attribution models such as last touch (first touch) attribution. Last touch attribution models assign all credit to the last channel (first channel) a user has been exposed to prior to conversion. The flaw in the last touch (first touch) attribution lies in the fact that channels further from (closer to) the conversion funnel are systematically undervalued. To allocate credit more fairly, algorithm-based methodologies have received significant traction in the past decade. In a series of three blogs will introduce three papers that discuss algorithm-based models for media mix modeling and attribution modeling. The dominance analysis approach for comparing predictors in multiple regression (Budescu, 1993) Regression models have become a common way to explore the interaction between revenue and advertising efforts. Budescu introduces a general framework known as dominance analysis that aims to decompose the coefficient of determination (R2). For the sake of simplicity, we will only deal with linear models in this post. Budescu’s work can be extended to any area of research that tries to deal with variable importance. Review of Legacy Methods Various methods have been developed over time to measure the importance of variables. These methods mostly rely on using the coefficients of independent variables from standard linear models to explain variable importance. Let’s look at a standard linear model defined as the following: y=β1 x1+⋯+βi xi+⋯+βp xp+ϵ Let’s denote the coefficient of determination of this model as R2y,X. The vector β= (β1, β2,…..βx) represents the change in the dependent variable y, associated with a unit change in each independent variable, given the other independent variables are left unchanged. Under these constraints, it is reasonable to conclude that the squared coefficients perfectly partition the coefficient of determination, as described in the equation below: R2y,x = ∑pj=1 p2y,xj  = ∑pj=1 β2j While this method of using variable coefficients as importance measures is intuitive and appropriate in the case of no intercorrelations between dependent variables, in most real-world applications, dependent variables (advertising channels in this case) have some level of correlation, making this method inappropriate. Dominance Analysis Dominance Analysis compares coefficients of determination of all nested submodels composed of subsets of independent variables with that of the full model. Too much jargon? Let’s take a look at an example. Let’s say we have a total of ‘p’ independent variables in our linear model. We will build 2p-1 models, since these are the total number of subset models that can be created. We will then compute the incremental R2 contribution of each independent variable to the subset model of all other independent variables. Let’s take a scenario where we have 4 independent variables X1 , X2 , X3 and X4. We will build 24-1 models ie. 15 models. These will be 4 models with only one independent variable, 6 models with 2 independent variables each, 4 models with 3 independent variables each, and finally 1 model with all the independent variables. Thus, the incremental R2 contribution for variable X1 for example, is the increase in R2 value when X1 is added to each subset of the remaining independent variables (i.e., the null subset { . } , { X2 } , { X3 } , { X4 } , { X2 , X3 } , { X2 , X4 } , { X3 , X4 } and { X2 , X3 , X4 } ). Similarly, the incremental  R2 contribution for variable X2 is the increase in  value when  is added to each subset of the remaining independent variables (i.e., the null subset { . } , { X1 } , { X3 } , { X4 } , { X1 , X3 } , { X1 , X4 } , { X3 , X4 }  and { X1 , X3 , X4 } ). The beauty behind dominance analysis lies in the fact that the sum of the overall average incremental R2 of all independent variables is equal to the R2 of the model with all independent variables (the complete model). This allows easy partitioning of the total coefficient of determination amongst independent variables. An inherent problem with dominance analysis is the lack of computational efficiency. The need to train  2p – 1 models means that the number of models that would have to be trained increases exponentially as the number of independent variables increases. Relative Weights Analysis Another paper, which can be found here, builds on the concept of relative weights analysis as an alternative to dominance analysis. However, relative weights analysis is a fundamentally flawed method of determining attribution and has been debunked, most famously in this paper. The reason I even bring this up, is to forewarn a reader that the theoretical underpinnings of relative weights analysis is dubious, and to recommend dominance analysis as the superior R2 decomposition method.

Increased Data Privacy for Advertisers and Publishers

Where do we go from here?  Privacy complaints made in November 2020 from Europe over the use of IDFA tracking code on iPhones, have pushed the industry to take privacy more seriously. In response, Apple said it would enable privacy control for its iOS users, by allowing them to opt-in to ad tracking. In 2021, consumers are more aware than ever about sharing their data. As regulators continue to step up privacy requirements, many businesses are exploring ways to use data to their advantage without violating industry regulations. In a webinar sponsored by Tavant, Strategies to Enable Advertising, Targeting and Measurement in a Privacy-Regulated World,experts from DISH Media, Integer Group (an Omnicom Group company), Sequent Partners, and Tavant got together to discuss the impact of these regulations on the media, publishing, and advertising industry. Here’s what the Panelists had to say: THE ADVENT OF PRIVACY REGULATIONS Privacy standards (GDPR, CCPA) are separating those that own data and those that do not. Third-party cookies and ad IDs are going away, but multinational companies in Europe (who worked with GDPR) are ready for it.  Advertisers will need to offer opt-ins. Publishers need to educate users to outline the opt-in message with information on what data is being collected and how it’s beneficial.   The consensus is that we will get smarter about ways to protect data, be compliant, and protect user privacy.  Yet, the current state of data quality is messy. Data used for targeting or attribution may have come with different levels of quality, which can introduce bias in the data processing and impact the efficacy in targeting or attribution. Will the increased privacy force the advertisers to put more emphasis on media mix and innovation? WHAT WILL BE THE IMPACT OF STRINGENT PRIVACY REGULATIONS? Targeted advertising may be dampened a bit due to ID loss. Consumers may begin to experience slightly more irrelevant ad content than earlier. Companies that license their data from third parties may be in a tough position as they don’t have a direct relationship with their customers.   Advertisers across the globe are still struggling to measure the reach and frequency of their campaigns, particularly across platforms. Will these privacy changes put more pressure on measurement tactics? Who will gain from these changes in data privacy? WINNERS AND LOSERS Many believe that consumers, media companies, and advertisers will all be impacted negatively. Companies that have first-party consumer data will come out the least impacted. Additionally, companies like Verizon and ATT, which have a huge reservoir of valuable first-party data, will be able to leverage it in different ways.   The experts noted that everything now done on our smart TVs results in rich digital data for advertisers and publishers. As we begin to see a lot more contextual advertising, there is likely to be more investment by publishers in NLP and video image processing. What other innovations can be expected thanks to increased privacy regulations? FUTURE EXPECTATIONS New players may come into the market with the workaround innovation to capture ID but maintain privacy. Consumers may be offered incentives to opt-in. Loss of IDs will not impact work in deep learning models for attribution and mixed media modeling.  Ad companies may hire specialists whose job will be to develop ID graphs which their brands can use.   Ultimately, the feeling is positive as change that creates contention often triggers market forces to innovate. As data privacy begins to fall into place, the new issue is data security and effective measurement.

Changing Pace of Business With Digital Assurance

Delivering Customer Delight with Digital Assurance Digital assurance – A Key Enabler for Digital Transformation Success Digital assurance is a significant step forward in quality engineering practice. Its adoption is growing by leaps and bounds and is set to achieve a CAGR of 12.9% by 2026. The end goal of digital assurance is to deliver customer delight and peak performance. DevOps, AI, and automation make digital assurance even more effective. Let us take the example of Guild Mortgage, a leading mortgage lender and advisor in the United States specializing in residential home loans. The company had over 3000 employees. Loan officers and real estate agents delivering in a distributed ecosystem. LOS and POS systems were scattered, making collaboration difficult. Guild Mortgage had to maintain a consistent relationship with community banks and credit unions. Unfulfilled customer needs and underserved segments were an increasing worry that needed immediate attention. The company adopted digital assurance to ensure end customer delight. It was a path to foster better collaboration between loan officers, real estate agents, as well as customers. With digital assurance, Guild Mortgage achieved significant business benefits. They built a mobile application to streamline the collaboration between various moving parts of the lending ecosystem. As part of the digital assurance framework, they tested the app on various devices and operating systems, ensuring platform-agnostic peak performance.     Seamless Collaboration and Uninterrupted Customer Experience with Digital Assurance Today, Guild Mortgage real estate agents are collaborating better with loan agents. The organization can customize loan collaterals as per customer needs, thanks to the ability to partner with customers at every step of the journey via the mobile app. Loan agents, too, have a seamless experience with the integration of proprietary and commercial systems. There is zero disruption or downtime at various touchpoints. Customers can access the platform on mobile devices from anywhere without disruptions. This is made possible by leveraging mobile cloud technologies and extensive usability testing. With digital assurance, this seamless collaboration and operation can become a reality for any organization. Bringing Speed in Value Delivery with DevOps and Testing A holistic approach and the right framework accelerate value delivery with digital assurance. Today, the metrics for successful digital assurance are DevOps, automation, and testing. Data security, product performance, and customer experience are all tied together. Various parameters are tested for functionality, performance, and security. Testing of applications, APIs, analytics, big data, and DR takes centerstage in digital assurance. The result? Accelerated value delivery and peak performance. Digital Quality Assured with Tavant’s Four Pillar Strategy Tavant’s digital assurance team is on a quest to innovate for quality. Tavant follows a four-pillar strategy to deliver the highest level of digital assurance for its customers. The strategy is designed with customer focus at its core to deliver end-to-end benefits of digital transformation. Customer experience testing, omnichannel testing, performance, and security testing contribute to customer satisfaction. The second pillar is testing various aspects of business processes. It includes the functionality of platforms, big data, analytics, and cloud components. The third and fourth pillars combine innovation and delivery. Test automation and DevOps build agility and innovation in businesses. Application delivery management, release management, and enterprise application management accelerate value delivery. This holistic approach supports customers’ digital journey by optimizing digital practices. What Next in Digital Assurance? Digital transformation is no longer a choice for businesses that want to deliver a great customer experience. It is a requisite, further enabled by quality assurance practices. Digital assurance is paving the way for enterprises to deliver delightful customer experiences in a device-agnostic, omnichannel world. With digital assurance, businesses are engaging their customers and speeding up product deliveries across devices and platforms. However, digital assurance is not a one-time activity. Digital assurance is a continuous practice aligned to larger business goals.  The future of digital assurance is smart and automated testing, and the practice is evolving at a rapid pace as new technologies enter the ecosystem. One thing is certain. As the business landscape moves towards omnichannel, device- and platform-agnostic 4IR models, only a relentless focus on digital assurance can help organizations realize the true benefits of digital transformations.  Organizations can now maintain and sustain the pace of change in today’s digital-enabled business world. Watch the webinar to gain insights. Want to learn more about Digital Assurance? Reach out to us at [email protected] or visit us here to understand how we can help your business navigate next. FAQs – Tavant Solutions How does Tavant provide digital assurance for rapidly changing business environments?Tavant offers comprehensive digital assurance through automated testing, continuous monitoring, performance analytics, and quality engineering services. Their platforms provide real-time system health monitoring, predictive maintenance capabilities, and agile testing frameworks that ensure digital systems perform reliably despite rapid business changes. What digital assurance capabilities does Tavant offer for financial services?Tavant provides automated testing suites, security assessment tools, performance monitoring systems, compliance validation platforms, and continuous integration frameworks. Their digital assurance services ensure financial systems maintain reliability, security, and compliance while enabling rapid innovation and deployment cycles. What is digital assurance in business?Digital assurance in business encompasses testing, monitoring, and validation processes that ensure digital systems, applications, and platforms perform reliably, securely, and efficiently. It includes quality engineering, performance testing, security validation, and continuous monitoring of digital assets. Why is digital assurance critical for modern businesses?Digital assurance is critical because businesses rely heavily on digital systems for operations, customer service, and competitive advantage. It prevents costly system failures, ensures customer satisfaction, maintains compliance, protects against security threats, and enables confident digital transformation initiatives. How does digital assurance support business agility?Digital assurance supports business agility by enabling rapid, confident deployment of new features, ensuring system reliability during scaling, providing quick feedback on system performance, and maintaining quality standards while accelerating development cycles and innovation initiatives.

Closing the Lending Gap with AI and ML- Adjusting to the Neo-Normal

Prior to the Covid-19 pandemic, the financial industry was already evolving at a rapid pace, mainly driven by evolving customer expectations, advancement in technology, and heightened competition from incumbents and new entrants. However, in just a few months, the crisis brought about years of change in the way companies in various sectors perform business. According to a recent McKinsey Global Survey of executives, most companies have accelerated their supply-chain digitation by 3-4 years. The cumbersome and time-consuming Loan origination process Many lenders still use manual and paper-based procedures, which is often a time-consuming process, making it extremely difficult for lending companies to meet their customers’ demands for ever-shorter response times. According to November 2020 Ellie Mae Origination Insight Report Data, the time to close loans has increased to 55 days, up from 54 days in October, which is the biggest concern for companies to satisfy consumers’ evolving demand of expecting much quicker turnaround times in the digital era. Fragmented lending Supply-chain: An age-old need for Digitization Furthermore, financial institutions have many potentials to increase their efficiency and streamline complex processes by digitizing the lending supply chain. Embedding Artificial Intelligence in the ecosystem can subsequently help companies to enhance their overall supply chain performance. It can also help lenders with possible implications across various scenarios regarding time, cost, and ROI. Tackling fragmentation with Digitization  Digitization resolves issues arising from fragmentation of delivery as well as sluggishness caused due to legacy loan origination. Moreover, COVID-19 has forced “a change of mindset” from the historically slow pace in digitizing supply-chain activities. Lenders are forced to develop truly end-to-end digital capabilities, from onboarding and application through approval and execution to improve servicing, capacity, and ability to automate underwriting and risk management. AI can be used in various ways in the credit process to make it more agile and efficient. Right from legitimizing a new customer who applies for credit to choosing a suitable credit product or optimizing the credit check, the credit sector’s scope of intelligent data analytics is wide. Not only that, by leveraging AI and ML applications, lending companies can tap into customer experience at the right time with the right offer and can deliver a delightful customer experience. The Road to Business Value – Digital Lending It takes advanced next-gen technology to successfully process mountains of applications to ensure same-day approvals come to fruition. Automation and AI can reduce the time and cost of closing a mortgage and can effectively speed up the time-consuming tasks of gathering, reviewing, and verifying mortgage documents. As a result, AI-backed automation can cut out the mundanity of manual tasks but augment processing with Machine learning can further reduce human interaction. This subsequently reduces time to process and cuts down the probability of errors. AI has moved beyond experimentation to become a competitive differentiator in financial services — delivering a hyper-personalized customer experience, improving decision-making, and boosting operational efficiency. As a result, Financial services companies have no choice but to implement AI and automate the credit value chains. Act now – Change is here! AI has begun to create a tangible impact on the mortgage industry. However, looking beyond the mortgage industry offers a glimpse into the actual magnitude of the AI-enabled disruption still to come. AI technology holds the potential to fundamentally redefine the industry on all levels – challenging traditional cost structures, enabling novel relationships with end customers, and much more. For those, who are yet to embark on their journey towards an artificially intelligent future, the time to act is now. What Next? Tavant recently sponsored Chief Data and Analytics Officers, Financial Services 2021 virtually. Tavant’s leaders Dr. Atul Varshneya, VP – AI, and Vaibhav Sharma, Head – Banktech, discussed ‘Adjusting To The Neo-Normal: Evolving the Art of Credit Decisioning with AI and ML.’ Watch the video here to gain more insights. Reach out to us at [email protected] or visit us here.  FAQs – Tavant Solutions How does Tavant use AI and ML to address lending gaps in the new normal?Tavant leverages AI and ML to expand credit access through alternative data analysis, remote verification processes, and adaptive risk models that account for changing economic conditions, helping lenders serve previously underserved markets safely. What lending gap solutions does Tavant offer for the post-pandemic landscape?Tavant provides digital-first lending platforms, contactless verification systems, flexible underwriting models, and real-time economic adjustment algorithms that help lenders adapt to new market realities while maintaining responsible lending practices. What is the lending gap and why does it exist?The lending gap refers to qualified borrowers who can’t access credit due to traditional underwriting limitations, lack of credit history, or geographic constraints. It exists due to rigid criteria, limited data sources, and risk-averse lending practices. How has COVID-19 changed lending practices?COVID-19 accelerated digital lending adoption, increased focus on remote verification, emphasized the need for flexible underwriting, and highlighted the importance of real-time data in assessing borrower creditworthiness. What is alternative credit scoring?Alternative credit scoring uses non-traditional data sources like utility payments, rent history, bank transaction patterns, and employment records to assess creditworthiness for borrowers with limited traditional credit history.

Leveraging MuleSoft to Mitigate Traditional Connectivity Challenges

Digital transformation does not end with buying the latest software. In the digital-everything ecosystem, success significantly depends on how quickly and effectively businesses can integrate their data and devices and applications for faster and more seamless delivery. This seamless delivery and low time to market require seamless connectivity. But traditional connectivity approaches can get in the way. Traditional connectivity approaches like point-to-point and ESB integration are not the best options for business scalability and delivering at speed due to their many limitations. These approaches lack agility and are also incapable of integration in a cloud set-up. MuleSoft, with its API-led connectivity approach, can help mitigate the challenges and enhance scalability. Top challenges with traditional connectivity approaches Point to point (P2P) approach The P2P approach is suitable only when the infrastructure has a few components, and the organization is not expecting significant growth in the near future. This approach does not help as it forms a “tightly coupled” connection between components, limiting instant expansion opportunities. P2P integration permits lower agility, high operational risk, difficulty in maintenance, and longer time to market. End to End Approach using ESB This approach is much more advanced than the P2P approach as it uses a single pluggable system. It also focuses on centralizing and reusing components. However, it has certain limitations in terms of implementation time and maintenance cost. Time to market is longer and not suitable for today’s fast-paced business environments. How MuleSoft can help mitigate traditional connectivity challenges MuleSoft has facilitated organizations to deliver projects three to five times faster and enhanced team productivity by 300%. It also provides scope for innovation and equips organizations to cope with change. MuleSoft’s Anypoint platform enables organizations to unleash the full potential of their data and applications with its API-led connectivity approach, both on-premises and in cloud environment. The API-led connectivity approach produces reusable assets and provides speed and agility to business operations. As per MuleSoft’s Connectivity Benchmark Report 2018, by leveraging its APIs, enterprises have been able to increase employee engagement and collaboration by 43%, meet business demands faster by 35%, increase IT self-service by 35% and decrease operational costs by 34%. MuleSoft makes it easy to define, write, test, and deploy APIs across multiple environments. The API management tools manage the API Lifecycle and speed up integration. It allows organizations to import specifications into development tools and to auto-generate the baseline code from just those specifications. This further reduces time to market. What Next? Integration solutions by Tavant and MuleSoft allow enterprises to efficiently design, build, and manage their APIs, applications, and products. Tavant has a robust MuleSoft COE with core practice areas in Manufacturing, Media, AgTech, and Financial Services. Tavant’s domain experience coupled with MuleSoft’s Anypoint Platform allows organizations to realize business transformation through API-led connectivity. Tavant provides flexible solutions that can simplify your overall architecture by removing point-to-point integrations and application silos to achieve business agility. Are you looking forward to improve business agility with seamless integration and connectivity? Get in touch with Tavant to unleash the benefits of MuleSoft’s integration platform. Reach out to us at [email protected] or visit our MuleSoft page.

Building a Successful Customer Experience in Today’s Banking

Delivering similar experiences to Neobanks and Fintechs is not as challenging for traditional banks as some in the industry believe. The race is intensifying between Banks, Neos, and Fintechs vying for the customer’s attention and wallet! I’ve always been passionate about leveraging technology in building better products and witnessing how more and more financial brands are using technology today to carve their own digital journeys and enrich their customers’ experiences. Conducting Financial Transactions with Unprecedented Ease and Speed My enterprise sales career in Fintech and Digital Banking is mainly fueled by a desire to impact both consumers’ and businesses’ approach to banking and how so many of us in this industry are making it safer, clearer, faster, and easier to conduct financial transactions today. Selling across the financial verticals, I’ve had both the pleasure and honor to interact with some of the most fascinating and bold decision-makers who are shaping tomorrow’s financial services industry as we know it. Meeting the Expectation of the Digital Customer: Now And in the Future Recently, while reading through what some of our favorite Neobanks are up to these days, I came across a phrase that I often hear but sometimes don’t really attribute the focus and truth that stands behind that simple statement… “The priority is to build a successful customer service brand that offers banking and financial services, then the other way around.” I couldn’t agree more. And the current pandemic, still surging amongst so many of us only strengthens the notion where we must accelerate within our digital transformation process to achieve those journeys that our customers so much rely on. As many of us in the financial services sector are focused on mapping our digital journeys, some believe that those who are “digital to the core” have been able to successfully carve their own winning strategies and steadily acquire an increasing market share. And then there are the non-digital at the core. The overwhelming majority, from community-focused to household brands, most of these banks and credit unions share one commonality between them. Being around for much longer and focusing on both their physical and digital consumer experiences, they all gained, over time, the ultimate trust and confidence of the customer. But if we break it down, being “digital to the core” simply means having access to all the building blocks that help create offerings that are fundamentally digital, faster to market, and provide an engaging, yet the simplified journey to our customers. Breaking it down even further, it is not extremely challenging for a traditional bank to create digital products and journeys. An example where banks of all sizes and budgets can launch new products and experiences would be to implement the following strategy. 1.  Launch a DAO (digital account opening) process through automation, where new accounts can be launched within minutes without manual review. 2. But don’t stop there. The next natural step is the implementation of DLO (digital lending origination) process, allowing banks to digitize current lending programs and launch profitable products such as instant, unsecured small personal, and SME loans. 3. Third-party integration – the essential plugin that most Neobanks rely on to extend third-party Fintechs into their own platform as part of their digital value proposition. It is a lot less cumbersome than many believe for traditional banks to emulate a similar experience. 4. Integration with the core of choice and existing third-party service providers who providing essential banking services to-date. Choosing an off-the-shelf tech stack will not deliver the transformation banks expect, may delay launch deadlines, and increase the initially forecasted budget. What Next? To make this work, banks should consider choosing those technology partners who can add professional services to their product stack offerings. A consultative approach combining product stacks with the outsourced talent to digital transformation is a must for any bank when considering a cost-conscious approach and time to market. At Tavant, we focus on helping traditional banks carve their own digital journeys and enrich the experiences offered to their consumer-facing channels by implementing the technology mentioned above stacks while adding a consultative approach strategy. Our partners at Tavant happen to be some of the most renowned brands in financial services and small banks who are quietly enabling their digital transformation. We take pride in being a mid-size and super-focused technology provider by approaching transformation projects; we have in-depth knowledge, passion, and proven industry results. To learn more, reach out to us at [email protected] or visit us at www.tavant.com. FAQs – Tavant Solutions How does Tavant help banks build successful customer experiences?Tavant provides customer experience platforms with omnichannel integration, personalization engines, real-time analytics, and customer journey optimization tools. Their solutions enable banks to deliver consistent, personalized experiences across all touchpoints while gathering insights to continuously improve customer satisfaction and engagement. What customer experience technologies does Tavant offer for modern banking?Tavant offers AI-powered chatbots, predictive customer service, personalized product recommendations, mobile-first interfaces, real-time notification systems, and comprehensive analytics dashboards. These technologies help banks understand customer behavior, anticipate needs, and deliver proactive, personalized service. What makes a successful customer experience in banking?Successful banking customer experience includes seamless omnichannel interactions, personalized service, fast problem resolution, transparent communication, intuitive digital interfaces, proactive support, and consistent service quality across all touchpoints and channels. How has customer experience changed in modern banking?Modern banking customer experience has shifted toward digital-first interactions, real-time services, personalized offerings, mobile accessibility, self-service options, and proactive communication. Customers expect instant access, transparent processes, and personalized financial guidance. What technologies improve banking customer experience?Technologies improving banking customer experience include AI-powered chatbots, mobile banking apps, predictive analytics, personalization engines, biometric authentication, real-time notifications, video banking, and integrated omnichannel platforms that provide consistent service across all channels.

Decoding the CX Paradox in the Mortgage Industry

The Journey So Far The clock is ticking as more and more customers have started expecting the mortgage companies to offer advanced digital capabilities. Customer expectations for speed, agility, transparency, convenience, and personalization are getting elevated by their delightful digital experiences outside lending. Customer expectations are at an all-time high in every other area of their lives, whether listening to Pandora or hailing an Uber or watching Netflix on their smart TVs. Digital mortgage players have been on the rise, and direct-to-consumer (DTC) originations account for a growing share (more than 25 percent) of the market. While the mortgage loan process continues to be a time-consuming and expensive endeavor for mortgage companies, which are not just witnessing the challenges of meeting escalating regulatory requirements but also increased demand from their customers for a quick, painless process. Most mortgage companies have also been striving to compete profitably in a rapidly transforming competitive landscape. FinTech has also greatly improved the mortgage process; however, traditional lenders are still dominating the industry. A look at the challenges that lenders need to overcome Firstly, buying a home mortgage is the largest financial transaction of most people’s lives. For many, it is also the most cumbersome financial transaction to endure. What adds to it is ‘not so digital’ borrower engagement approach of today’s lenders. Most lenders continue to provide fragmented collaboration solutions that are heavily call-center-based. They still prefer to follow the traditional methods where document collection and borrower information is obtained via channels such as call contact, e-mail, portal connections, etc. Suffice to say; there is massive room for improvement.  The mortgage companies need to embrace next-gen technologies to revamp their loan application process and, subsequently, the CX. Secondly, mortgage lending is a collaborative venture. Lenders partner with an array of service providers, including credit, flood protection, fraud prevention, compliance, appraisal, title, and insurance providers, along with income, employment, and asset verification providers. Each delivers a vital element to create a loan that can be closed and sold into the secondary market. This has been paper-based work with information passed between partners in documents and forms before, which created significant time delays and errors that led to inaccurate and inconsistent data resulting in poor quality data and higher loan origination costs. Thirdly, disparate systems are often poorly connected, impacting data quality negatively while increasing the risk of security breaches. Lending companies consider system integrations a pretty daunting, expensive, and time-consuming process to implement and maintain. Lenders need a road map to success that will guide them through the process of digital transformation while remodeling the lending process. When lenders partner with third-party providers to complete the loan process, the data moving through third-party systems must flow into the lender’s LOS. To sum up, lenders must connect the blocks and retrospect their current redundant processes, system integrations issues, and borrower satisfaction. Solving the mortgage riddle Ask any prospective or recent borrower what matters most in choosing a lender, the answer will be ‘loan-to-value’ while the other important element consistently remains as ‘Customer Experience’. These are the twin pillars for most borrowers that drive their decision to go with one lender instead of others. But how do prospective borrowers figure out which lender has the best prices or provides superior CX or the speediest approval or the most reliable closing? The 3 Secret Ingredients for the Ultimate Mortgage Customer Experience Borrowers are consistently looking for connected digital customer experiences that can be accessed wherever they are, whenever they want. Lenders must leverage digital technologies to tap into new business opportunities to streamline processes and exceed borrower expectations and create a holistic ecosystem around the lending experience. 1. Digital Innovation- A bridge to the future To provide a truly delightful borrower experience and take a customer-centric approach, which drives revenue growth, lenders should evaluate what the primary focus of digital technology is. Besides empowering borrowers with next-gen customer-facing technologies, lenders must also consider measuring the sensitive points in each borrower’s loan journey that end up making or breaking their delightful experience. Reassurance, simplicity, transparency, as well as speed, are extremely critical during the entire mortgage journey. 2. Borrower Data- A Secret Sauce to the Customer Journey Delightful CX begins with understanding what customers want, which is only possible by analyzing borrower data. Leveraging data helps them understand the customer and build the proper customer journey view. Needless to say, the power of data insight is undeniable, and with a data visualization platform that can provide a 360-degree view of all data—no matter where it resides—lenders should create scalable and relevant omnichannel experiences for their customers. Understanding customer behavior, as well as their preferences, can help lenders prioritize investments in CX. It is crucial to gather feedback across all channels to explore opportunities for improvement. Lending companies should map out their customer’s journeys to identify all touchpoints across all channels and then leverage it to engage effectively with them. Data analytics can subsequently help lenders deliver superior customer experience at a fraction of the cost. 3. AI, Automation and Analytics – 3A Solution The right lending solution should emphasize on UX and delivers an unparalleled digital experience for consumers. It should provide a seamless and superior user experience across the value chain of the loan origination process for everyone involved, including the borrower, MLO, and the operations staff. Providing an optimized user experience from the initial borrower portal interaction until the loan is closed and funded not only delights customers but also assists in hiring and retaining key talent within the organization. This can be done using: Real-time expert assistance from experienced live loan originators, chatbots, and self-service tools. Data aggregation and workflow automation to quickly qualify and complete the application accurately to ensure the loan is approved and closed faster once submitted; Robust, rule-driven loan scenario loan calculators to play out different loan scenarios and for accurate decision making Looking Ahead: The use of technology in the mortgage industry will always be at a vital inflection point. Technology capabilities

Tapping Digital to Deliver Exceptional Mortgage Experience

Though the COVID-19 outbreak has affected all sectors of the economy, it has particularly exposed glaring needs within the mortgage industry. These changes will undoubtedly rewrite many of the industry’s “traditional” practices moving forward into the rest of 2021 and establish a “neo-normal” standard going into 2022. Among the battery of revelations, digital transformation and customer experience have become industry-wide focal points caused by the pandemic. According to a recent Forbes Report: Nearly 90% of lending executives stated that the pandemic is proving a powerful catalyst for digitizing their firm’s mortgage processes, and 85% described their efforts for mortgage process digitization before COVID-19 as aggressive. As a former Loan Officer, I was forced to use dozens of workarounds due to inherent flaws within the systems that I – and the majority of LO’s in the industry – were forced to use. Developments in recent years have addressed those limitations to a certain extent, but at a fundamental level – things need to change. Looking back at the early 2000s and then fast-forwarding to today, it’s amazing to witness all the functionality that helps automate the mortgage lending process. Back in 2003, the average loan closing time was somewhere between 30-45 days. Although technology has changed much of the world since 2000, it is astonishing that the most important metric has stayed roughly the same. Today, it takes just 2 hours to walk in and out of a Maserati dealership with a $150,000 car financed and ready to go. Why does it still take 30-45 days to finance a $150,000 mortgage? Let’s rephrase that question – why has the “time-to-close” mortgage stayed the same after all this time? In an era where customer expectations have never been higher for next-gen digital solutions, the mortgage loan process remains highly dependent on disparate systems. The size and complexity of mortgage applications make it nearly impossible to eliminate manual work. The need for process automation is great, not just to provide more satisfying mortgage experiences, but also to significantly increase productivity, reduce operational costs and minimize the impact of human error. It’s a win-win for both mortgage providers and borrowers. Lenders can automate the mortgage origination process and upgrade their “traditional” methods of processing with “neo-normal” digital solutions. A sophisticated process solution will allow lenders to simplify the application cycle. Furthermore, enabling real-time integrations of all associated parties by using loan origination systems (LOS) to exchange data between applications can significantly bring down the loan-processing time from weeks to days. Put simply, an efficient automation process can lead to shorter loan processing and overall better customer experiences. Sometimes, in order to make forward progress, you must be willing to let go of what’s holding you back. Indeed, automation has vast potential in the mortgage industry, just waiting to be tapped. Adapt, Survive and Thrive   What should mortgage companies focus on in this new technology-enabled environment? What are the benefits that really matter? What technological capabilities should they pursue? Disruptive technology closes the gap and helps lenders create positive customer experiences, for both borrowers and loan officers. Tavant is able to turbo-charge this process, creating unforgettably satisfying customer experiences. We welcome you to contact us at [email protected] and/or learn more about Tavant VΞLOX, the industry’s leading AI-powered digital lending platform. FAQs – Tavant Solutions How does Tavant help mortgage lenders tap digital technologies for exceptional customer experiences?Tavant provides comprehensive digital mortgage platforms with AI-powered automation, intuitive user interfaces, real-time communication tools, and seamless integration capabilities. Their technology enables lenders to deliver fast, transparent, and convenient mortgage experiences that exceed customer expectations and drive competitive advantage. What digital capabilities does Tavant offer to enhance mortgage customer experience?Tavant offers mobile-first applications, automated document processing, real-time status tracking, AI-powered chatbots, personalized loan recommendations, and digital closing capabilities. These features create seamless, efficient mortgage journeys that improve customer satisfaction and accelerate loan processing. What digital technologies are transforming mortgage experiences?Key digital technologies include AI and machine learning, mobile applications, automated underwriting, digital document processing, blockchain verification, cloud computing, API integrations, and real-time communication platforms. These technologies enable faster, more convenient, and more transparent mortgage processes. How do digital mortgage experiences benefit borrowers?Digital mortgage experiences benefit borrowers through faster processing times, 24/7 accessibility, transparent communication, reduced paperwork, real-time status updates, mobile convenience, and simplified application processes. These improvements reduce stress and uncertainty while accelerating homeownership goals. What makes a mortgage experience exceptional?Exceptional mortgage experiences feature fast processing, clear communication, transparent pricing, personalized service, convenient digital tools, proactive updates, and smooth closing processes. They combine efficiency with personal attention to create positive, memorable customer interactions.

Quality Engineering Trends in 2021

The new consumer demands nothing less than instant gratification. Need a cab? It’ll reach your location in 5 minutes. Hungry? Give the food tech app 24 minutes. Maybe less. Besides, digital services are seeing a large-scale adoption from consumers around the world, and COVID-19 has further accelerated this pace of adoption. These trends leave no room for error or software failure anymore. Volume and accuracy drive business. Naturally, organizations commit to quality at scale and are therefore rendering software testing and engineering an essential enabler of their operations. This has ensured that quality engineering becomes an even more critical part of the development process, going from a standalone vertical to a horizontal enabler. In this blog post, we discuss the trends that will further drive the rise of quality engineering in 2021. 1. The need for speed will be the key driver for quality Test automation has become a major area of focus in QA in recent years. Automation enables high speeds in the testing process, thus powering lower time to market. This calls for higher agility and DevOps across organizations. Software changes fast now. New feature enablement, enhanced user experience demand this. DevTestOps will drive the faster deployment of these changes in software, once again reducing the time to market for enhanced features and UX improvements. 2. Connected devices will call for higher instances of IoT testing Connected, smart appliances are on the rise and will continue to be so in the coming years. In fact, by 2025, it is expected that there will be more than 30 billion IoT connections, averaging almost four IoT devices per person. The proliferation of connected devices is driving the rise of IoT testing, with its cutting-edge technologies that test software in-built IoT devices. Beyond hardware challenges, IoT testing will also include compliance requirements, access management, hardware issues, among others, to test the seamless performance of connected devices. 3. New AI, ML RPA led testing skills will need to be acquired With saving time, enhanced collaboration high on organizational agendas, AI, ML, RPA will no longer be good-to-have in testing processes. They will become mainstream and will be used to build entire QA environments and help enterprises scale with sustainability and stability. New skills will have to be acquired for these new standards of testing. 4. Testing for UX across devices 90% of consumers around the world use more than one device – from smartphones to smart TV, tablets to laptops. Naturally, apps and services will need to be tested across devices for performance. Interoperability will become a must-have, and organizations will further scale their multi-device, interoperability testing rapidly in 2021. 5. More cybersecurity testing in the face of increasing cyber threats Cyber-attacks are costing organizations and consumers significant amounts of money. 68% of business leaders globally feel that their cybersecurity risks are increasing. Naturally, cybersecurity testing is gaining momentum in the quality engineering space in order to ensure minimal costs and downtime in the occurrence of a threat event. Cybersecurity testing includes penetration testing and provides an in-depth understanding of an organization’s security posture. It identifies points of weakness that could invite threats into the system. Therefore, in the face of increasing cybersecurity incidents around the world, cybersecurity testing will gain even more prominence in 2021. 6. Performance Engineering will become part of organizational cultures Performance engineering enables continuous, proactive testing of application performance. With loads increasing and UX demands on an all-time high, organizations will have no choice but to take on performance engineering head-on. Performance engineering enables QE teams to build accurate and effective performance metrics. In 2021, we foresee performance engineering becoming a matter of corporate culture. It will allow checking every section of systems and software and delivering business value through quality. Which trends do you foresee driving QE in your organization? Reach out to us at [email protected] or visit here to learn more.

Tavant Sponsors CDAO FS Live 2021

Tavant is participating at CDAO FS as a Gold Sponsor. Join us as we lead the conversation on data-driven transformation in financial services and discuss AI, Data Monetization, DataOps, Data Protection, Cloud Migration, and more at this 3-day virtual summit from March 2-4, 2021. Hear our experts at CDAO FS Tavant leaders Dr. Atul Varshneya (VP of Artificial Intelligence) and Vaibhav Sharma (BankTech Practice Head) will be speaking on “Adjusting to the Neo-normal: Evolving the Art of Credit Decisioning with AI & ML” at CDAO FS. The session is scheduled at 03:15 pm, EST  on March 3. The session will focus on – Automating information capture and flow for STP (straight-through processing) Multi-parametric assessment for more accurate risk prediction Opportunity to engage in customer’s journey and value for the institution Atul and Vaibhav bring over two decades of experience in AI and BankTech leadership, respectively. They will share deep insights about industry needs and solutions as the Financial Services industry goes through unprecedented churn and change. Why you should take the time to meet Tavant at CDAO FS The global population generates up to 2.5 quintillion bytes of data every day. This opens up a world of possibilities for visionary businesses to become truly data-centric in their decision-making. The Financial Services industry is no different. At Tavant, we believe that this data explosion opens up brand new opportunities for the industry to begin the process of AI and ML adoption. Technological advances have led to more mature AI tools, and there is an increased awareness of AI applications in the industry. Needless to say, the ability to constantly learn and adapt to changing circumstances is what separates AI from other technologies. At CDAO FS, we are taking it up a notch. We look forward to sharing our latest innovations in AI ML applications in the new normal and how Tavant can enable you to navigate your business to the digital next. We look forward to seeing you there! For more information or to register for the event, click here.

Is RPA Shaping the Future of Test Automation?

Unleashing the Power of Automation Today, organizations face a perfect storm – technology changes, the fragility of customer’s loyalty, and the intense pressure from all across to keep the cost low. Businesses are forced to explore newer technologies, constantly evolve, and cut down the time from conception to deployment to meet these challenges. Furthermore, the COVID-19 pandemic has accelerated the organizations’ need to be hyper-productive. Organizations realize that they have to transform to build the capabilities that will prepare them for the future. They are thinking of ways to drive efficiency and effectiveness to a level not seen before. Moreover, there is a strong push for automation to play a pivotal role in making that happen. It is also clearly reflected in the testing domain, where any opportunity for improvement will be welcome. Organizations want to adopt RPA in testing, realizing the need to drive efficiencies and reduce manual efforts. We are at a tipping point where RPA adoption benefits are clear; what is needed is to add further efficiencies to existing frameworks. Pandemic has ramped up innovation and scale automation. Business and technology leaders will demand clear, direct benefits from investments in a digital workforce in the post-pandemic world. Automation will be taking on an even more critical role in a post-pandemic world as business resilience and cost takeout become the main destinations on the technology roadmap. A surge in RPA’s value According to Gartner, Robotic Process Automation Software Revenue worldwide will reach nearly $2 Billion in 2021. Despite Economic Pressures from COVID-19, RPA market forecast to grow at double-digit rates by 2024. In the last decade, automation has evolved and matured with time and changing technologies. Automation in testing is not new, but its effectiveness has been a challenge – especially the associated expense and lack of skill sets. Within an enterprise, RPA can cut through the maze of toolsets, replacing them with a single tool that can talk to heterogeneous technology environments. From writing stubs to record and playback to script less and modular testing, and now to bots, we witness a natural evolution of test automation. In this next-gen testing brought about by RPA orchestration, an army of bots will drastically transform the time, energy, and effort required for validation and testing. We are gradually heading towards test automation that requires no touch; no script works across heterogeneous platforms, creates extreme automation, and allows integration with opensource and other tools. According to Forrester, “RPA brings production environment strengths to the table.” It translates into production-level governance, a wide variety of use cases, and orchestration of complex processes via layers of automation. RPA allows organizations to democratize automation very rapidly within the testing organization. Needless to say, RPA has an advantage over traditional tools in that it can be deployed where they fail to deliver results. For instance, when the testing landscape is heterogenous with complex data flows, there is a need for attended and unattended process validation and to validate digital system. These all need an RPA solution that can bring in a tremendous amount of simplicity for building out bots quickly and deploying them with the least amount of technical know-how that even business stakeholders can understand. The adoption of RPA is gradually gaining immense momentum. However, some organizations get perplexed when it comes to identifying and selecting the right processes for RPA as some operations are more suited for automation whereas some are not. Selecting the right process and internalizing a technology require a lot of thinking through prior to implementation. As a thumb rule, however, processes that are manual, repetitive, and rule-based are more suited for RPA implementation. Within the world of automation, RPA’s role is quickly growing. It has already gained popularity in software testing for efficiently eliminating repetitive manual efforts in end-to-end testing by automating workflows and dissolving data silos. Accelerating the pace of digital Transformation with RPA Digital transformation has led to a paradigm shift in how quality is perceived and assured. RPA helps organizations reshape how they operate and their level of responsiveness at most touchpoints within the value chain. The core component of any organization’s intelligent automation tech stack is RPA. It enables rapid end-to-end business process automation and accelerates digital transformation journey. Robotic Process Automation (RPA) is a key driver on the digital transformation journey—which is why, according to Deloitte, 53% of companies surveyed are ready to begin implementing RPA. Are you automation-ready? According to recent stats 30 – 50% initial RPA implementation projects fail (Report: Get Ready for Robots – Ernst & Young). With this, businesses are not sure about the desired results after investing in the technology. Enterprises should start bringing RPA into the testing fold if they plan to save resources, reduce defects, and prepare for a future that is already closer than we perceive. Fast-track your Intelligent Automation journey with Tavant. We believe that intelligent automation is not only about new-age technology. For Tavant, it’s much more. We are working with organizations to orchestrate new ways of working and embed intelligent automation into their operations to drive continuous innovation and business impact. Want to know more about the potential of RPA in testing? Reach out to us at [email protected] or visit us at https://www.tavant.com to understand how we can help your business navigate next.

Accelerating Agility and Innovation with Salesforce Financial Services Cloud

Evolving customer expectations Uncertainty is the name of the game in 2021. The surge and ebb in markets drive the financial sector to look for technology solutions that keep them anchored. The disruption unfolded by the COVID 19 pandemic brought to light some stark realities.  Customer expectations are shifting goalposts, and the trick to be ahead is being well informed. Artificial intelligence, advanced analytics, and automation make it easier to leverage insights and meet customer expectations. But to leverage those capabilities requires a solid underpinning technology foundation. How upgrading to Salesforce Financial Services Cloud can enable transformation. Salesforce Financial Services Cloud brings advanced capabilities to stitch together marketing, sales, and services and break down silos to deliver a seamless customer experience. If you haven’t done so already, migrating to Financial Services Cloud should be one of your top priorities in 2021. However, before you make a move, reflect on these three key considerations: Objective Assessment of Existing Systems Financial institutions have been running on a relatively limited set of standard technology solutions historically. As the complexities of markets increase, constant customization to meet evolving business needs has become a limiting factor to business growth. A move to the cloud to leverage tools that offer increased flexibility is a clear path to take, but how best to chart this journey? You may decide to take an incremental migration route and integrate legacy systems or completely move to FSC. If you are an existing Salesforce user, you have an edge as the updates are likely more straightforward. However, it is important to have a thorough assessment of your current CRM and IT landscape to arrive at a migration strategy that balances the operational challenge of completing the migration promptly with the objective of ensuring a platform that empowers support for strategic business needs over a longer-term.  Just getting on FSC is one thing – it is another thing to get on FSC in a manner that will support long term business needs effectively. Clear Strategy and Executable Plan Demands of instant gratification from an increasingly digital-savvy clientele drive banking, insurance, and wealth management institutions towards solutions tailor-made for their needs. The urgency makes it all the more important to have a well-defined plan that delivers on the promise. A clear strategy to make a move to FSC keeps you anchored and allows you to build a thorough execution plan. Given that businesses have realized a 41% increase in customer satisfaction, 37% uplift in productivity, and accelerated decision making by 40%, the benefits of FSC are well established. However, your move to FSC must be guided by your business goals, and the timeline you set for the migration must deliver to business expectations. Strict Evaluation of Security Requirements The financial sector has been at the receiving end of security breaches and cybercrime. In 2020, 71% of cyberattacks were directed at financial institutions. Security of systems, applications, and data is thus, non-negotiable. Salesforce Financial Services Cloud comes with advanced security features to alleviate your concerns. However, you must map your security requirements and define metrics to suit your business priorities. With FSC, you have the opportunity to revisit your security posture with advanced encryptions and better access control. Creating value with the cloud Salesforce Financial Services Cloud is a force multiplier. It equips your teams with the right insights and better collaboration. Your advisors deliver quality advice to clients, and your retail touchpoints win customer delight. However, you must decide on the right partner with proven capabilities to steer the migration to optimize your path to these objectives. What next? Tavant has won the trust of financial institutions across the globe, delivering them an unmatched cloud advantage with custom made migration strategies and comprehensive managed services. To learn more about how we can help you gain a competitive edge with Salesforce Financial Service Cloud, visit here or mail us at [email protected]. FAQs – Tavant Solutions How does Tavant integrate with Salesforce Financial Services Cloud to accelerate innovation?Tavant provides seamless integration with Salesforce Financial Services Cloud through pre-built connectors, API integrations, and shared data models. This integration enables lenders to leverage Salesforce’s CRM capabilities while utilizing Tavants specialized lending technology, creating comprehensive customer relationship management and loan processing workflows. What benefits do lenders gain from Tavant Salesforce Financial Services Cloud integration?Lenders benefit from unified customer views, streamlined lead-to-loan processes, enhanced customer service capabilities, and improved sales productivity. The integration provides 360-degree customer insights, automated workflow triggers, and consistent data across sales, lending, and customer service teams. What is Salesforce Financial Services Cloud?Salesforce Financial Services Cloud is a specialized CRM platform designed for financial services organizations. It provides tools for relationship management, client onboarding, compliance tracking, and collaboration, specifically tailored to meet the unique needs of banks, credit unions, and other financial institutions. How does Salesforce Financial Services Cloud improve agility?Salesforce Financial Services Cloud improves agility through rapid customization capabilities, automated workflows, real-time collaboration tools, mobile accessibility, and integration with third-party financial services applications. It enables quick adaptation to market changes and customer needs. What are the key features of Salesforce Financial Services Cloud?Key features include household and relationship mapping, financial account management, goal tracking and planning, collaborative action plans, compliance and audit trails, mobile banking integration, and specialized financial services analytics and reporting capabilities.

How to Decipher Customer Journey with Relevant Advertisement Attribution

Every advertiser has a unique context. Are you enabling the right choices? A large multinational company is launching a new lifestyle product and is looking to gain the attention of high-income, mid-career women across tier-one cities. Their advertising campaign has unique requirements, and they want the best slots suited to their product promotion. How would you guide them to make the best choices and drive premium returns for your platform? You need deep insights on advertising performance across segments that demonstrate the optimal ROI to win the trust of the advertisers. You can promote specific segments, drive higher revenue, and make better pricing decisions based on quantifiable metrics unearthed with actionable analytics. Accuracy in advertising attribution is your key for precise audience targeting, campaign optimization and improved performance that boosts the bottom line while bolstering top-line growth. Campaigns leave important cues.  Are you listening? The new product line you launched last week has caught the imagination of young shoppers. Your e-commerce site has an upsurge in traffic.  You used different media to advertise and promote the product line, and it has worked. The ad spots on prime-time TV and jingles on FM radio are on for a week now. You placed inserts in newspapers with QR code for discount coupons. The redemption of those coupons is doing well too. Your social media campaign is running in parallel, and you are ready for another round of emails to roll out referral offers. You are convinced that your ad spend has delivered the desired results. It is important to trace your customer journey through all the different touchpoints up to the conversion or buying stage. Your ad spend needs to be rationalized and focused on the media mix that delivers optimum results. In short, you need to analyse ad attribution, to zero in on your campaign effectiveness. Ad attribution unlocks the significance of every touchpoint to conversion. Personalized campaigns demand an intimate understanding of customer behavior, as well as customers’ channel and platform preferences. Your campaign ROI depends on your knowledge of the customers. Advertising attribution processes allow you to trace your customers’ actions across multiple touchpoints to reveal the levels of interaction that brought them to the point of sale. This data is crucial for evaluating past campaign performance and intelligently planning the next ones for better outcomes. Advertising attribution is a quantitative measure of each touchpoint in nudging the customer journey towards conversion. Single-touch ad attribution – for example, measuring the first click or last click for a given promotion on any platform – can deliver straightforward analysis if the action is definitive, such as a discount offer for the first 50 customers within a day, advertised on Facebook. The marketer can assign success to the specific promotion. Multi-touch journeys are deciphered through various ad attribution models. A multi-touch customer journey is more difficult to attribute. For instance, a customer may have seen a newspaper insert ad, noticed a similar advertisement on a social platform, received a promotional offer through a friend, checked out the company website, and then received remarketing enforcement before making a purchase. Now, every touchpoint is a nudge forward and must be accordingly scored. This multi-touch attribution is a flexible scoring model for marketers to assign due credit to each interaction for a comprehensive performance insight. Linear models assign equal value to each touchpoint. Shapely values, such as U and W-shaped scoring models, give more credit to the first and last touchpoints and first, middle, and previous touchpoints, respectively. Some marketers prefer a time decay model that treats the touchpoints closer to purchase as more important than touchpoints at the beginning of the journey. Advanced algorithmic and statistical models leverage AI and ML for ad attribution. The complexity of advertising data requires advanced custom models to assign performance metrics to every touchpoint adequately. Data-driven and statistically evolved, these attribution models leverage AI-based algorithms to score the customer purchase decision milestones appropriately. Machine learning algorithms guide the marketers in deciphering conversion probability to plan promotions accurately. The right choice of ad attribution model drives campaign performance. The pressure on marketing budgets has created a greater need for campaign precision. Marketers must choose the right attribution model to improve campaign performance and sales lift. However, there are no absolutes in this game. No statistical model, no matter how evolved and data-rich can guarantee 100% accuracy. Marketers must consider models that align with their customer journey and campaign intricacies. An advanced AI and ML-powered analytics platform can algorithmically design attribution models to deliver timely and accurate metrics. Making the right choice for ad attribution is intrinsic to campaign success. Marketers can leverage these attributes to design just-in-time campaigns with higher confidence. Which ad attribution model would you bet on for your campaign analysis? Please share your thoughts with us at [email protected]; or to learn more about Tavant’s media solutions, click here.

Managing Product Recalls – Harness the power of Data

Product Recall – a word that could send chills down the spine for some manufacturers, spark media attention, hamper hard-earned reputation, and, no doubt, pose an unwanted financial burden. Despite stringent rules and regulations around product safety, recalls regularly make headlines.   While no executive wants to face a recall, how it is handled determines the actual impact on the business. If an organization appropriately reacts and adjusts its operations, it can minimize damages and enhance its brand’s reputation for transparency, honesty, and genuine customer focus. Even after the need for a recall has been identified, the costs can quickly increase according to several associated factors list below: Identification The seriousness of the recall needs to be established; for example, is it contained to a consignment or batch, or a larger issue? If the product batch is at a warehouse waiting to be shipped, the recall cost will be far less than if the product is already in the consumer’s hand. Speed is of the essence at this point because if the product continues along the supply chain, the issue and cost can accelerate. Perception The emotional component of a recall can also wreak havoc on costs. For example, China’s parents are still haunted by an incident in 2008, the melamine baby milk scandal, and killed six infants. Many parents lost their trust in domestic brands, paving the way for foreign companies. Regulatory reporting Certain jurisdictions mandate reporting a product-related issue if any defects become known to a manufacturer or distributor, which may necessitate a recall. Companies need to decide which media channels should be used to release the recall notice. Social media platforms have reduced the potential costs, but advertising space in newspapers, television, and radio may still be required, and costs can increase considerably. The message about the recall will need to be carefully crafted to ensure it is clear and concise while also informing consumers that the company manages the issue effectively. Logistics The product may need to be physically removed from outlets, supermarkets, and/or showrooms. Retailers/dealers may also need to be reimbursed for their costs and loss of trade and remove the affected product from their shops/showrooms. The ripple effect If a faulty product is used as a component in other products, then the recall cost will also be considerably higher. While this is most common in the automotive sector, where a faulty part can easily damage other products in proximity, which must also be paid for, other sectors see similar situations. Business interruption Depending on the severity of the recall, lawyers, consultants, and extra staff may be required to help manage the recall. Also, the potentially lost person-hours associated with a recall’s distraction could be significant. Rehabilitation While the actual product recall is likely to be incredibly costly for a client, the expense does not stop once everything is safely off the shelves. The next significant cost can be returning the company and brand to its position before the recall, which may include extra promotional expense and sales promotion offers. Prevention is Key – what more can we do? AI and Machine Learning to the rescue! Big Data and emerging technologies underpin a positive response to an adverse event. Data analytics and emerging technologies in the supply chain, combined with customer intelligence and product knowledge, can help companies of all sizes more nimbly mitigate the detrimental effect of recall situations while simultaneously addressing customer concerns and preventing future recalls. We are in the era of industry 4.0, where smart and connected devices, powered by machine learning and AI, can predict faults and anomalies in the manufacturing process. Another way AI and machine learning can help the manufacturers is by analyzing the flood of manufacturing data received by machines. By analyzing this data thoroughly and looking for anomalies via machine learning, you can predict catastrophic failures earlier, avoiding total breakdown and saving businesses large amounts of revenue and brand equity. This, in turn, minimizes businesses’ need to issue recalls routinely or for consumers to suffer the potentially dangerous fallout from faulty equipment. Imagine being able to predict something before it does, pre-empt failures, and proactively take corrective actions. This is where artificial intelligence and machine learning come into play. The ability to create a full digital copy of an engine is achieved by creating ‘Digital Twins’, granular virtual copies of parts in the manufacturing process, which are enabled by deep learning and artificial intelligence. By creating ‘Digital Twins’, insights can be garnered to address the tiniest of issues that would otherwise be missed during a manual inspection process. When it comes to safety issues, the sooner they are discovered, the better. The advanced data analysis can help identify the early warning signs. Using multiple databases, complaints & reviews can be tracked and researched to pinpoint patterns with specific parts and performance. By investigating potential safety concerns and developing campaigns earlier, manufacturers can perform outreach to equipment owners more effectively to protect both the public and their brands. Text analytics platforms can empower big manufacturing companies to quickly assess their customers’ expectations, possible miscommunication issues, and the impact of the company’s actions on customer sentiment. This approach to crisis management enables businesses to seamlessly align internally and place their customers at the center of their product recall strategy. By combining machine learning and natural language processing, an organization can begin laying the foundation for cognitive analytics or artificial intelligence—sophisticated ways to make faster and more accurate decisions down the road. In the study, published in the Journal of the American Medical Informatics Association (JAMIA) Open, the researchers taught an existing “deep-learning” AI called Bidirectional Encoder Representation from Transformations (BERT) to predict food product recalls from Amazon reviews with about 74% accuracy. The AI also identified 20,000 reviews that suggested potentially unsafe food products that had not been investigated. Conclusion Putting strong measures to handle and counter the reasons that caused product recalls can help keep potential adversaries at bay. It fosters the goodwill needed to maintain

The Big Leap: Tavant Accelerates Growth; Surpasses Significant Milestones

2020 is now – finally – hindsight. It was undeniably a year of unprecedented disruption. Merriam-Webster recently announced that the Word of the Year for 2020 is pandemic. However, my personal vote goes to perseverance. Despite the challenges and twists and turns, Tavant is so proud to have achieved several major milestones last year –thanks to the perseverance of our team. These significant milestones demonstrate our commitment to enabling clients across multiple tech industries to rapidly accelerate their digital transformation journey and provide the best possible customer experience. Aligned with this trend, the key milestones that helped fuel the company’s unprecedented growth include: Product adoption and new product launches The debut of the digital software factory Launch of Banktech business Key strategic alliances 20 prestigious industry awards including Stevie® Award for AI and Machine Learning Reflecting on significant milestones in 2020: Tavant saw a surge in growth and increased adoption of its flagship AI-powered product suite, Tavant VΞLOX. Tavant’s core growth acceleration also came through its Digital Software Factories (“Digital Factory”) at its new technology innovation center in Dallas. Tavant further expanded its fintech and digital lending business by launching its Proptech business, bringing technology and innovation together to address common customer challenges in this burgeoning sector of Real Estate. Furthermore, at the American Banker’s Digital Banking 2020 conference, Tavant announced its expansion to new business lines with the launch of its Banktech practice in New York. Tavant expanded its industry-leading aftermarket product suite beyond warranty with salesforce connectors like Field service, B2B, and CPQ, available on the service cloud. They work seamlessly in conjunction with Tavant Warranty and support the entire gamut of Service- lifecycle management for a wider and faster digital transformation. Tavant Warranty got featured in Salesforce road to recovery apps. 2020 proved to be a year of key successful alliances. The company teamed up with Microsoft and Land O’Lakes Inc. to help farmers generate new insights from their crops, leveraging AI technologies. Tavant entered a strategic partnership with Softworks AI to deliver the touchless mortgage promise. The company also launched FinDecision, which improves loan quality while enhancing the overall borrower experience. Additionally, Tavant FinConnect and FinLeads made their debut on the Salesforce AppExchange, the world’s leading enterprise cloud marketplace. Early 2020, the company got recognized for driving fintech innovation forward during the year and bagged a few major fintech industry awards. Additionally, it was named to prestigious IDC FinTech Rankings by IDC Financial Insights. Tavant was also named the winner of a Stevie® Award in the 18th Annual American Business Awards® in the Business Technology category for Artificial Intelligence/Machine Learning solutions. Furthermore, Tavant’s next-gen quality engineering (QE) business was recognized by Everest Group in its PEAK MatrixTM assessment report. Tavant was also recognized as a leader in the IDC MarketScape for manufacturing warranty and service contract management applications and made it to the IDC TechScape: Worldwide Service Life-Cycle Management and Servitization Optimization in Manufacturing, 2020, where it was mentioned in Service Analytics and Business Intelligence and Warranty Software sections.   The company was also mentioned in the aftermarket, professional, and life sciences services section in the IDC Market Glance: Next-Generation Automotive and Transportation Strategies report. Empowering businesses to build resilience for today and what is ahead. Tavant is grateful to its associates, partners, and community for navigating through the pandemic together. This adversity presented a historic opportunity for innovation and digital transformation. Tavant is uniquely positioned to leverage its technical and domain expertise to drive value for all its partners. We built a solid foundation in 2020 and are planning to continue to execute our strategy in 2021. We aim to empower companies to accelerate their digital transformation journey to respond, recover, and thrive in the new normal reality in a most secure and cost-effective way. These significant milestones are a true testament to how our customers bolster their digital strategy to improve profitability and enhance their customer experience using Tavant’s products and solutions. We will continually support your digital journey and help you rise to new levels of business resiliency, and stay relevant in an uncertain world. To learn how we help our customers use digital to create value by reinventing the core of their business, visit www.tavant.com or reach out to us at [email protected].

Agones – a Kubernetes-centric Game Server Toolkit

A typical multiplayer gaming deployment and its maintenance can be a complex process. Unlike web applications, game servers have a complex lifecycle. They need to connect to the clients directly, and the latency needs to be at the lowest for a better user experience. Other challenges include aspects like scaling for surges, planning, and continuous delivery of games changes to players across different regions, and the list goes on. Choosing the right solution becomes important to reduce the overall complexity. Kubernetes is one of the commonly used container orchestration and clustering solutions. It allows automating application deployment, scaling, and management. However, Kubernetes does not address several game-specific needs. Most companies end up writing custom solutions to allocate game servers, manage players, and auto-scaling. Though Kubernetes can work, it involves a certain degree of custom implementation. Agones Agones is an open-source platform for deploying, hosting, scaling, and orchestrating dedicated game servers for multiplayer games, built on top of Kubernetes. Agones replaces custom/proprietary cluster management and game server scaling solutions with an open-source solution to focus on more important aspects of building a multiplayer game. Agones can run on any cloud or on-premise and scale as needed. This helps use any existing on-premise infrastructure, while the cloud can be used for spikes during peak hours. Architecture  Agones integrates with Kubernetes and exposes certain APIs, making it easy to handle the game-specific needs of clustering. Agones focuses on online multiplayer fast-paced games requiring dedicated, low-latency game servers with the state usually held in memory for match’s duration. These game servers have a short lifetime; a dedicated game server runs for a few minutes or hours. These fast-paced games are sensitive to latency, hence requiring dedicated game servers also need a direct connection to a running game server process hosting IP and port, rather than going through load balancers. Agones allows Kubernetes’ tooling and APIs to create, run, manage, and scale dedicated game server processes within Kubernetes clusters. Agones also supports out of the box metrics and log aggregation to track and visualize what is happening across all the game servers. Agones Integration Agones provides command-line integration through Kubernetes kubectl. But we likely want to interact with Agones programmatically. This allows Matchmaker to interact directly with Agones to provision a dedicated game server. Agones gives two ways to integrate, one Kubernetes APIs and another directly with Agones API using their SDKs. Agones SDKs are available in various programming languages like Unreal engine, Unity, C++, Node.js, GO, etc. The above diagram shows the game server allocation workflow. Matchmaker requests a game server through Kubernetes APIs where Agones intercepts the call to serve the request. Agones changes the game server’s status to Allocated and returns the game server IP and port details to the Matchmaker, which in turn is sent to the game client. Allocated game servers are not touched by Kubernetes for scaling down or termination if the status remains ‘Allocated.’ Once the game client finishes the session, the shutdown API called the game server is marked back to the ‘Ready’ state. Google Game Servers It is easy to run a cluster in a region or a zone. However, if we wish to run it in multiple areas around the world, things get more complicated. Google Cloud Game Servers is a management layer that sits on top of open-source Agones. It provides a great set of functionalities and features that make it easy to orchestrate and scale multiple clusters of Agones around the world while still not locking into a specific vendor package. It gives a lot of flexibility and power to run the game servers precisely the way we want to. The following diagram shows an example of the deployment of more than one Agones cluster across different cloud providers and on-premise data centers. Here Realms are a grouping of game server clusters defined by users based on latency, region, etc. Game server clusters are Agones clusters that have registered themselves with Google Cloud Game Servers so that Google Game Servers are aware of them. The rest of the Agones cluster functionalities remain the same. We can run the game servers on the Agones cluster the way we want while GCP deals with autoscaling and managing the fleets. Downsides  Agones is a relatively newer framework that recently came out of the beta release. Agones still does not support Windows game servers, which means all the multiplayer games running on Windows will have to wait. Some of the basic game specific functionalities like player handling were added only recently as part of the alpha release. Still away from multi-cluster policies or multi clusters across the globe, and the only workaround is using GCP Game Server.   In essence Overall, Agones can be a great open-source tool for deploying, orchestrating, and scaling dedicated game servers. Currently, there are no other ‘openly available’ tools in the industry, and the open-source community seems to be excited about this. Though there are other SaaS services like GameLift by AWS and Playfab by Azure, with the SaaS services, we will not be able to use on-premise infrastructure. The features provided may not in-line with what we are looking for, while open-source tools are primarily shaped by the community. Agones is here at the right time, and it is just a matter of time before its popularity explodes.

Is the Pandemic Accelerating Digital Disruption?

The pandemic has caused not only a global economic decline but a complete disruption in the way we rely on to communicate with our clients and prospects alike. This is the time when customer loyalty and conviction can be easily won or lost. And for those of us who have not perfected their omnichannel reach or launched their desired digital strategy – you are running against the clock!! We can all admit that none of us was ready, and many were knocked off balance, shifting from standard daily routines to a completely virtual workplace environment. Between longer working hours from home and reinventing the way we meet and communicate, there is no doubt COVID introduced a state of disruption upon all of us. As I see it, COVID didn’t disrupt the way we do business with our customers and colleagues. COVID simply accelerated the “State of Disruption” that many of us knew we’ll have to face sooner rather than later. The only question we should all be asking ourselves is how ready we are in our current “state of disruption”. Even those of us who practice ‘digital disruption’ are not always ready. I still catch myself sometimes telling clients I’m ready to book a flight and meet at their earliest convenience. Of course, reality sets in when I am politely reminded a video conference would suffice just fine. It’s never easy to face uncertainty, but during these challenging times, experience shows us that there’s also no better time to build and prepare for the next phase in our future. Outmaneuver Uncertainty with Tavant Organizations from across the world are working hard right now to serve their customers and frontlines. And many rely on partners like Tavant to ensure they receive the support, top IT talent, and cutting-edge financial technologies to help them get back to what matters most in an efficient and minimally invasive manner. Tavant’s partners happen to be some of the most renowned brands in financial services, professional sports, digital media, and even motion pictures brands. Rising to the challenge and remotely supporting partners during COVID is not easy and new challenges present themselves almost daily. However, the critical components that create Tavant’s formula for success somehow outweigh the risks and allow us to deliver WINS, even during these unprecedented times. Many of our clients refer to Tavant as truly an extension of their internal teams. And in an event such as COVID-19, we knew early on that formula to succeed and deliver results required us to reimagine how we do business by shifting technical capabilities and entire teams from brick-and-mortar locations to virtual. We realized from the get-go that in order to deliver disruptive innovations in a virtual environment, we had to prove to ourselves first that our virtual business model works and works well. Some of the new user experiences and operational excellence models we are deploying for partners entail: Digital account opening (DAO) with hyper-personalized customer recommendations AI-powered lending solution for digital loan origination (DLO) and auto-decisioning capability for consumer & commercial banking customers Digital engagement platform with API first approach and over 125 leading ecosystem partners for end-to-end compliance, KYC, and BaaS (banking as a service) capabilities. Add connectors to launch customized products and deliver personalized experiences Salesforce driven, Tavant’s Customer 360° solution for a complete customer and workforce view, as well as engagement across all digital channels At Tavant, we are always focusing on each other – supporting, informing, and inspiring our people. The most important business lesson anyone can learn from today’s pandemic is the importance of planning ahead, conditioning, and adopting new policies, operational procedures, and digital technologies to meet the rising levels of omnichannel expectations from our customers, no matter where they are and what they need. Moving forward, we remain diligent to continue and evaluate our clients’ exposures to technological vulnerability, risk and monitoring the stability of legacy systems while tracking and advising on various digital transformation paths moving forward. To all our clients, prospects, and industry colleagues leading their paths across the world, we wish good health and fortune to all the teams. Tavant is Helping Businesses Navigate the Pandemic Tavant is helping clients outmaneuver uncertainty in both the short- and long-term. We’re supporting organizations to emerge stronger and flourish in the days ahead. You can count on us to help your company stabilize revenue, keep pace with evolving demand and forge new, disruptive, and sustainable growth pathways. Through it all, we are here to help. For a deeper discussion, please mail the author Michael at [email protected] FAQs – Tavant Solutions How did Tavant help lenders adapt to pandemic-accelerated digital disruption?Tavant provided rapid digital transformation solutions during the pandemic, enabling lenders to shift to remote operations, implement touchless loan processing, and deploy digital-first customer experiences within weeks. Their cloud-based platforms allowed lenders to maintain business continuity while meeting increased demand for digital lending services. What pandemic-resilient features does Tavant offer for future disruption preparedness?Tavant offers cloud-native architecture, remote workforce enablement tools, automated processing capabilities, and flexible deployment options that ensure business continuity during disruptions. Their platforms provide scalable infrastructure, secure remote access, and automated workflows that maintain operations regardless of external circumstances. How did the pandemic accelerate digital transformation in lending?The pandemic accelerated digital transformation by forcing immediate adoption of remote operations, touchless processes, and digital customer interactions. Lenders rapidly implemented online applications, digital document processing, and virtual closings to maintain business operations while meeting social distancing requirements. What digital lending changes from the pandemic are permanent?Permanent changes include widespread adoption of digital applications, remote loan processing, virtual appraisals, electronic closings, and hybrid customer service models. Many customers now expect digital-first experiences, and lenders have maintained these capabilities as standard offerings. How can lenders prepare for future disruptions?Lenders can prepare for future disruptions by investing in cloud-based systems, implementing automation, establishing remote work capabilities, creating flexible operational processes, and maintaining robust cybersecurity measures. Building resilient, adaptable technology infrastructure is essential for business continuity.

The Intuition Behind U-Net CNN Architecture

The U-Net architecture initially developed for Biomedical Image Segmentation has found consistent success in being adapted to various additional tasks. It was also used as the backbone of the PCNet architecture in the Self-Supervised Scene De-Occlusion Paper which I discussed in my earlier blog. As a sequel to that blog, I share a few of my observations about the U-Net architecture. The main advances of the U-Net architecture are better localization on the end output and its speed. The output of the network is a labeled segmentation mask, as shown in the image (c) below, as taken from the paper. Labeled segmentation essentially stands for assigning a class label to each pixel. Below is an image from the U-Net paper showing the left half convolutional layers, the right half de-convolutional layers, and the residual connections which occur between corresponding levels of the ‘U’ architecture. The distinctive ‘U’ architecture occurs because of the greater number of layers dedicated to the de-convolutional steps than many other architectures (in many networks, the vast majority of layers are dedicated to convolutional layers). It is the additional de-convolutional layers which, along with the residual connections, lead to better localization in the segmentation mask output. The specific layers are fairly typical of a traditional CNN, so I will not address those here, but they can be found in the Network Architecture section of the paper. The U-Net paper-primarily shows that the de-convolutional task is non-trivial and requires a significant number of layers to produce useful masking of the original image. Additionally, the residual connections allow for greater access to information that is likely useful for the de-convolution task, both increasing the accuracy of the resulting de-convolution and freeing up the main bottleneck (at the base of the U) to encode more meaningful information. Furthermore, the paper shows that this architecture performed well with very little training data available (they relied heavily on typical augmentation techniques, specifically elastic deformations), which is clearly a useful feature for practical business applications. Wrapping up Thoughts Overall, the U-Net architecture was a clear choice for the Self-supervised Scene De-Occlusion paper. It is an excellent architecture for cases where output segmentation masks are most helpful than more traditional computer vision classification techniques such as bounding box.

Powering the Complete Lending Value Chain with AI

Mortgage lending is a data-intensive business. The volume of available data grows drastically in a mortgage company, with more added every day from calls and payment systems. Needless to say, the success of any lender is built on thoroughly understanding its borrower data, the speed at which it can leverage such data, the degree to which it can meet evolving customer expectations, and the technology it adopts to process it. Challenges faced by lending companies in changing times As the COVID-19 pandemic continues to create changes, many Fintech companies are under stress on many fronts. The pandemic has also exposed modernization needs for critical systems. At the same time, lenders also need to address the challenges such as fluctuating origination volumes, increasing costs, higher expectations from borrowers, and rising competition from new, technology-savvy entrants amid changing times. To compete in this environment — to even expect to stay in business — legacy lenders have started showing their willingness to abandon multiple disparate systems with fragmented data, rigid and inefficient legacy systems & processes, and embrace cutting-edge automation and digitization. How can Fintech fuel innovation amidst changing times? Fintech companies tend to have unique advantages that allow many to create new ways of delivering real value in the current environment and position themselves to thrive in the longer term. Fintech companies have several attributes that give them the agility needed to create and deliver new solutions rapidly. Generally speaking, they are Adept at analyzing and harnessing various types of data, such as credit and underwriting data Exceptionally focused on a seamless and delightful digital customer experience   Unlocking the real potential of Artificial Intelligence and Machine Learning to power the complete lending value chain The Fintech space has always been about disruption and driven by innovation, whether it is investments, payments, lending, capital markets, wealth management, or personal finance. From growing revenues, reducing churn, expanding customer bases, or managing risk and efficiencies, AI and machine learning can provide powerful tools for the top fintech companies in the world. Fintech’s traditional tech stacks were not designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What is needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI and ML have proven to be very powerful at interpreting and recommending actions based on real-time data streams. Machine learning has become ubiquitous, but organizations are struggling to turn data into value. The stakes are high. Those who advance furthest fastest will have a significant competitive advantage; those who fall behind risk becoming irrelevant. It is time for a change Because of rising loan costs, improving operational efficiency has become just as important to lenders as enhancing the borrower experience, maybe even more so. Undoubtedly, why a growing number of lenders have begun embracing artificial intelligence (AI) and machine learning, which remains the two most talked-about next-gen technologies in the mortgage industry today. AI models can help fintech organizations throughout the lifecycle of the loan process. For instance, in the initial phase of the loan process, AI can automate and optimize processes around identifying new target customers, predicting propensity to convert, risk-based pricing. And further, along the lifecycle, AI and ML can bring efficiencies and speed in loan processing through more accurate risk models, detection of fraud, and assisting underwriters with decisioning, managing customer churn, default prediction. These reduce costs, improve processing times, and customer experience. The light at the end of the tunnel The current uncertainty has undeniably placed businesses across the globe under economic duress, and Fintech is no exception. Albeit, many companies in the mortgage arena are already rising to the challenge and arranging their products and services to keep up with the evolving needs of customers who are struggling through the pandemic themselves. What is more, given their differentiated capabilities—namely innovation, resilience, and adaptability— many Fintech companies are well-positioned to survive the crisis and contribute to the industry in meaningful ways once the crisis is behind us. For this unprecedented crisis, if history provides any lessons, it may be that adversity inspires creativity. Final Thoughts Maintaining operational resilience is top of mind of most mortgage companies. Lenders that capitalize on next-gen technology to re-imagine their credit risk scoring and decision systems can enhance the quality of leads and make better recommendations while cutting down manual activities, maintenance costs, and losses. Transform Decision Making Tavant solutions enable customers to make better business decisions every day by incorporating the latest developments in machine learning. To learn more about Tavant’s machine learning-based conditions management & decisioning platform, visit here or reach out to us at [email protected]. FAQs – Tavant Solutions How does Tavant implement AI across the complete lending value chain?Tavant integrates AI throughout every stage of the lending process, from loan origination and underwriting to servicing and collections. Their platform uses machine learning to automate decision-making, reduce processing times, and improve accuracy in credit assessment, document verification, and risk management. What specific AI capabilities does Tavant offer for lending value chain optimization?They provide intelligent document processing, predictive analytics for risk, automated underwriting engines, real-time fraud detection, NLP for customer interactions, and machine learning models that improve decisions based on historical patterns. What is AI in the lending value chain?AI in the lending value chain refers to applying artificial intelligence to all stages of lending, including origination, underwriting, processing, servicing, and collections, to automate processes, improve decision-making, and provide predictive insights. How does AI improve lending efficiency?AI automates repetitive tasks, reduces document review time, speeds credit decisions, minimizes errors, offers 24/7 processing, streamlines compliance checks, and provides predictive analytics for risk. What are the benefits of end-to-end AI lending solutions?Benefits include reduced costs, faster processing, better customer experience, enhanced risk accuracy, improved compliance, and scalability without proportional staff increases.

Driving Efficiency with MLOps & Microsoft Azure

The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. However, as is always the case with new technology, machine learning comes with its own set of challenges: Concept Drift – Accuracy of model degrades over time due to disparity in training data vs production data Locality – Pre-trained models’ accuracy levels change with changing demography/geography/customer Data Quality – Changes in data quality affect accuracy levels Scalability – Data scientists, while good at creating models, don’t necessarily have the skills to operationalize models at enterprise scale Process & Collaboration – A lot of models are developed and remain confined to sandboxes or within silos in an organization with no clearly defined process for the model lifecycle Model Governance – Most data science projects have no model governance – who can create models, who can deploy them, what datasets were used for training? Most ML projects do not have this defined clearly A typical ML model lifecycle Here’s what a machine learning model lifecycle looks like:     What is MLOps According to Wikipedia, “MLOps (‘Machine Learning’ + ‘Operations’) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.” How is MLOps different from DevOps So, is MLOps just another fancy name for DevOps? Since machine learning is also a software system, most of the DevOps practices apply to MLOps too. However, there are some important differences: Team skills: a machine learning team usually has Data Scientists or/and ML researchers who may be excellent with different modeling techniques and algorithms but lack the right software engineering skills for building enterprise-grade production systems. Continuous Integration (CI) is not only about testing and validating code and components, but also testing and validating data, data schemas, and models. Continuous Deployment (CD) goes beyond deploying a package or service. It requires deploying an ML training pipeline that should automatically deploy another service (model prediction service). Continuous Testing is unique to ML systems, which is concerned with automatically retraining and serving the models. Monitoring – ML uses non-intuitive mathematical functions. It requires constant monitoring to ensure its operating within regulation and that the models are making accurate predictions. Implementing MLOps with Azure Machine Learning Tavant’s Manufacturing Analytics Platform (TMAP) is an analytics and machine learning-based platform that provides important business insights to our customers in the manufacturing domain especially Warranty. It is based on Azure and we use Azure Machine Learning’s MLOps features for managing our models’ lifecycle. Here’s a list of features that Azure provides for MLOps: Workspace – An Azure Machine Learning workspace is the foundational resource that is used to experiment, train and deploy machine learning models Development Environment – Azure ML provides multiple pre-configured ML specific VMs and computes instances. These come with most of the Machine Learning and Deep Learning libraries pre-installed and configured. One can also choose to create a local development environment if required Data Set – This step involves connecting to different data sources like Azure Blob Storage, Azure Data Lake, etc. and create a Machine Learning Data set. This Dataset can be used to access the data and its metadata when we create a Run Experiment & Runs – An experiment is a logical grouping of all the trials or runs. For each ‘Run’, you can log the metrics, images, data or enable logging. All these will be attached to the corresponding ‘Run’ under the Experiment. Compute Target – Creating a compute target helps you run your machine learning training. This compute target can be local or remote Azure GPU/CPU VMs Model Training – Azure ML already comes with multiple Estimators for Sklearn, Pytorch, Tensorflow and Keras. These Estimators helps you organize the ML training. Azure ML also has a capability to create Custom Estimators of your choice. All training logs, versions, and details will be logged in under the ‘Run’ of your experiment. Model Registry – Once the ML training is complete with different ‘Runs’ and you get the ‘best model’, the next step is to register the model in the Azure Model Registry. Model Registry maintains the model versions, descriptions of the model, model metadata, etc. Model Profiler – Before you deploy the model for real-time inference, profiling the infrastructure requirements for the model is very important. Profiling will give you a better understanding of how much minimum memory and CPU’s required for the model to give low latency and high throughput. Model Deployment – A model can be deployed to Azure Container Instances or Azure Kubernetes. This step involves providing which model and what version to deploy, its configurations, and the deployment configurations. Data Collection – It is used to capture real-time inputs and predictions from a model. It is used to analyze the performance of a model. Data Drift – It helps you understand the change in features, data quality issues, natural data shift, change in the relationship between features, etc. Conclusion MLOps is a must for enterprises using machine learning at scale. It allows for managing the complete model lifecycle including model governance and should be made mandatory for all Machine Learning projects. Azure Machine Learning provides has a great feature set for implementing MLOps. It does lack some of the advanced features like model lineage but one can always use dedicated MLOps platforms like MLflow or DotScience on Azure to bring in any missing features.

Manipulating Objects in an Image Through Self-Supervised Scene De-Occlusion

A well-recognized paper https://xiaohangzhan.github.io/projects/deocclusion/ from CVPR2020, introduces a complete framework for reproducing and recreating objects in a scene. It is a fascinating read, so in this article, we are providing commentary on the key aspects of this paper. To keep this article short, here we mainly present the structure of the framework, and specifically, we do not cover how the involved convolutional networks   Scene de-occlusion decomposes an image, extracting cluttered objects in it into entities of individual intact objects. Orders and positions of the extracted objects can be manipulated to recompose new scenes (Partial Completion Networks, or PCNets) work. We will cover that in a subsequent article. The framework presented in this paper trains two distinct convolutional networks, PCNets. Both convolutional networks have slightly altered UNet architectures, (https://arxiv.org/abs/1505.04597), whose output is at a pixel level. The PCNet-C (PCNet-Content) network uses a partial convolution for completing the unoccluded object. The images below are taken from the excellent video on the project’s page and available in the paper. The input for this framework is an image with appropriate bounding boxes around objects. From this input, we determine, in this sequence (numbering matches the image at the end of the post): the ordering of objects (1), the complete un-occluded shape of each object (4), and finally the complete un-occluded color and pattern of the object (6). With the un-occluded object, we can perform a variety of tasks such as rearranging the objects in a photo while maintaining the correct object ordering and performing image inpainting on the background. The image inpainting step is not addressed here as the paper’s novelty is in their self-supervised object de-occlusion method. Both PCNet networks are trained by creating random occluding shapes and overlaying them onto the image. By generating the occluding shapes, we can train the networks through self-supervision. The training of the PCNet-M (PCNet-Mask) network consists of placing a random shape either in-front of or behind the target object (as determined from the original bounding box). In both cases, the model is trained to predict the original target mask. The second case is meant for regularization and is necessary to prevent the model from always assuming an object is being occluded. PCNet-C is trained to complete the portion of the target object that is occluded from the random occluding shape. Note that, as in the image below, the target object is not only occluded by the random shape but also a car (the black car in the bottom right of the image). Any attempt to account for this occlusion in the training process would require knowledge of object ordering and thus require a supervised framework. Although we disregard this cars occlusion, other non-occluded car objects in the dataset will allow our model to learn the true shape of a car.  Importantly, the authors found that this training procedure generalizes to cases where there are multiple occluders, perhaps not overlapping with the pastry in the final image below. The framework procedure is described below (numbers match picture numbering): Recover the ordering of objects in the image using the PCNet-M We test for the ordering between two objects by selecting each as the ‘target’ and running the PCNet-M to find the amodal/un-occluded mask. If an object’s modal/occluded mask matches that of their amodal/un-occluded mask, they are not occluded by the other object. Retrieve all of the objects occluding(blocking) a given target object Generate two images for input into PCNet-M: A black and white image centered on the target object with the target, background, and union of occluding objects distinguished as in the picture below. A RBG image centered on the target object with the union of occluding objects greyed out. Use the PCNet-M to predict the amodal/un-occluded mask of the target object Generate two images for input into PCNet-C: A black and white image centered on the target object distinguishing between the target occluded object and the rest of the image. An RBG image centered on the target object with the difference of the amodal mask vs the modal mask greyed out. Use the trained PCNet-C to predict the amodal/un-occluded object.

Predicting Quality Assurance Code for Warranty Claims using AI

The Warranty claim process for equipment manufacturers requires their dealers to provide certain details about the machine part e.g. part number, date of manufacture, etc. Some of the key details provided by the dealers are the descriptions of the problems seen by the dealer, the potential root cause of the problem, and the possible solution. These descriptions map to certain codes as prescribed by the manufacturer and the dealer must pick the right codes and submit the claim for warranty. The list of these codes is usually long, running into many hundreds of codes, and many times, dealers end up entering the wrong code or select a default code like ‘Others’ or ‘Miscellaneous’. Once the claim reaches the manufacturer, their subject matter experts (SMEs) must validate the codes as part of the Quality Assurance process and end up spending a lot of time in correcting the erroneous entries made by the dealers. This results in a longer claim cycle and cost for the manufacturer. Bringing AI into Quality Assurance One of Tavant’s customers has been facing this challenge and was exploring the options to solve this using Artificial Intelligence (AI). So, the idea was to use AI to predict the right code based on the description entered by the dealer in real-time so that the data entered is correct and the SMEs don’t have to spend a lot of time in validating and fixing the codes resulting in better data quality and significant savings for them. The Challenge At first glance, it looks like the problem can be solved as a classification problem using machine learning. However, there were certain challenges to handle such as: Unreliable historical data – as mentioned above, the available claims data in the warranty system has a large proportion of incorrect codes, hence cannot be relied upon as training data. For instance, we found that there was a lot of imbalance in the data with a lot of data under the ‘Miscellaneous’ category code Descriptions entered by the dealers are free-form text and can vary in the style of language used The customer QA team recently formulated a set of new codes making the current codes in the data obsolete The Solution Natural Language Processing (NLP) has progressed leaps and bounds with state of the art changing every few months. This has mainly been driven by the rise of Deep Learning and specifically the usage of Word Embeddings – starting with Word2Vec to the current state of the art models like BERT, ELMO, etc. In word embeddings, words having the same meaning have similar representation and can also maintain the context in which the word is used, thus being able to differentiate between things like Apple, the company, and Apple, the fruit. We also approached the problem using Word Embeddings. However, to handle the mentioned challenges and to build a production-grade solution, a lot more was required. Below are some of the salient features of our solution: Data augmentation – there was very little data (~ 2 descriptions/code) provided by the experts from the customer – recall they had formulated a set of brand-new codes. We used NLP based data augmentation techniques to generate realistic descriptions Semantic similarity – we used word embeddings to find semantically similar descriptions and associated codes Continual Learning – the solution presents the dealers with top-3 predictions, and the dealer can select the right one from them. This allows the model to learn and evolve with more data Low latency – the models predict the code with sub-second latency this ensuring good user experience High scalability – the models are containerized using Docker and orchestrated using Azure Kubernetes Services (AKS), ensuring high scalability with an increase in workload The whole exercise would have been meaningless without reasonably high accuracy. We beat the expectations and achieved accuracy levels of almost 90%. This will improve further as more data comes into the system. The Benefits The ROI of the solution is significant with respect to the improvements in business metrics, as mentioned below: Reduction in time for the dealers to select the QA codes Better data quality as dealers can no longer assign a “default” code for the descriptions entered Reduction in time spent by the manufacturer SMEs in correcting the codes entered by dealers – from weeks to minutes Reduced claim processing time

Bringing the Power of Artificial Intelligence to the Media Industry

In the coming years, Artificial Intelligence (AI) and Machine learning (ML) are going to change lives so much that today’s science-fiction will be tomorrow’s reality. Earlier, humans made machines to reduce physical efforts required to do jobs. AI and ML are now giving brains to these machines and making them smart, which further reduces efforts. AI and ML have a significant impact on every industry. Automated transportation, predictive policing, intelligent gaming, enhanced health care, and smart homes are a few examples. Like other domains, digital Media and Entertainment industry has evolved in leaps and bounds with Artificial Intelligence and Machine learning.     With the power of Artificial Intelligence, the media industry is becoming more interactive, personalized, and engaged. The technology has enabled media providers to personalize entertainment to unimaginable levels using streaming services, including on-demand movies, music, live telecasts, etc. These technologies provide many algorithms to eliminate buffering and low-quality playback, getting you the best quality content using intelligence from your ISP providers. Also, with concepts of data science, ML algorithms intelligently analyze unlimited streaming data to understand consumer’s viewing habits to offer more useful recommendations. Along with this, the sequence-to-sequence learning technique of Machine learning can translate your content from one language to another, from one writing style to another, allowing your content to reach distinct audiences more efficiently. Similarly, NLP (Natural Language Processing) algorithms help writing top trending news stories to decrease the time to produce new content. Shelley, an AI tool developed by MIT, allows users to write horror and fictional stories through deep learning algorithms. Following this trend, one could state that the creators of the next great content may not be human at all. Traditional media planning models have limited information on behaviors and purchasing patterns of the targeted audience. AI and ML can power numerous kinds of models, such as: Machine Learning-based pay-per-click campaigns Machine Learning-based content campaigns Machine Learning-based hyper-targeted email campaigns Efficient predictive data modeling and planning These ML models help media planners and marketers increase customer reach, improve the relevance of the audience, and create a great user experience. With extensive interactions and existing research, AI will soon influence market strategies, including business models and sales processes. In the future, Sales and Media planners will be assisted by AI agents who will monitor telephonic conversations in real-time. For example, such AI agents might infer from the client’s tone that the client is unhappy with the approach of that media planner or salesperson, and AI agents might help them decide the best possible strategy. In this way, AI could augment the capabilities of team members. With the endless possibilities offered by AI, we certainly have exciting times ahead.

Maximize Production Capacity with Prescriptive Analytics

Management Science is an approach to decision making based on the scientific method that makes extensive use of quantitative analysis. In today’s world, many use the terms management science, operations research, optimization, prescriptive analytics, decision science interchangeably. One of the most significant management science applications developed by the operation research (OR) group came about as a result of the deregulation of the airline industry in late 1970. Consequently, a number of low-cost Airlines were able to move into the market by selling seats at a fraction of the price charged by established carriers such as American airlines. OR group suggested offering different fare classes (discount and full fare) and, in the process, created a new area of management science. The OR group was able to achieve this by using forecasting and optimization techniques to determine how many seats to sell at a discount and how many seats to hold for full fare. Let us understand what decision making is and what are the two approaches by which we can take decisions. Problem-solving can be defined as the process of identifying the difference between the actual and desired situation and then taking action to resolve the difference. In contrast, Decision making is the term generally associated with the first five steps of the problem-solving process. Thus, the first step of decision making is to identify and define the problems. Decision making ends with the choice of an alternative, which is the act of making the decisions. An example of decision making can be a student who needs to decide which job to choose based on job evaluation data that leads to decision-making problems. Making a choice from the available alternatives is difficult. If the student decides that salary is the only criteria, then his decision will be referred to as single criteria decision making if he selects multiple options along with salary, like location and potential advancement then his decision will be multi-criteria decision-making. And in the real world, it becomes even more complex to solve. This leads to two approaches, called the Qualitative approach and Quantitative approach. The Qualitative Approach is based primarily on your judgment and experience. It includes your intuitive “feel” for the problem and is more of an art than science. A simple example is a manager taking decision for his company based on his experience. The Quantitative Approach is followed when the problem is complex, then Quantitative Analysis of the problem can be an important consideration for your final decision. Using the quantitative approach, an analyst will concentrate on the quantitative facts or data associated with the problem and develop mathematical expressions that describe the objective, constraint, and relationships. Linear programming is used when the objective function and the constraints of the problem can be expressed as linear equations of decision variables. Such comes under prescriptive analytics, which helps in providing the optimal solution to a problem. Traditionally, Operations Research (OR) techniques are used for finding the optimal solution to a problem. Many machine learning algorithms use optimization techniques such as gradient descent while solving a problem. Based on the above understanding, let us try to solve a product mix problem that comes under linear programming. “Maximize production capacity of a Manufacture” Suppose two products need to be manufactured: Tables and Chairs. To manufacture a product requires two resources:  budget ($) and labor (man-hours) The resources required to manufacture each product and the total available amounts of each resource are given in the table below: Such Linear/Integer programming problems can have a significant influence on the profitability of organizations. Modern-day issues can have several millions or billions of decision variables and are solved using sophisticated software tools such as IBM CPLEX and FICO Xpress, GAMS. Such problems can also be solved using R (library ROI, lP solver, optimx), Python (library pulp), and even excel using solver. While experimenting on various tools like GAMS, Excel Solver, R, and Python, we identified optimal solution is 3 tables and 6 chairs. When using the Branch and Bound algorithm, there could be a scenario where multiple optimal solutions exist. We then leverage a qualitative approach in decision making. Reference: An Introduction to Management Science by Anderson Sweeney Williams Martin Business Analytics by Prof U Dinesh Kumar  

Retaining your Customers with Seamless Warranty Resolution

In this era, companies are trying to stray away from the term of one-time customers. Competition has become immense, and organizations are pulling every string possible to ensure they can retain and satisfy their customers.     Would providing the best product or having the best sales team solve the problem? They are all the fundamental part of the product’s sale, but the customers must have a healthy relationship with the organization even after the first sale. Here is when the warranty comes into the picture. Warranty is a written contract issued to the customer by the company, promising to repair or replace a product, if necessary. But providing a warranty alone is not enough. The company should follow a seamless warranty management practice that gives resolution with the best outcome and in the best possible timeframe. How does a seamless warranty resolution satisfy the customer? Customers are satisfied when they see a quick turnaround time for their warranty claims. Every touchpoint with the customer will be satisfactory if the warranty system is in place to give all the required information. The system should help the dealer/company make quick decisions regarding the customers’ warranty claims. How can a company achieve a quick turnaround time? Turnaround time is the time taken to resolve an issue. It is based on various variables, such as: Online questionnaire Parts availability Human resources Streamlined approval flows Failure identification Fraudulent detection Consistent systems Fast processing Configurable system   When a company achieves this, it would be able to process a customer’s warranty claim with the fastest and best resolution. The company can do all this with their allocated budget if they can streamline the process. Warranty systems give all essential data about the defects and can predict the possible future failures from the past data. The failure information captured in the system can provide valuable information to set up future field actions or campaigns. These field actions, when set up efficiently, can make a huge difference to customer satisfaction. A robust warranty system can address the following in providing a seamless customer experience: Providing timely update; the vehicle warranty status Ensuring quick claim processing time Providing notification to the customers on any scheduled maintenance or field action which may be due Having a telematics connection with the vehicle to record the machine activities Enabling customer feedback through an online portal Reducing wait time for claim approval Providing access to required information for the team to engage the customer’s issues   Wrapping up When an organization has a seamless warranty management in place, it has an added advantage over the competitors. So, it is clear to say, a seamless warranty system not only helps manufacturers in streamlining their internal processes but also helps in increasing customer retention and satisfaction. Reach out to at [email protected] in case you wish to gain more insights.

The Rise of Programmatic Advertising

Publishing ads in a heterogeneous media platform is a complex mechanism. Programmatic solutions address this problem through automated guaranteed, unreserved fixed rate, invitation-only auctions, and open auction transactions. According to IAB, in today’s digital supply chain, automation will continue to refine buying and selling processes and shift attention to higher-value marketing and advertising functions. Programmatic Ad Trends: The share of programmatic advertising in digital ad trade is growing across the globe. The stake of programmatic ads is two-third of online display advertising space, according to Zenith programmatic trend projections 2019. The potential of the programmatic landscape may depend on the following digital streams. pDOOH (Programmatic Digital out-of-home) media pDOOH is a medium to display ads in open/publicly accessible environment like Roadside digital billboards Outdoor signage Apartments lift lobby Airports Public waiting rooms Shopping centers etc. This medium not only reduces management cost but also influences customers with varied content. In 2019, industry revenue from billboards grew by 0.6%, which is more than $8.6 billion in revenue. pDOOH space is bright – more education and standardization with less fragmentation sets the ball rolling to long-term success. Voice Activated Advertising Amazon Alexa, Google Assistant, and Apple home are now becoming an integral part of our lives. These medium guides not only potential customers in the right direction, but also serves effective tailored ads. According to the VoiceBot.AI survey, 25% of respondents said that the daily grocery orders were mostly placed through voice-assistants. By looking at all these opportunities to generate more revenue, streaming audio platforms such as Soundcloud, Spotify, and Pandora have launched their applications with ad impressions. Also, the number of people to pitch is growing fast, with an estimated 1.8 million smart speakers sold last year and 15.1 million expected yearly sales by 2020, according to researcher strategy analytics. Advertisers are observing voice-activated advertising as the gateway to behavior-based targeted consumer engagement. Connected TV (CTV)  Over-the-Top (OTT) or CTV is a wireless or ethernet connected TV that can stream internet content like video or audio. Advertisers are now primarily investing in connected TV (new inventory becomes available, with YouTube, Hulu, and Roku) dominating the landscape. According to the eMarketer survey, CTV advertising will surpass $10 billion by 2021. Demand-side platforms (DSP) are building up their ability to sell ads to TV networks programmatically, and, with the increased availability of high-speed(5G) wireless networks, mobile users are increasingly watching digital videos on their phones and tablets. After COVID-19, CTV advertising may rise more steeply as focus on e-trade and digitalization is increasing. Header Bidding  In the ad world, most ads are picked and served on a publisher, based on priority; for example, the preference is given to pre-book an ad and then to others like RTB. Header bidding is a new technique, and this is different from priority. On a web page, the header contains page metadata and script code. To display ads, script code executes and sends auction request to all demand partners. Therefore, header bidder auction takes place in the header of the page even before the page gets loaded on the publisher. This is different from RTB, where auction occurred for those inventories which remain unsold after pre-book; therefore, if we compare quality-wise, header bidder auctions for inventories, could be considered premium as these are accessed ahead of pre-book ads. The Road Ahead As artificial intelligence is growing in the programmatic landscape, advertisers have now started to contemplate the future for better results in advertising through different solutions and media. All the above channels aid in reaching the targeted audience and allow surprising customers with innovation.  

3 Reasons You Must Adopt AI-Based Quality Engineering

Regulations are constantly changing in the mortgage industry. Lenders are under continuous pressure to meet fast approaching deadlines on UCD and HMDA. The Uniform Closing Dataset (UCD) is a standard industry dataset enabling information on the CFPB’s Closing Disclosure to be communicated electronically. The first deadline of 25 September 2017 mandates lenders to deliver borrower data and Closing Disclosure in the UCD file. UCD improves loan quality through increased data accuracy and consistency. This is of interest to the GSEs as it enhances the loan’s eligibility for sale in secondary markets. The year 2018 brings updates to the HMDA. The new HMDA rule requires over 48 data points to be collected, recorded and reported. This includes multiple new data points and a few modified from the previous rule. New fields include credit scores, CLTV ratio, DTI ratio, detailed demographic data etc. The CFPB asserts that the changes improve the quality and type of data reported by financial institutions leading to greater transparency. The updated regulations bring a new set of challenges to lenders. Investments in technology systems and processes can potentially increase the ever-rising loan origination cost. Data privacy and security is another concern. With the increased number of data fields, protecting sensitive borrower information is a priority. Additional data can also be used in fair lending claims thereby increasing litigations risks and costs. Since 2008, the mortgage industry has been taking giant strides in improving data reporting and compliance standards. TRID rule impacted the industry at almost every point along the transaction, and UCD/ HMDA will change the way data is collected, recorded, reported and delivered. Over the years, Tavant’s mortgage expertise has helped lenders implement regulatory changes with cutting edge technologies. In 2015, we helped multiple lenders achieve TRID compliance ahead of schedule. In 2017, we are doing the same with HMDA and UCD. It’s time to achieve Accelerated Compliance with Tavant Testing. The countdown is on! To learn more about our testing solution please visit: UCD/HMDA Compliance Testing by Tavant

Five Essential Features of a Good OTT Streaming Service

The recent spurt seen in streaming activity will be one of the many things that COVID-19 will leave behind as a legacy. However, this super fest of content consumption across dozens of streaming apps was in play last year itself as if all of this were prescience. From March 9 to March 16, the total streaming time grew to 156.1 billion minutes per day in the United States, compared to 127.6 billion minutes during the last week of February, as per Nielsen. In March, streaming accounted for 23% of consumer TV viewing time, up from 21% in February and 14% a year ago. All the major platforms have shown adaptability and scalability in handling the increased workload. It is a testament to the idea of cloud engineering and modern application concepts such as microservices. The core architecture of the streaming applications has evolved a long way over the last decade to be able to be so flawless now. This blog post tries to explore the concepts that are fundamental to building steady data streaming applications.     There are many functional and performance requirements for which an OTT service must be ready: Sensitivity to the bandwidth disparities in different networks and the fluctuations in network bandwidth Performance latency and buffering issues Ever-increasing catalog of content to be made searchable and accessible Access to vast content on a site or app that is lightweight Prevent latency issue es for popular and most liked content Handle high load and ability to deliver to all requests Maintain consistency of the content usage across devices and time zones Security and data encryption as well as entitlement Ability to store and record billions of user actions and then process and use the insight to make the service better Intuitive user experience across different screen and device sizes   These are but some of the requirements that a good streaming service would require to fulfill. While there are too many things from an architecture perspective that go into the building of a streaming app, we will try to cover a few foundation elements. Edge Computing and CDNs: Content is delivered to devices through CDNs, which act as the cache for the content. Edge computing uses the power of the cloud and takes computing power close to the end device. This is the first layer of logic that interacts with devices, and it links the request to the appropriate API within the services architecture. It also provides the abstraction layer to the mid-tier services. Load Balancing:  This helps the streaming service manage peaks in the load by implementing throttling mechanisms that reject the extra incoming requests and diverting them to other servers when the traffic crosses a threshold. Microservices: The entire backend application is split into hundreds of independent services. These services implement specific business logic, hence encapsulating one service from another. This enables the entire service to function without the risk of one flaw bringing the whole application down to its knees. These services provide functions such as authentication, licensing, playback, artwork, etc. Encoding and Content Delivery:  Every media file is broken into chunks and transcoded into different bit rates. This is done to provide the best possible quality to different devices at varying bandwidths. Every media content demands its quality standards. A fast-moving video will require transcoding at a higher quality than a video, which is relatively slow-moving. Adaptive streaming is used to push the most appropriate bit rate segment of the video. Local caching is used to serve subscribers. Push fill methodology is used to load the content in a regional CDN based on the popularity of the content in that region. Data pipelines:  Data is created at an enormous scale since billions of events take place in a day and millions in a second. A data pipeline must be designed to enable the cloud storage of the video viewings and UI activities. This data is used for big data analytics. This, along with device error logs and diagnostic events, is also used for monitoring and debugging operations. Final Thoughts Like Rome was not built in a day, it takes time to build excellent streaming services. Most of these components of a service evolve over time. The three goals of any streaming service to achieve architectural excellence are scalability, availability, and immutability. Scalability enables the streaming service to scale for massive audiences and handle spikes efficiently. Availability allows the service to be available for requests while maintaining the site’s performance. Immutability provides robustness to the architecture so that any change to the infrastructure on the fly does not upset the system and disrupt the service. Companies can use third party tools or build the components on their own, depending on the scale of the service they are trying to develop. Custom build applications provide more control to the companies, and they can build services based on their requirements. Several open-source components can be used, which can be then built upon to create exciting, viable products that can scale quickly.  

Aftermarket 4.0 During and Post COVID-19 (Using IoT, AI, Chatbot and DIY)

In today’s volatile, uncertain, complex, and ambiguous world, we are facing unexpected impediments. And manufacturers are one of the most negatively impacted industries. Due to plant shutdown, Auto manufacturers alone in India incurred a loss of close to three billion dollars, and it continues.[i] David Nabarro, WHO special envoy for COVID-19, stated, “I believe that the world will have to learn to live with Corona in our midst, and that means being on a constant defense everywhere as we do with other infectious diseases.” [ii] Businesses that want to survive and win are looking out for avenues to create a system of disaster-response infrastructure. They understand that most customers are not loyal to a brand; they are loyal to their needs of that brand. “Needs” such as quality, prompt response, timely services, value for money, or priority privilege, and so forth. Organizations are finding ways to reach their customers and reassure their peace of mind.     To offer initial diagnostic service call, Consumer Priority Service Corporation, an international provider of repair and extended warranty services for the consumer electronics and appliance industries, has partnered with Zoom.[iii] Warrantywise, a British auto extended Warranty selling company, is now allowing their dealers to sell extended warranty policies from home.[iv] Almost all auto companies are offering an extension to their policies that are about to expire soon. Is that enough to keep up the brand performance? How do the leaders ensure the safety of their employees and customers and still secure the supply chain efficiency? How can they manage near-term revenue and cash expenses while satisfying endless customer needs at the same time? Can sales be achieved as a by-product of responsive, reliable, and reassuring warranty services offered to the customers who are dealing with much uncertainty? Dealing with such a situation, the leaders must equip their companies with a pandemic-response capability by building a robust digital infrastructure with the help of Aftermarket 4.0. So, what is Aftermarket 4.0? It is an Aftermarket services version of Industry 4.0. Aftermarket 4.0 is an infrastructure and practice that includes cyber-physical systems, IIoT, cloud computing, big data, and predictive analytics to instill a culture and trend of automation and data exchange in the operations. The benefits are enormous, such as: 1. Data Access across the supply chain 2. Improved safety, output, and satisfaction for employees, workers, and customers 3. Enhanced customization based on customer and operational needs and different services offered 4. Improved productivity and business output To enable these changes as a leader, you must follow the five points described below: 1. An Intelligent Warranty Management System: First thing first, all manufacturers that are in the business of warranty must go for a seamless Warranty Management system to automate complex human-led processes and decisions. Manufacturers must get rid of all their age-old on-premise solutions. They must aim for an intelligent cloud-based system that seamlessly binds different organizational aspects, both internally and externally. 2. Building AI Base: Leaders must consider the capability of the warranty management system to working seamlessly with near real-time data received over IoT and uses artificial intelligence to automate more decisions that were otherwise needed collaborative human interference. 3. Enabling Chatbot: By consulting and evaluating the historical customer queries, the AI system can identify and present various patterns of problems vs. solutions. Chatbots, backed by extensive data, can especially be very useful in responding to customers’ queries practically instantaneously. This investment towards the Chatbot can save the enormous human hours spent otherwise. And, at the same time, reassure customers with prompt and significant responses. 4. Prescriptive Maintenance: It is no longer enough to predict the failures alone. The ‘Smart Manufacturing’ demands ‘Prescription’ along with the ‘Prediction’. Prescriptive maintenance not only tells you that a problem is likely to occur but also shows you multiple response scenarios to choose from. By utilizing artificial intelligence and machine learning methods, perspective maintenance advises the technicians on what to do and how to perform a repair. 5. DIY Friendly products and solutions: The last step towards building the eco-system of robust customer satisfaction is to enable and offer more ‘Do-It-Yourself’ activities on maintenance for your customers. Encouraging your customers to use the mobile app version of your warranty management system to scan parts receipts/invoices to the appropriate places is a great way to replace the paper trail for the repair history, helping you to keep the warranty intact.   Final Thoughts The clock is ticking. We are in unprecedented times. Nevertheless, there are many possibilities, but are any of them being practiced? And where do you get such a futuristic system? Reach out to us at [email protected] today. We got you covered! References: [i] https://www.deccanherald.com/business/business-news/coronavirus-impact-auto-industry-to-suffer-loss-of-rs-21000-crore-due-to-plant-closures-817266.html [ii] https://www.indiatoday.in/india/story/coronavirus-new-reality-we-will-have-to-learn-to-live-with-it-who-official-david-nabarro-1660190-2020-03-27 [iii] https://www.cpscentral.com/consumer-priority-service-partners-with-zoom/ [iv] https://cardealermagazine.co.uk/publish/warrantywise-can-help-dealers/188765  

Staying on Top of Quality Control in an OTT Video Streaming Landscape

Streaming services like Netflix, Disney+, Amazon prime, etc. have seen a substantial rise in subscriptions in recent times. NBC has also unfurled its streaming site ‘Peacock’, making it available to Comcast subscribers. Nielson’s latest report reveals that the on-demand video streaming is up nearly 100%, which is significantly much more than the pre-pandemic time. The rise in the consumption and production of OTT video has brought about its challenges. Increased consumer choices lead to churn if the quality of viewing experience is poor. Therefore, it is critical to make Quality Control (QC) part of the end-to-end OTT process to ensure that high-quality content gets delivered across multiple platforms. This helps to avoid higher operational costs and delays in issue resolution, thereby increasing customer satisfaction. How is Quality Control done? Whenever we encounter stalling or buffering in a video, it can be due to various reasons. It can either be a single point of failure within one of the components or an integration failure between different components or low internet speed at the endpoint. Testing needs to be done at various points to identify the cause of the disruption. Every OTT provider adopts different practices for quality control. Let us discuss the stages at which the providers generally do the testing with the help of the underneath diagram: In a typical OTT system, the validation happens at three main points: Test point#1 – Testing after encoding and ingestion  The ingested files are checked for their integrity and compliance to ensure that they are not corrupt and have been encoded to the standard that the downstream systems can consume without any issues. This quality assessment is done with the help of a perpetual quality assessment algorithm called VMAF (Video Multi-Method Assessment Fusion). VMAF is developed by Netflix and is made available as open-source. VMAF score varies between 0 to 100. Higher the VMAF score, better the quality. Test point#2 – Testing after transcoding During transcoding, every input stream or file results are converted into an array of outputs or profiles. Multiple renditions of the same stream or file are created at different quality or bitrates, also called profiles.  Below are the tests performed at this stage: QoE (Quality of Experience) checks – Each stream or profile needs to be tested for its quality like rate, format, syntax, loudness, blockiness, etc. ABR (Adaptive Bit Rate) checks – Test is performed to ensure if the frames are time aligned across each of the profiles to enable seamless switching.   Test point #3 – Testing at CDN This is the final point of testing. Below are the tests performed at this stage QoS (Quality of Service) checks – Here, the tests are performed to ensure the content is accessible over HTTP/HTTPS to catch any download delays/failures. All requests/responses(3xx,4xx,5xx) are passively monitored and logged. Content downloads are simulated in the network congestion environment to observe the behavior of the distribution server under stress conditions. QoE (Quality of Experience) checks – Basic audio and video quality checks done to check for blockiness, black frame, loudness, etc. Entitlement checks – Check if the file is accessible only by those entitled and if the unauthorized redistribution is prevented. The test is also done to decrypt each file and look at the audio and video available to subscribers (for every profile) ABR (Adaptive Bit Rate) checks – Test is performed to ensure if the frames are time aligned (between audio, video, and subtitle) across each of the profiles to enable a seamless switching Player control checks – This includes testing of play, pause, rewind and fast forward controls of players during content or ad playback, content bookmark validation for the played content and autoplay validation at the end of the video Video playback analytics validation- Validation is performed if the analytics calls are triggered on user actions like play, pause, full screen, etc.   Looking Ahead OTT testing is still evolving, and so are the requirements for testing. Broadcasters and content owners are continuing to spend a significant portion of their revenue on acquiring content. However, this cannot be monetized until the content quality gets to attract and retain viewers. The methods used need to be architecturally versatile and must allow content providers/broadcasters to figure out the most critical areas to focus on. An industry-driven by rapid change requires more than technical evolution. It needs what we call ‘revolutionizing.’ By deploying the complete QC automation and monitoring solution at the appropriate points from ‘Ingestion to Delivery’, broadcasters can deliver the best experience to all the viewers in the OTT world.

Embrace CX Equivalent to AI & UX to Reinvent the Future of Fintech

Making Transformation Real The pace of digital transformation has intensified dramatically and empowered customers to engage at their convenience with organizations with whom they interact and transact across multiple channels. Amidst this, there is an unstoppable rise of automation, analytics, and AI, and with that comes unprecedented levels of speed – the speed of accelerating business, generating ROI, making intelligent decisions, meeting evolving customer expectations, and bringing new products and services faster to market. AI, AI Everywhere Today, even the most advanced digital technologies are usually reactive rather than proactive. Think of intelligent digital assistants such as Alexa, Siri, or Cortana, you just need to give them a command, and they’ll respond to it instantly—ordering a product you’ve requested, say, or placing a call. However, when powered by transformational technology, these virtual assistants can become more intelligent and proactive. Soon, your virtual assistant might observe that you’re running low on a particular product and suggest that it place an order for you—or tell you how you can find the best value by adjusting your purchase habits. Or imagine that you enter a retail store, browse shelves aided by an intelligent digital assistant, and once a purchase decision has been made, you simply walk out of the door with the product. Across the BOARD  With more and more modern consumers expecting a response to their queries in less than an hour, the digital technologies will prove game-changing. Organizations must unlock digital transformation — and leverage the advantage of AI and machine learning. As more and more organizations are pushing for differentiation, AI-driven CX is a pivotal area. Needless to say, AI and ML will exponentially improve CX through intelligent chatbots and virtual assistants. It will help push margins regardless of industry or sector type. Given the current experimental status, early adopters will have a clear mover advantage. For FinTech companies, this indicates a new way to attract eyeballs, emotionally connect with customers, and build an everlasting relationship. How is AI causing a seismic shift for CX? Beat fraudsters before they strike with Predictive Insights and Real-time Analysis Analytics tools collect evidence and analyze data necessary for conviction. Subsequently, AI tools learn and monitor user’s behavioral patterns to identify rarity and warning signs of fraud attempts and incidences. Claims management can be built up using Machine Learning (ML) techniques in different stages of the claim handling mechanism. By leveraging Enhancing CX with Predictive Analysis  Predictive analytics in financial services can directly impact overall business strategy, revenue generation, sales nurturing, and resource optimization. It can undeniably act as a game-changer by enhancing business operations, improving internal processes, and outperforming competitors. Predictive analysis gathers and arranges the data, analyzes it using our leading-edge algorithms and technology, and briskly deploy customized, prescriptive solutions unique for each customer. It can help calculate credit scores and help organizations prevent bad loans as it uses a massive amount of data to find patterns and predict insights. These insights and results can reveal what is going to happen next: what the customers are willing to buy, how long your employee might last, and so on. Delightful CX through UI/ UX Creating intuitive experiences with the help of smart UI and UX designs to enable your business to render excellent customer experience. If you engage with users through seamless navigation, layouts, directions, etc. it will help you enable superior customer experience. UI and all kinds of assistants stand at the forefront of all Fintech as a service. No matter how complex the formulae are, how bizarre the analysis is, or how advanced technologies used — the customer still needs to navigate it and utilize everything properly. Regardless of the industry, the business will perform better only if the customer feels valued. And that value can only be brought by delivering unique CX. All over Déjà vu again! Companies – fearful of straggling behind – scrambled to build online footprints back in the 1990s, when the WWW was the digital frontier. In today’s digital era, AI is causing a similar seismic shift. When keeping customers happy has never been tougher. They’ve more of everything: devices, information, channels, and choice. They also have more power. They can switch brands on a whim – and if they don’t like something, they will broadcast the fact over social channels. What’s more, customers’ expectations are ascending ever higher. They have witnessed how digital disruptors deliver frictionless, connected, automated, and personalized Customer Experiences (CX) – and they expect you to do the same. Such is today’s CX challenge. But as with any challenge, it’s perfectly surmountable. Indeed, if organizations embrace AI + UX and act fast and transform their enterprise to a data-driven, connected and adaptive CX infrastructure, not only will they secure the customers they have, but also win the new ones.

How Great Leaders Help Their Teams Excel

Great Leaders expect great work from their teams. It is the ongoing commitment and deliberate practice that helps build a great team. The question is how great leaders discover their team’s potential and allow them to express it fully. Developing the team is like growing seeds to becoming a tree. The seed needs three ingredients – water, correct temperature or warmth, and good soil (environment). During its early stages of growth, the seedling relies upon the food supplies stored with it in the seed until it is large enough for its leaves to begin making food through photosynthesis. Similarly,  a team needs three ingredients to develop – ‘Trust,’ ‘Coaching,’ and ‘Opportunities to stretch.’ Establishing a foundation of trust and acceptance: Just as the seed needs good soil, likewise, a team also needs a right environment- an atmosphere of trust and acceptance that builds the foundation for having meaningful and open conversations, beyond just day to day regular activities or performance.  Development discussions include aptitudes, interests, guiding the different paths, and how leaders can help team members achieve what they want. Investing time and coaching the team:  Water and warmth are needed for the growth of a seed. Similarly, nurturing and supporting the team is essential for their growth. Great leaders take time out from their schedule to invest, coach, and grow their team. And when the right opportunity comes, they delegate work to enable the team to demonstrate their potential. Provide opportunities to stretch: As a plant grows, the leaves begin to make their food by absorbing the sunlight and through photosynthesis. Similarly, as the team grows, leaders need to provide challenging opportunities because giving someone a good challenge, and a real stretch allows them to develop and unlock their potential. Teams then set a high bar and provide support to those working to reach it.   Plants need water, warmth, nutrients from the soil, and light to continue to grow. Similarly, a team also needs regular feedback, rewards & recognitions to enable them to grow and excel. Successful Ways to Encourage Employee Development Provide regular feedback: Regular feedback is a gift that every team member receives to realize those aspects where something is not being done in the right manner and needs correction. A great leader can point out those blind spots and enable us to find the way out to resolve and do course corrections. This gift truly nourished a person’s growth. Encourage and Applaud: Development is a journey – when too many obstacles and challenges are there, some may tend to give up. Great leaders work together with their team to improve their performance. If you believe in their potential, they will meet the confidence and excel. Holding the team members accountable for their performance is as important as encouraging and motivating them. Appreciation for their excellent work and accomplishments to team members are like nutrients in the soil that enable the plant to grow faster and stronger.   Wrapping up Personalized learning is the way of life today. Every person should own their growth and career path. The sky is the limit, especially when changes are happening fast. There is no limit to creativity and innovation in today’s world. Great leaders help their team to explore, develop on their potential, and excel in everything they do. Testing the changes,  Testing the changes,  Testing the changes 3

Crowdsourced Learning Enables Communities to Grow

The word ‘Crowdsourcing’ was coined in 2005, a portmanteau of the crowd, and outsourcing is a process of collating services and solutions from an online community. One good example is Wikipedia, where we post information, and people across the globe can add their inputs or comments. ‘Learning anywhere, anytime’ is the need of the hour when everyone focuses on continual learning. Opportunities to get information are immense, and therefore taking advantage of ‘Crowdsourcing’ as one of the methods of learning proves advantageous in today’s times. Here, learners come together to achieve a common task and learn from each other, which benefits mutually. Some aspects that need to make Crowdsourced Learning successful are: Transparency and Openness: Learning in a cohort is based on trust and respect for each other. People need to be authentic in what they communicate at the same time, be open to other’s points of view. Being sceptical will not help in visualizing other’s points of view or seeing from another lens. Ideas, Design, and Preparation – It is important to think and prepare well before adding new ideas. Giving feedback is excellent, but it becomes more effective when people understand concepts well. Critiquing also helps in learning and improving one’s skills. It is important to use a common language to communicate unfamiliar topics or skills. This way, people who are engaged and interacting can understand concepts well. Simplicity in communication – An open network of communication requires simplicity in delivering thoughts and ideas. It is important to be specific as well as provide details as we are unaware of the audience and their familiarity with the topic being discussed — effective communication when crowdsourcing requires a balance of both specifics and details. Incremental Learning – Crowdsourcing is a progressive way to learn. Learners need to provide references and links wherever appropriate to understand facts and points of reference. As people who are engaging in the discussions increase, the discussions become more meaningful, people become more familiar and experienced. Feedback provided becomes more relevant. Enriched Community – Learning from sharing ideas, thoughts, experiences, and observations enrich each person with abundant knowledge. It becomes community-based learning, which provides opportunities to learn from each other. Collaboration is the key to crowdsourced learning. It helps communities to grow and perform better.

‘Own Your Learning’ is Today’s Mantra

With the pace of change in every aspect of life, it has become crucial for every individual to practice ‘Continual Learning’. The beauty is that with the knowledge explosion, we have so many ways to access knowledge and learn. Information is all around us, but we must make the choice of making time, getting the right information, learning and then practicing it to gain expertise. While organizations today promote a culture of continual learning, individuals need to take time out, leverage every opportunity that is available, build new knowledge and skills for personal and professional growth. Here are some ways by which people can practice continual learning: Books and articles – Some of us like to read books or articles – a great source of information. It is available as printed copies as well as digital. Many have lost their interest in these, and the buzz phrase one gets to hear is, ‘I don’t get time’. With long hours of travel and bad traffic, this is perhaps not an excuse, but a fact. Some say a lack of energy, and some have less attention span! While digital methods have given various options like kindle and tablet. Book summaries are one way to get the gist of various books. There are various speed reviews available. YouTube also has book summaries presented in infographics, which are easier to understand. Reading articles from various websites such as HBR, McKinsey, Gartner, Deloitte-Bersin, etc. Podcasts and mobile learning via apps are another way to learn – it is like listening to a radio show. Audiobooks are like lectures that people can listen to on their own schedule. TED Shows and watching videos are another great way to learn from experiences. Attending webinars and seminars is another method by which individuals and teams can develop a focus on their continuous learning to build new knowledge and skills. Taking online courses using the various MOOC (Massive Open Online Courses) gives a lot of opportunities to individuals to learn from masters, experts and educational organizations across the globe. Taking challenging assignments and new projects in areas that individuals have not been exposed to earlier. This gives them opportunities to network with people across teams as well as external experts. When people reach out to mentors and coaches when things are unclear, they get guided into solving complex problems and also learning from experiences. When working with an expert, observing the approaches of the experts also gives insights to specific areas. Immersive learning by solving real-time problems with the team by trial and error is another great way to learn in an agile manner. Once concepts and new approaches, technologies, or tools are learned, practicing, and applying new skills helps in gaining expertise in various skills and competencies. It is also important to reflect on progress as a self-assessment. Self-analysis is a great way to track improvement and progress of learning. Another way to work towards improving skills is asking for feedback. Meetups, participating in discussion groups, and other networking methods are other methods to learn from experts around you as well as across the globe. It is important in today’s times to be aware of changes, appreciate those, and keep one’s mind open to new learning. Points to remember: Learn a wide variety of things, not just topics related to current role or work Seek out more knowledge from a variety of sources, by doing new things and having new experiences Always be ready to learn something new and look out for trends and future technologies/industry changes Appreciate change and see it as a new opportunity to discover new paths Be ready to take risks, experiment, fail and learn Be connected and increase your networks – there is always new experience and perspectives which broaden our minds to provide new ideas Be agile and leverage every opportunity to learn Use your social connections to share and learn So, leverage every moment to learn something new through reading, sharing, observing, reflecting – any mode that you get – enhance your knowledge, skills, and get empowered!

Top 5 Salesforce Trends that Will Shape 2020

Salesforce has become a business-critical application in an age of digital transformation, where once 24/7 management for Salesforce was uncommon, it’s now becoming a new expectation–impacting the broader ecosystem, as well as the teams, partners, and companies that surround it. Why does Salesforce continue to astound reviewers across the board? What is it exactly about? Ease of use, Customized CRM, Automation, or Analytics? All the answers are resounding yes, but there’s something more than that. No doubt, Salesforce is an important part of business success and strategies with over 150,000 customers and their success stories and positive reviews. But the other side of this success can be explained as- Salesforce = Industry + Innovation. Here are the top 5 Salesforce trends, which are revolutionizing businesses today to support the above equation. Enabling AI with Salesforce Einstein – Vision, Prediction & Voice Do you know, how quickly AI-powered apps can be built for employees, partners, and customers on an incredible artificial intelligence platform? From machine learning and natural language processing to computer vision and automatic speech recognition – with Salesforce Einstein brings you the power to think, see, and act to success. Three methodologies, which are nailing the entire innovation are: 1. Einstein is a key catalyst for Salesforce: o Einstein Prediction Builder and next best action What if someone could predict the future? Yes, Salesforce Einstein leverages past data through machine learning to predict future activities with minimal programming. It helps predict business outcomes, create custom AI models on any Salesforce field or object with just clicks and not to code. With a guided approach, it works with yes/no types of questions and predicts numerical data. It provides a scorecard of the expected accuracy of the prediction and provides key insights from the results. o Einstein Vision See from the far! This methodology is beneficial to keep track of the entire conversation about your brand on social media and more. Image recognition in your apps by training deep learning models is another plus. This is to recognize your brand, products, and more. We can apply in our application workflows like visual product search, identifying the product, and automated planogram analysis. Einstein Image Classification leverages pre-trained and customizable models to recognize and classify images specific to your business, at scale. While Einstein Object Detection leverages customizable models to recognize and count distinct objects within images, providing granular details like size, count, and location of each object. o Einstein Voice Talk to an AI assistant is just like experiencing a real conversation. Talk to Einstein Voice Assistant to get daily briefings, make updates, and drive dashboards. It also helps create and launch your own custom, branded Voice Assistants with Einstein Voice Bots. With Service Cloud Voice, Voice assistant, and Voice skills, it will serve the customer in a whole new way. Needless to say, Einstein Voice is a game-changer with the power of the voice and the intelligence of Einstein. 2. Accelerating transformation with Salesforce – A digital reinvention Today’s business world is undergoing the process of transition. With newer technologies evolving, industries across the globe are determining new processes as older ones may no longer be enough. Customer expectations are constantly evolving. They are more connected, informed, and unprecedentedly technologically savvy that they expect the organizations to be likewise up to date. Digital transformation starts with customers. Digital transformation is as much a cultural shift as a technological shift. Leveraging the industry-leading Salesforce solutions and its innovative applications is one of the best ways to achieve quality, efficiency, and scalability faster. Salesforce helps you make it easier for businesses to sell more and grow with the help of CRM solutions. It helps you focus on individual relationships with customers, service users, colleagues, or suppliers. It even helps in finding new customers, winning their business, and providing support and additional services, and much more. 3. Designing Seamless Customer Experiences – Customer 360 Journey This is a customer-driven era where customers expect connected experiences across channels and departments, and no matter what, you want to fulfill that. Being a #1 CRM, Salesforce enables your organization to meet these expectations by building online customer communities for clients to share their ideas and resolve a problem. These communities allow agents as well to connect more easily with customers through a variety of channels and resolve even the most critical problems of customers in just a few clicks. Design experiences across channels to help your business drive customer delight and deliver the seamless experiences they expect. 4. Unifying the Customer Experience – MuleSoft Capabilities with Salesforce As we discussed, customers expect connected experiences. They want to avoid experiencing the layers where your systems and departments meet. MuleSoft’s Anypoint Platform and Salesforce Integration Cloud help connect every experience by making it easy to connect any application, data, and device with APIs — Application Programming Interfaces. APIs take requests and tell a system about the user’s needs and requirements, conveys system response to that user as well. This process helps optimize a reusable process and enables organizations to accelerate IT delivery, increase organizational agility, and deliver innovation scalability. MuleSoft works as a Salesforce Connector. It can connect any system, application, data, and device to unleash the power of the Customer 360. The integrated capabilities of MuleSoft and Salesforce enable companies to unlock data across systems, develop scalable integration framework, and ultimately create differentiated, connected experiences at a rapid pace. 5. Data Virtualization: Multi-Org Strategies with Salesforce and Heroku Multi-Org Strategy enables a customer to own multiple Salesforce Org. Data and applications are split in different Orgs based on various factors like business units or product lines. Salesforce Multi-Org. The strategy helps you with data separation, reduces the risk of exceeding Org limits, improves time to market, gives freedom to innovate, simplifies Org-wide setting management, reduces the risk of teams to be impacted by shared updates, reduces complexity within a single Org and more. Here comes Heroku Connect to make a difference.  It helps Salesforce to share data with your Salesforce

Four Key Factors to Consider Data Testing

It is a data-driven world. The amount of data produced each day is 2.5 quintillion bytes. And by 2020, 1.7 megabytes of new information will be created every second, per person. Data, therefore, is the new oil, and businesses have already started leveraging this data for their benefits. With a growing movement to digital technologies like IoT and AI/ML, data is all set to become even more valuable. However, what is of paramount importance is the way this data is leveraged. The reality of data is that it is never clean. Data is either incomplete, or inconsistent, or invalid, or inaccurate. For the sake of understanding, let’s take a simple example – in a mortgage firm, while collating data about debtors, there could be fields that might be missing from the source like no zip code (incomplete data) or there could be an error in entering the zip code and its only 4 digits, making it inaccurate. These errors can lead to inaccurate insights that can result in revenue loss, missed opportunity, or even reputational damage. Testing, therefore, forms an imperative part of any data strategy. It is critical that data is tested at the source to clean, correct, and validate it. A good-quality data brings in several benefits to a business, which include: a. Boosting productivity: Data scientists are data experts who have been hired to analyze and interpret data. Instead, 80% of the time of a data scientist is spent on cleaning and preparing the data, which also is the least enjoyable part of their work. In short, businesses are wasting their premium data scientists as ‘data janitors’. Testing data can prevent this and data scientists can focus on their core work – getting relevant and actionable insights. b. Making better decisions with the right insights: A better data quality results in the right insights, which in turn leads to high confidence decisions by removing the ‘guess-work’ out of critical decisions. This decreases the business risks to a great extent. c. Enabling customized targeting to customers: With the right data, the business has the right insights to customize its offerings to the customers. Imagine this – a Netflix user who watches a lot of horror shows and movies would continue using Netflix if he gets the right recommendations. d. Improving revenues: Businesses lose millions of dollars every year due to poor data quality. In the above example of Netflix, if the user doesn’t get the right recommendations, he would move to your competition, thus resulting in the loss of business. Conclusion: Needless to say, Data is important. However, one of the biggest issues is masking unstructured data and archiving data. Moreover, organizations also lack the necessary skills and expertise to test their data, which results in inaccurate insights. Data can only be as useful if an organization can maximize its full potential. The good news is that Tavant can offer a leading-edge automated solution for testing data to improve its quality and make it appropriate for consumption. For more information, reach out to us at [email protected]. Source – • https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#3fd7751d60ba • https://www.nodegraph.se/big-data-facts/ • https://hackernoon.com/a-few-facts-to-take-into-account-about-big-data-market-growth-eaf7c993f0fd

Optimizing OTT Ad Performance by Identifying Right Metrics

In the digital world, everything that happens leaves behind an electronic trail behind, and thereby every consumer’s actions and movements can be monitored. This is what differentiates OTT (Over-The-Top) advertising on digital platforms from our traditional way of advertising like cable TV or print. Leveraging OTT enables one to gain access to a lot of information and data about consumers in terms of how they engage and interact with advertising campaigns. OTT platforms provide better control and transparency over the ads served, thereby providing the best options in terms of placing as that will have a higher completion rate. These insights into every feedback, whether negative or positive, for the betterment of future marketing and advertisement campaign. Ensuring the right campaigns are run to the right set of audiences at the right time needs pacing OTT campaigns evenly across channels through optimization. There are several key metrics that need to be tracked in order to optimize OTT ad performance: Click-Through Rates (CTR) Rather than traditionally applying advertisements, the modern audience is expecting a considerable step up in terms of advertising techniques. Hence click-through rate is an important measure to improve and optimize OTT ad performance. Therefore, over time, the comparison of results and analysis from different ads or platforms enables us to have data that can be used in further study of engaging messages. Ad Optimization Even in the advertising world, “First Impression is the Last Impression” holds good in various front. Therefore, it is better to optimize OTT ads in a way that it will perform best. There are many characteristics that play a vital role in OTT campaigns. Color and accents usage, images, fonts, sound, language, visual graphics, ad size, etc., are some of the aspects that should be kept in mind while optimizing OTT ads. Device Optimization This is another technique that can help in the optimization of OTT ad performance. Ads should be rolled out, keeping in view the device they are on. Let’s say that your ad of Samsung Galaxy Note 10+ is to run on the iTunes Store. This would turn out that the ad might not be viewed in the right format. Therefore, it’s always better to know what ads are running on which device because it can dramatically improve the ad’s performance. This is another way to optimize OTT ad performance. Live Event Advertisements Broadcasting live ads can provide several benefits to the advertisers. 95% of estimated sports viewing happens live. From live concerts to sports events, it is an effective way to optimize OTT ad performance. Live broadcasting of ads can provide vital and crucial data by responsiveness and engagement of the viewers. Let us say that an ad is broadcasted live, and the website traffic increases, then there is a clear relationship between both. Therefore, tracking and analyzing website traffic while broadcasting live can tell us to gain detailed information in real-time. However, with prerecorded content, these ads can be watched at any time of day or night or anytime in the future. In the future, OTT attribution will be the critical element in the optimization wave. This will help teams to analyze campaign performance at device level reporting and impression forecasting to identify the right audience segment and serve the right ad across preferred OTT device and platforms. Overall this will help boost ad completion rate and conversion, thereby ensuring the right set of outcomes are delivered for each advertising dollar spend.

The Future of OTT Cross-Media Measurement in the Digital World

The basic concept of OTT (Over the Top) was to deliver television viewers content wherever and whenever they wanted, along with the goal to give users the quality and the live TV experience. With personalized content being served, the need raised on understanding how content was being consumed by end-users. Information such as who viewed the content, for how long, from where and which device the content was viewed became important for advertisers and agencies for providing the right audience at the right time. For brands, it’s important to know where your consumers consume media – whether it’s TV, mobile, tablet, or consoles. These questions are difficult to answer. Therefore, brands are wondering where to buy media to start engaging consumers with their ads. How can they optimize their campaigns across different platforms? That’s where cross-media measurement comes into the picture. What is Cross Media Measurement? Cross-media measurement has been in focus for quite some time by media researchers. The scope includes measurement of various content, including images, videos, or music along with the devices they are viewed across various matrices. All the information that is gathered is thoroughly classified, analyzed, and researched to generate a clear picture of how content is being consumed. This approach helps various stakeholders within the media industry to create, distribute, and monetize content much more effectively and forms the basic concept of cross-media measurement. With the change in media consumption, advertisers are looking at multiple ways to reach the right audience. Therefore, they are running integrated campaigns along with different media platforms and devices. However, to do that it is more important to assess everything with accuracy and correctness. The main advantage of cross-media measurement is that it provides a better and on the spot results and views from the consumer end. Therefore, advertisers can have a more detailed look at their media consumption habits. OTT Cross-Media Measurement – Looking Forward The main advantage of OTT is that unlike cable services, both advertisers and service providers can analyze a large amount of data about their consumers. The beauty of OTT cross-media measurement is that data can be accessed as per the demand. Since every single operation is programmed, it is relatively easier to analyze data from the viewer’s recommendation. So, what is the future of OTT cross-media measurement? We cannot say for sure whether linear TV will be replaced in the span of the next couple of years. But there’s a bright chance that this shift could occur sooner than before. As more and more advertisers demand added transparency and ROI into the ad money spend, we will see broadcasters investing in cross-media measurement platforms to measure the effectiveness of campaigns. At the same time to make the whole system to be extensive in terms of a win-win for advertisers, agencies, and broadcasters, a more collaborative effort is required to bring in a more consistent and stable cross-media measurement system. The concept of TV broadcasting is changing, and so will the way one view and interacts with content and therefore, how ads will be viewed and consumed. It is more like a Rubik’s cube, and we need to get everything correct for a better viewing experience in the future. Conclusion In addition to analyzing data, there’s still more work to be done on the researcher’s side. As more content goes online, advertisers, agencies, and broadcasters will need to know the importance and significance of cross-media measurement. These stakeholders will need to come together to build a cross-media measurement model that can span the various marketing ecosystems to measure campaign performance effectively. Integrating data across sources is just one part of the whole ecosystem, how the cross-media measurement model will evolve will ultimately define how multi-channel campaigns can extend their reach and effectiveness.

How can MuleSoft Address Integration and Digitalization Challenges?

We are living in a “connected everything” world, where users look for integrated experiences and workflows across networks, devices, mediums, and touchpoints. Thus, applications and software have to provide an integrated view of the users’ world to stay relevant. It means that the ability to integrate data, services, and events across systems, whether on-premises or cloud, has become a critical need. Enter, the “middleware platform” in the form of enterprise service bus (ESB) and API gateway. Some of these platforms are also provided as iPaaS (Integration Platform as a Service) on the cloud. Multiple such integration platforms are vying for the top spot, and MuleSoft has been one of the rising stars. MuleSoft has recently been named a Magic Quadrant Leader by Gartner [1], and is recognized as one of the thought leaders in the API-led connectivity domain. Let us look at some of the critical areas that current middleware and iPaaS solutions need to cover, and see how MuleSoft addresses them: Cloud Today’s solutions need to be cloud-ready and architected-for-the-cloud, and MuleSoft can claim to be both. MuleSoft is designed from ground-up to be able to connect services across cloud-to-cloud and on-premises-to-cloud situations. MuleSoft’s “Anytime Runtime Fabric”, users can reap the benefits of Kubernetes and Docker to deploy to AWS, Azure or on-premises. And now, with “CloudHub” it provides a global, fully managed, multi-tenanted and highly available platform for APIs and integrations, ensuring 99.99% uptime. Reusability MuleSoft has taken a robust stance on reusability by creating a marketplace for pre-built integration assets, including APIs, connectors, templates, and examples. It means that teams need not spend time on designing and building APIs for most of the common services on the web. It also means that a business can build and publish its own reusable assets that can then be used across various divisions, functions, and teams, thus reducing replication, improving governance and standardization and improving efficiency. API Networks The “connected everything” enterprise today needs a complex network of APIs to ensure all its integration needs are met. Not only does this require a middleware fabric on which to build the network, it also means that effective tools for managing, monitoring, and securing such networks become critical. MuleSoft has all these areas covered. The “Anypoint Design Center” provides the ability to design and build the network faster using a semi-visual interface, while the “Anypoint Management Center” provides deep visibility into the health of the network and allows engineers to diagnose issues. “Anypoint Security” provides security at every layer of the network, with in-built compliance for common security standards like ISO 27001, PCI DSS, SOC 2 and GDPR. It also allows for multi-level security and authorization policies providing both broad and granular control. Thus, the MuleSoft “Anypoint” platform has everything needed for a complex API network for a modern inter-connected, digital, globally accessible application. Time to Market As always, any transformational change needs to meet the “Time to Market” test, and the move to an API-driven, integrated system is no different. Many such projects fall by the wayside due to lack of resources to finish within time, so productivity and speed are essential for business success. MuleSoft’s combination of a unified platform, visual tools and workbenches, simplified integration runtime, support for the full development and testing lifecycle, and availability of reusable ensures that integration projects can highly efficient and agile. MuleSoft claims [2] 3X faster delivery and 70% higher productivity for app dev teams, resulting in faster time to market. If you are looking for a market-leading integration solution that meets your business needs, MuleSoft should definitely be on your radar. Their success in the market and their 94% customer satisfaction claim seem to indicate that they are delivering on the tagline – “Connect anything. Change everything”. What Next? Tavant, MuleSoft’s partner and reseller, works closely with MuleSoft to offer solutions that allow enterprises to quickly and efficiently design, build, and manage their APIs, applications, and products. Tavant has a robust MuleSoft COE with core practice areas of financial services, aftermarkets, and media & ad tech. To gain more insights, reach out to us at [email protected]. _____________ [1]  https://www.mulesoft.com/why-mulesoft [2] https://www.mulesoft.com/why-mulesoft

Redefining the Customer Journey in the Digital Age

Are you optimizing your customer’s journey? Customers don’t need to be wowed. They’re super busy. They just desire a streamlined experience, at any given moment with quick and seamless resolution. Needless to say, they are looking for an easy, personalized, connected, and consistent experience. The value of focusing on your customers’ journey can’t be understated. Done right, it helps make your marketing feel more like matchmaking and builds a lasting relationship between your customers and your product. As the old adage goes that if you don’t understand the customer journey and evaluate how, why, when, and where customers are interacting with your brand; you cannot influence them. This holds true with today’s digital customers; ultimately, you will fail miserably to meet their evolving needs. Thanks to digital and social media, the customer’s intolerance to brands that get their engagement strategies wrong is growing significantly. Isolated Conversation- A thing of the Past; the connected customer wants hyper-personalized Digital age customers expect hyper-personalized user experiences when they interact with a particular brand, including high-value communication across multiple channels and devices. Customers are now much more savvy, more agile, more independent, more complex in terms of what they look for to help make their purchasing decisions and their behaviors are increasingly inconsistent and harder to predict – in contrast, the marketer’s traditional approach to defining their target audience, planning and executing campaigns is struggling to keep up. A consumer’s real, personal journey of self-improvement is constant throughout that individual’s life. The need to understand the customer journey through your product and service experience has been widely heralded as a fundamental component of marketing for years. Those companies who fail to unravel the mystery behind the customer journey cannot strengthen their bond with their customers. However, the term “customer journey” is broken. This sounds cliché, but the complexity of customer journeys coupled with multiple broken, disconnected channel partner networks is impeding organizations from delivering personalized customer experiences. In today’s data-rich world, the term ‘customer journey’ is undeniably too high level, too generic, and too prescriptive. It’s incomplete, rarely actionable and therefore it is said to be ‘broken.’ The focus of tomorrow’s marketer, therefore, needs to be less linear, segment focused, and personal. Delivering a seamless experience at every phase of the customer lifecycle can bolster a brand’s relationship with its audience and it requires an ample amount of data and a thorough understanding of the customer journey. However, customer journeys don’t work that way, because customers don’t behave this mechanically. They are human and should be respected as individuals. Taking this account, Offline or CRM data on its own is no longer enough to drive this approach. Despite the high sophistication, the truth is, the ‘real’ customer journey is something far more complex, variable, and volatile than it is challenging to cope up with this effectively. Challenges that businesses face with the customer journey: Extremely difficult to harness the information on customers and audiences from social networks. The growth of online conversations and metrics, which require a robust platform to manage the enormous amount of content. Conversations between business and customers are scattered across various channels. All the data that is available on a customer is somewhere in silos and is not utilized fully. Companies are struggling to find ROI across multiple channels.   To solve these challenges, Salesforce has come up with a powerful platform, ‘Salesforce Marketing Cloud’ to integrate all disparate data. Why your business should Choose Salesforce Marketing Cloud? Salesforce marketing cloud is one of the market leaders in the marketing cloud domain along with other clouds like IBM marketing cloud, Adobe marketing cloud, and Oracle marketing cloud. It is the platform for delivering relevant, personalized journeys across channels and devices. It helps marketers to deliver the right messages at the right time to the right people using the right channel. It provides your organization – journey builder, contact management tools, content management tools, analytics builder, and various channels such as email and mobile. To gain more insights, email us at [email protected] or visit Tavant.com/Salesforce.

Blockchain – An Emerging Trend in Warranty Management

Warranty providers today are still dealing with the three critical challenges of the industry: protecting against fraudulent claims, detecting counterfeit products, and deciding on the status of coverage. Businesses are becoming increasingly complex with more vendors, dispersed manufacturing facilities, new distribution channels, and disruptive business models to make matters worse. It’s small wonder then that the processing and administration costs of warranty keep on rising. A study[1] by IBM found that in the electronics industry, only one-third of the warranty costs go towards repair or replacement of defective goods, with two-thirds being spent on processing and administration. There is a dire need to find better ways of managing warranties to keep costs from rising steadily. Technology has always provided us with solutions to address these problems in the past, so will it come to the rescue again? Well, it looks like a solution may already be around the corner, based on an emerging technology called blockchain, which originally evolved to enable decentralized transactional data sharing across large networks of untrusted participants, and is based on the concept of a distributed public ledger. Such a distributed ledger technology (DLT) could also be used to track-and-trace parts throughout the supply chain with a complete history of events related to the part. This capability would help in addressing all the three key warranty challenges mentioned above. Since the blockchain-based public ledger is accessible everywhere, warranty providers would be able to validate the claim and the warranty status at any point in the warranty management process in order to make quick decisions. Also, using the track-and-trace capability of the ledger, they could readily trace the manufacturer of the item, helping them get the repair or replacement process started immediately. The extensive history of the part available would also make decisions on claims easy for the manufacturer. Once the complete lifecycle of a part is available through a trusted public ledger, it would be possible to see the exact time and place of manufacture, note when the part transited through the warehouses of the distributor or supplier, check when it showed-up in the seller’s inventory, and find out when and to whom it was finally sold. This detailed traceability would make it very easy to detect counterfeits, which would fail to show the expected transition history through the supply chain of authorized manufacturers, distributors, and sellers. Thus, blockchain-based systems would make the whole warranty management workflow much faster, simpler, and fraud-resistant, drastically reducing administrative and processing costs. A welcome side-effect of a transparent, fast, and efficient claim-handling process would be enhanced customer experience. Thus, blockchain technology could ultimately have a direct bearing on customer satisfaction and brand health while cutting down costs and improving the provider’s bottom-line. Don’t get too excited yet, though; this technology is still at the proof-of-concept stage in most industries with very few production deployments. However, the applicability of the technology to warranty management is pretty certain. It is just a matter of time before solutions using blockchain get proven, and as we have already discussed above, they will not only address the issues of fraudulent claims, counterfeit parts, and unclear warranty status, but will also increase customer satisfaction and reduce costs by making the claim-handling process fast, fair, and fail-safe. Therefore, if you are a warranty provider, get ready as a blockchain-based innovation is coming soon to a warranty solution near you. Want to Explore More? To gain valuable insights into how the latest innovations can help you stay ahead of the market, register for our webinar with a guest speaker from IDC, on September 10, where we discuss the latest innovations transforming warranty management. [1]IBM: Powering warranty reinvention (https://www.ibm.com/downloads/cas/D6QBER28)

How are New Age Technologies Powering Test Automation?

Test automation is not a new testing concept; however, it is still underutilized, with less than 20% of testing being automated. Test automation is among the top testing trends globally. The reason why test automation is still a global trend and will remain so in the next few years to come is because of the constantly changing market dynamics. For instance, companies like Uber and Airbnb have transformed the transportation and hospitality industry, respectively. They are major disruptors that have put their competitors out of business, with the help of technology. As a result of these changing technologies, enterprises need to be on their toes to not only adopt the latest digital technologies but also have the provision for testing these technologies swiftly. This is where new-age technologies such as AI/ML and innovation can help.     Here are some of the ways new technologies and innovation can help in increasing the efficiency of test automation: Artificial Intelligence as a catalyst: Testing in today’s world is about efficiency in terms of reliability and time. Artificial intelligence assists test automation by making testing more efficient. AI/ML can help in quickly identifying the defects by going through the test files at a greater speed than a human can do. Moreover, AI/ML can be designed to create algorithms that can help in generating better test cases, scripts, and data. Additionally, predictive models can be developed using AI/ML that can provide insights on what, where, and when to test. AI, therefore, has tremendous potential to drive the next level test automation that would be much more efficient than its predecessors. IPs and Platforms: Automation platforms are a big help for testers. Typically, an automation test platform provides a quick turnaround and comprehensive automated test coverage with limited manual intervention, thus helping in saving time and performing tests 24/7. It also helps in overcoming the failures of manual testing, which in turn improves ROI. Having said that, one tool cannot fulfill all test automation purposes. Test automation platforms must be selected after thorough requirement analysis and the ease of use by the available testers in the business. Cloud technology driving test automation: Cloud technology has been one of the biggest technology transformation agents recently. This is applicable in test automation too. Testing in cloud is not only cost-effective but also scalable – cloud has the option to increase or decrease the required configurations as and when required. Additionally, testing in cloud provides support for all the available platforms, devices, and browsers and enables continuous delivery by allowing running automated test scripts immediately after code changes. Conclusion: Test automation has been in use for some time. However, the importance of test automation persists, and with the growing challenge of delivering quality products in the required timeframe, automation in testing remains valuable. The need of the hour is to leverage the latest digital technologies in delivering test automation that can cater to the above challenge and help businesses stay ahead of the curve. Tavant’s expertise in leveraging AI/ML, cloud, and data analytics, along with its test automation platform, has helped companies achieve significant improvement in their ROI. To understand how we can help, mail us at [email protected]. Sources: https://www.softwaretestinghelp.com/software-testing-trends/  https://www.forbes.com/sites/forbestechcouncil/2019/01/07/five-trends-that-will-shape-testing-in-2019/#197862e262a8

Leveraging Data Science for OTT Content Personalization

Why is content personalization important? OTT (Over the Top) platforms are transforming the global entertainment scene. The critical players, like Hulu, Netflix, and Disney, are competing in terms of viewership and revenues. With the increasing overlap of content across all these platforms, it is crucial for these services to improve the consumer experience by delivering relevant and engaging content to prevent audience churn. Content personalization is, therefore, vital to acquire more viewing time and improve market share. How do OTT platforms personalize the content? More than 80% of the content streamed is influenced by its AI-based recommendation system. It is important to understand the crucial things that these platforms consider while designing such a recommendation system. -Focus on giving the user only what they want using personalized content ranker. This ranking is influenced by the user’s activity and interaction with the brand. It makes the content experience unique for every user by making the content delivered to the user influenced by their activity -Rank the top and trending content not only based on the content popularity but also based on the personal information available about the user. The critical thing here is while people are interested in the content popular, they still want to be influenced by their interests -Sort recently viewed content based on whether the users are expected to continue watching or dump the content due to not finding it interesting. It is quite tempting to keep promoting the same content since you have invested in it. However, it’s better to relegate the content and offer something more interesting if the user activity indicates a lack of interest -Recommend content to the user like what they have just watched. People likely to consume content akin to the one they just consumed How is big data used to enhance the user experience? OTT platforms have a vast user base of over 500 million subscribers that gives it a massive advantage. Content personalization begins with gathering subscriber data and then focuses on the metrics such as: -nature of content watched -ratings -the device used to watch the content -searches made on the platform -user location data -content that got re-watched and paused -metadata from third parties like Nielson -social media data from Instagram, Twitter, Facebook, etc. Once the data has been gathered, the platform uses this data to build an AI-based recommendation system to determine why customer eyeballs tune out. The more the granular data is available, the more it can be adjusted to make the content relevant to viewing preferences. Over time, it is important to reformulate the recommendation problem to optimize the probability a member chooses to watch a title and enjoys enough to come back to the service. The more the data available, the better would be the result. It is important to have optimized approaches, appropriate metrics, and rapid experimentation to enable better result. The Road Ahead: The OTT industry is rapidly evolving. Most of the platforms have started to also look at integrating machine learning approaches with the data-driven A/B testing to combine the best of both worlds. It is of paramount importance to perform offline testing to optimize the algorithms before performing online A/B testing.  The abundance of source data, measurements, and associated experiments help to innovate effectively. OTT platforms have only scratched the surface in this area and are left with virtually infinite space to continue excelling at innovating personalization.

Driving Transformation Beyond Digital

Digital is no longer a differentiator. It’s just table stakes. The word ‘Digital’ is almost clichéd now. Everyone has heard about it, and they have all jumped on the bandwagon. If you are an established bank or a lender, you must have already incorporated digital into your long-term strategy, and you are most probably thinking: “Oh, you mean that ‘everything-online,’ ‘integrated customer experience’ and ‘connected enterprise’ thing? Yeah, we have a plan for that.” Unfortunately, the brash new startups that are entering the lending scene are not thinking that way. For them, online and connected are already a given, the bare necessities. For them, ‘Digital’ is a means to ‘Disruption.’ You have surely come across some of the more famous ones among these upstarts, right?Have you heard of ‘Kabbage,’ the lending platform that offers nearly instant loans to small businesses, based on creative, alternative data – like the number of UPS packages sent or received by the industry?  Or ‘Tala,’ which approves microloans for borrowers from underserved economies who lack credit history by crunching out myriad data points ranging from financial transactions to mobile games played? And what about the aptly named ‘Upstart,’ which uses data such as education, employment history, and whether applicants know their credit score to underwrite and price loans? Upstart’s algorithms are supposedly so well-trained that they now approve 47% of loans with zero human intervention and yet manage to have one of the lowest default rates in the industry! Get digital already. There is no time to lose! Going digital is no longer the endgame. It just places you at the start-line for the sprint towards innovation and disruption. Therefore, if you have a long-term, multi-year digital roadmap, you have lost the race even before you have started. You need to go digital right now—within weeks—so that you can compete on level terms and give yourself a chance to race with (and fend-off) these new-age disruptors. If you are a bank or a lender with HELOC offerings, Tavant VΞLOX product suite can help you do just that. It offers a ready-to-use toolbox of services, integrations, and interfaces that propel you instantly to a fully digital-ready enterprise within weeks. Do not waste your time and energy on figuring out how to get digital. Tavant has that covered, which means you can focus your precious resources on figuring out how you will unleash the power of digital for innovation, disruption, and market leadership. FAQs – Tavant Solutions How does Tavant drive transformation beyond basic digital initiatives?Tavant drives comprehensive transformation through cultural change management, advanced analytics implementation, ecosystem integration, and innovation frameworks that extend beyond simple digitization. Their approach includes organizational redesign, new business model enablement, and strategic technology adoption that fundamentally changes how lenders operate. What transformation capabilities does Tavant offer beyond digital technology?Tavant provides change management consulting, business process reengineering, cultural transformation support, and strategic planning services. Their comprehensive approach addresses people, processes, and technology to achieve sustainable transformation that improves business outcomes beyond technology implementation. What does transformation beyond digital mean?Transformation beyond digital means comprehensive organizational change that includes cultural shifts, new business models, reimagined processes, and strategic innovations that go beyond implementing digital tools. It involves fundamental changes in how organizations operate, compete, and create value. Why do digital transformations often fail?Digital transformations often fail due to insufficient change management, lack of clear strategy, resistance to cultural change, inadequate leadership support, poor communication, and focus on technology without addressing underlying business process and organizational issues. How can organizations achieve successful transformation?Successful transformation requires clear vision and strategy, strong leadership commitment, comprehensive change management, employee engagement, cultural alignment, continuous communication, and holistic approaches that address technology, processes, and people simultaneously.

Personalizing CX with Intelligent Marketing Solution

Marketing is witnessing an unprecedented transformation in the recent years as customer expectations are continually changing due to the proliferation of smart devices, ubiquitous presence of technology, the emergence of the digital native, 24/7 connectivity, and the deep penetration of social media. They dictate what they want, how they want, and where they want. They have become both creators as well as critics demanding a more personalized service. These factors are causing traditional brand and marketing strategies to lose effect where marketers lump audiences into broad groups, typically based on attributes such as location or industry. Because marketers don’t know enough about each person or even if they do, it’s too labor-intensive to engage people specifically with the customized message, content, or offer. It is good news for organizations as they have the chance to discover new ways to engage and think beyond channels and consider how customers are engaging with things like connected homes, connected products, and artificial intelligence-driven interfaces. A new level of personalization and precision, brought about by intelligent machines using data more smartly, applies to marketing as well. An AI-enabled marketer can reach out to every customer at the right time, identifies the best audience for every campaign, and delivers the perfect content for every customer. Today’s organizations have a wealth of data and insight at their disposal — but still, they are unable to translate that into intelligent customer and prospect interactions. Embrace the intelligent marketing solution ‘Salesforce Marketing Cloud™’ to enhance CX  Salesforce Marketing Cloud helps you know your customer, personalize it with intelligence, and engage at every touchpoint. It offers you a 360-degree view of your customers, by giving you a personalized experience and flexible service, customized to your business. It helps you chalk out the interactions with your customers, carve predictive journeys; subsequently, cultivates customers into deeply engaged users while giving your marketers the right insights. Let’s delve deeper into how Salesforce Marketing Cloud can help you create a consistent and smooth customer experience Engage current customers — and find new ones Capture data from any source or device. Unify customer data no matter where it was collected. Use it to segment and activate audiences to deliver better advertising, content, and commerce experiences. Reporting and tracking at every step of your customer journey is made possible by communicating with them in a consistent brand voice via email, mobile, social media, targeted ads, web, predictive intelligence, and customer data platform. It forges a much stronger, significant relationship with your customers as you rely on a robust intelligence mechanism, which helps you connect the dots across all your customer touch points. Discover new audiences with AI Use Salesforce’s AI engine, Einstein, to identify new high-value segments. Understand your entire existing customer and new prospects base across clusters of personas and devices. Target cross-channel experiences on any device Drive more relevant and valuable customer engagement — and business results for consistent customer experience across channels. Capture, unify, and activate customer data without limits with Data Studio Know your customer through data, personalize every experience with intelligence across the entire customer journey — including marketing, sales, service, and commerce touchpoints. Get the best of both worlds with personalized email marketing Experian reports that not only is personalization proven to lift transaction rates and revenue; but also, personalized promotional mailings have a 29% higher unique open rates and 41% higher unique click rate. Easily send and organize emails based on data and insights with personalized email marketing and fuel your customer journey by developing personalized, relationships with them. Deliver better CX with Contact Builder Put together customer data from multiple sources for having a better grasp of their behavior and other attributes and deliver exceptional brand experiences across channels through a unique digital marketing platform. Optimize every journey with Journey Builder Uncover the optimal sequence of events to optimize every journey and help marketers customize their interactions with customers according to their real-time behaviors. Map, execute and track social media campaigns with Social Studio Monitor audience discussions while leveraging machine-learning sentiment analysis and image recognition to extract meaningful insights. Create compelling content with Content Builder Create engaging and smart content via mobile-optimized templates and drag-and-drop functionality and host the images in the content builder for best performance. Import the content that you need rather than importing everything together. Wrapping up The age of intelligent marketing is here, and you need an experienced Salesforce partner like Tavant. Drive your marketing across every interaction with the world’s number one marketing platform. Collect and analyze data to understand the customer. Create personalized experiences at every interaction with AI. Engage across the entire customer journey and deliver experiences that each person will love. Consequently, you can align your marketing strategy to your business objectives and drive customer loyalty, which ultimately leads to business growth. Wish to explore further? Reach out to us at [email protected] in case you wish to gain more insights into Salesforce Marketing Cloud.

Can AI and ML Impede the Next Financial Crisis?

A decade after the subprime crisis, we find ourselves in quite a different mortgage environment. The fundamental changes in the business models, expansion of mortgage portfolios, and rise in the magnitude of intricate warehouse lines have led mortgage lenders and servicers to adopt technology to manage diverse financial and operational challenges including multi-channel document collection, document ingestion, data extraction, and unstructured data consolidation. The scale and complexity of the lending industry are more significant than ever before. The complexity posed due to the multitude of stakeholders in the mortgage lifecycle combined with a highly competitive landscape has led to the birth and adoption of data-driven approaches to ensure business continuity and ways to manage and mitigate the financial impact from the next big ordeal. A paradigm shift in the lending industry Mortgages and their associated document processes are complex by nature. Furthermore, the changing paradigm of loan processing times and organizations’ global presence with large teams scattered across have eventually led to complicated business models and differentiation between business units, processes, functions, which in turn has bolstered the scope, and scale of the ever-evolving mortgage industry. Most organizations in the financial sector are evaluating cutting edge technological developments such as AI and ML to automate the ingestion and analysis of unstructured data in their business models and deliver exceptional customer service while fearing the next financial meltdown. AI is all set to rethink the future in a changing paradigm Recent reports have also shown a drastic surge in financial organizations moving towards AI and ML methodologies to automate a wide array of functions within the mortgage lifecycle to ensure early assessment and mitigation of credit and geopolitical risks and improve operational effectiveness. According to Fannie Mae, 63% of lenders are familiar with some form of AI or ML technology. From self-driving cars to maximizing agricultural yield, the benefits derived from AI and ML are enormous and play a vital role in the mortgage industry. Whether it will help us prevent the next big financial crisis, time will tell. Reach out to us at [email protected] to learn more on Tavant VΞLOX, the industry’s leading Artificial Intelligence-powered digital lending platform. Your opinion matters! Take a few minutes to fill out our survey.

Unlocking the Secrets of Digital Transformation with Salesforce

Today’s consumers expect hyper-personalized and seamless user experiences when they interact with brands that include high-value communication across multiple channels and devices. Delivering that at every phase of the consumer lifecycle can undeniably strengthen a brand’s relationship with its audience; however, it requires data insights and a deep understanding of the customer journey. Companies of all sizes must consider deploying Salesforce to automate and manage their marketing, sales, and customer service functions, and to drive digital transformation and innovation. By integrating Salesforce with other leading digital technology platforms (i.e., AI/cognitive computing, IoT, mobile, live video, image recognition, etc.) business can transform the way they engage, retain, and grow their customer base. Why is Salesforce a critical component for driving a successful digital transformation strategy? 3.    It gives the ultimate customer experience: CRM + UX + CX Amidst the fourth industrial revolution, many businesses have joined the race to deliver connected customer experiences. However, providing these experiences requires more than just providing products and services on time. It requires creating organic connections with real people at every touchpoint of their journey The element of customer experience will soon exceed price and product as the key influence on customers’ purchasing decisions, and 86% of buyers will be happy to pay significantly more for better customer experience, according to Walker’s Customers 2020: A Progress Report.  This is in line with Gartner’s prediction that by 2022, two-thirds of all customer experience projects will make use of IT, up from 50% in 2018. Customers don’t just seek products or services in and of themselves; they also at the same time demand more convenient processes throughout the entire engagement lifecycle. It is vital for organizations to live up to these demands and offer customers the ultimate experience. These stats clearly indicate that an organization’s customer relationship management (CRM) software should provide a richer, faster, and more efficient user experience, and help bring new, innovative applications to the market for a more significant competitive advantage. Salesforce platform helps companies achieve the necessary customer experience excellence by delivering a modern user experience that bridges the gap between customers and businesses. Specifically, Salesforce Lightning helps companies provide a smarter and faster experience for customers and allow IT and business users to bring new applications to market faster to meet customer demands. 2.    It helps in unleashing the power of data According to IDC, business across the globe will expect 175 zettabytes of data worldwide by 2025. The growth of this data will be the result of the amalgamation of intelligent agents that leverage ML and AI to evaluate the growing amount of data generated by the digital things in our lives. According to Salesforce.com’s latest market studies, enterprise sales professionals spend only about a third of their time interacting with prospects. The rest of their workday mostly comprises of administrative tasks such as gathering lead data, time that the cloud company is working to free up. To make Salesforce recently released new features for its flagship Sales Cloud aimed at helping workers find the information they need to be productive faster. Interestingly, most of the capabilities use Einstein, the company’s artificial intelligence system, under the hood that organizations can leverage to unravel the power of data and improve the pipeline. 1.    It empowers your customer with information to increase loyalty, retention, size and average order size  Technological advancement has enabled and given customers control over the experience of purchasing products and services. Organizations have thus shifted their paradigm from a focus on mere products and services to overall experience during the entire engagement lifecycle. Organizations must improve each touchpoint of their user experience (UX) to successfully adapt to this trend, which coincides with growing user expectations. Being a customer can often be frustrating; system inefficiencies create a gap between delivering the products and ongoing services desired. Because customer loyalty is so important to business success, and because that loyalty is so hard to win, being aware of customers’ experience over time is incredibly important. It is helpful to think of the totality of this experience the customer journey. ` Historically, absent infrastructure and information, together with rapidly scaling business growth have made tracking customer journey quite tricky. With the advent of new technologies such as IoT and cloud services, businesses now have access to this information—if they want it, and if they are willing to take steps to extract it. Making the right decisions about which technologies to implement is going to be the difference that makes the difference. Simplify by putting intelligence into your Salesforce CRM & create exceptional CX with Tavant The future of every CRM software is anchored to the fluid architecture of the overall system, including its flexibility to accommodate rapid changes in the market and deliver on ever-evolving customer expectations. Salesforce powerful capabilities are massive steps in this direction, as the entire framework puts Salesforce in a strong position to conquer the all-important “disrupt or be disrupted” philosophy that has taken hold of the modern-day business environment. Tavant’s Salesforce services modernize CRM applications and processes using automation and cognitive intelligence. The result is an increase in conversions, a surge in sales volumes, and increased customer retention. Tavant has been enabling successful Salesforce implementations and integrations over the years. Reach out to us at [email protected] or visit here to explore our Salesforce offerings.

Is Face Recognition Technology Shaping the Future?

Face recognition is one of the fastest growing technology these days, and there are various companies not only involved in R&D but same time developing numerous applications in different fields. There have been various biometric ways in use to recognize a person’s identity like finger-scan, hand-scan, retina-scan, and face recognition. Face recognition is not an altogether new technology, but artificial intelligence and machine learning techniques are continually making it better. It is the latest way to identify people. Face recognition is rapidly gaining momentum with many business benefits such as enhanced user experience, cost-effective without manual intervention. In some cases, people can be recognized even without his/her knowledge by taking his live photo or video. How does it work? Face recognition uses deep learning algorithms which is an advanced form of machine learning, to compare a digital image to the stored faceprint to verify an individual’s identity. Every human face has approximately 80 nodal points that are nothing but the peaks and valleys that make up the different facial features and help to distinguish individuals. These nodal points are measured for each face and create a numerical code, called a faceprint, representing the face in the database. Some of the features measured by Face Recognition Technology are: The distance between the eyes The width of the nose The shape of the cheekbones The depth of the eye sockets The length of the jawline   In Face recognition application, these measurements are retained in an application database and used as a comparison for any person image needed to be identified. Use Cases: While the use case of face recognition can be endless, here are a few that are in production already and widely being used or being researched most – Human face-based Attendance Management System for any company Implemented in Tavant and being used as a pilot project Identify suspicious person at any public places or restricted places Already deployed at some of the airports/subways in Japan, UAE, and China Face recognition-based check-in for better customer experience and save time Recently Delta airlines rolled out face recognition technology at Detroit Metro Airport Unlocking mobile, PC or any personalized device For example, Apple’s iPhone X includes Face ID technology that allows users to unlock their phones with a faceprint mapped by the phone’s camera. Entertainment Industry For example, the Kinect motion gaming system leverages face recognition to differentiate among players. Targeted Advertisement Smart advertisements in airports are now able to identify the gender, ethnicity, and approximate age of a passer-by and perform targeted advertising according to the person’s demographic. Improved User Experience MasterCard,  Amazon, and Alibaba have rolled out face recognition payment methods  often referred to as selfie pay Technology Adoption Challenges: With every technology adoption, in addition to benefits, there are a couple of challenges as well which needed to be known before implementing any use case: Security: Your facial data can be easily gathered and likely to be stored privately or in the public domain, often without your knowledge and permission. It is possible that hackers could access and steal this data and may misuse it. Privacy: Face recognition technology is being used more widely. That means your facial data could end up available in a lot of places. You probably even would not know who has access to it. Freedoms: Government agencies and even unauthorized entity may track your personal data. It would not be easy to stay anonymous. Safety: Also, face recognition can also be misused and lead to online harassment and stalking. For example, what if someone takes your picture in public and uses face recognition software and finds out exactly who you are. Mistaken identity: Face recognition data can be prone to error, i.e., misidentify or failing to identify, even with very low probability, which can implicate people for crimes they haven’t committed. Limitation: Face recognition application cannot differentiate identical twins. Also, it will not give the desired output in dim light.   The Road Ahead: Face recognition technology is still in an evolving phase and gradually being adopted in various applications ranging from social media to critical government applications. Many top players like Amazon, Google, and IBM have come up with their offerings that can be used by anyone and investing in research to improve the speed and accuracy of the process. Companies must unleash the potential of face recognition technology and its implementation applicability to gain a better competitive advantage in their business.

Lifetime Warranty—a Timeless Opportunity?

Starman and his Tesla Roadster became the first automotive satellite of the sun when they were launched into space, as part of the payload, during SpaceX’s “Falcon Heavy” test flight. Based on the distance Starman has covered, the warranty on his Tesla has expired long ago. According to [i]whereisroadster.com, Starman’s Roadster has exceeded its 36000-mile warranty more than 13,000 times over in the past year. Should Starman have taken a lifetime warranty on his Roadster? Well, it depends, since “lifetime” warranties are most often “limited” warranties, and the definition of “lifetime” varies widely. Definitions range between the true lifetime of the product, the period when the product is owned by the first buyer, the period till the point that the production and sale of the specific version or model are stopped, and other narrower definitions. Marketing gimmick or reality? Longer warranties do seem to have become a competitive weapon in numerous industries. Some companies—Midas being a very well-known example—have actually built their whole brand around this concept, and have been very successful. But do customers really get benefitted? It seems they do. As a popular Warranty magazine[1] puts it: “In June 2009, the Detroit News reported that Rachel Veitch of Orlando FL was still driving her yellow 1964 Mercury Comet Caliente, with 557,000 miles on the odometer and counting. Over the past 45 years, she has taken advantage of numerous lifetime warranties. Veitch is on her seventh Midas muffler, and thank you, gentlemen, for the lifetime warranty,” writes the author of the article, reporter Neal Rubin. “She’s had three sets of Sears shock absorbers, also through a lifetime warranty. And though the number seems high, she claims to have had 16 free batteries, courtesy of J.C. Penney and Firestone.” The complex nature of lifetime warranties Lifetime warranties present challenges in the area of cost and pricing models since it is very difficult to predict events over such long time spans. What makes things even more complex is the fact that different types of products have significantly different failure patterns in the long term. Digital products, for example, have very few moving parts, and therefore have a front-loaded failure pattern. If they do not fail early in their lifetime, they may have a relatively failure-free lifetime. Industrial machinery on the other hand, with lots of moving parts, suffer constant wear and tear, and the rate of failure usually increases over time. Due to these uncertainties, some OEMs are transferring the warranty reserve burden for lifetime warranties to franchisees and partners. Other forms of warranties, like the third party extended warranties, service contracts, and labor warranties, are also being used to transfer the warranty risks away from OEMs while still providing customers the peace of mind that they seek. Another interesting development is the advent of ‘Digital Twins’. The data and intelligence obtained from digital twins may soon allow us to get much better predictive models, making it easier to design long-term warranty offerings. In conclusion, though lifetime warranties can be used as a differentiator for competitive advantage and customer loyalty, they can also often be very complex, due to the ambiguity around the terms of such warranties and the difficulties in arriving at reasonable cost and pricing models. It is wise to ensure that such offerings are backed by in-depth analysis and strategic intent. To gain valuable insights into challenges around developing cost and pricing models for lifetime warranties, and how technology can help you stay ahead of the market, please come and listen to our speakers at the WCM conference 2019.   [i] https://www.whereisroadster.com/

Optimize Ad Targeting by Leveraging Human Emotions

In a world facing information deluge, digital ad delivery is a proven way of maximizing sustainable business traction. However, it can be a tricky business to be in, due to the oversimplification of its usage and value proposition it can offer. Due to the involvement of multiple players (buyers and sellers) with divergent interests for pervasive ad frauds, it is easy to lose control and perspective over the quality of ads. Enhancing the efficacy of digital ads Header bidding has helped publishers gain some control over the type of ads delivered on their pages. Against this backdrop, the need for an ad library which can intuitively intercept, and tailor ad calls made to respective ad servers becomes paramount. This disruption can be an effective way to include new ad call parameters based on the content delivered on the page. It circumvents the overall need to develop messy applications and amplifies latency for ad delivery. New parameters to be determined on the original ad server call can be provided from a separate ’emotion’ database derived from multiple types of content. This database will tap into the emotion or sentiment felt by the user reading an article. These emotions are channelized for targeting ads towards a specific piece of content. E.g., content tagged as ‘adventurous’ can be displayed to a hiking or camping gear maker, and in the event of a catastrophic tragedy, the ad can be structured accordingly or blocked altogether. The key advantage is that all these events can be seamlessly configured into the ad library. It can also be seen that the real value of emotion database is in its ability to demonstrate the dynamic, real-time nature of the content. Final Thoughts Enable data compliance, ensure customer centricity This ingenious method of ad targeting ensures ads are not predicated on consumers’ browsing data. Instead, it will be based on the nature of content delivered and collective user reactions. Besides, it will also extricate delivery from cookies, which was the traditional way to understand user behavior and target end users. The successful synergy of user level metadata with content metadata can be leveraged for precise targeting of ads and to optimize its efficacy. Going by the current trends, the ad tech space is bound to see more innovations at scale, and the future looks exciting and promising.

Top 5 Sales Challenges that Salesforce Can Resolve Effectively

Impeccable customer service isn’t just a way of building customer loyalty; it’s also what sets you apart from your competitors and reflects your digital readiness. Salesforce® has been the game changer in achieving this. Today, customers expect you to deliver the right answer the first time, every time, on whichever channel they choose. Keeping up your customers’ expectation means more than just providing the right answer at the right time. It indicates delivering a personalized experience for every customer and efficiently collaborate internally. Gathering customer’s data at this stage is important to be able to qualify your lead effectively. Businesses must boost their sales process and invest in the right tools and intelligent technology to stay ahead in the digital world. Organizations not well-equipped for this face regular challenges like: No single unified view– One of the main challenges lies in integrating data from various disparate sources and create a single unified view. For example, a customer’s data might be stored in a CRM, or their order history information might be in a custom legacy system, their purchase data might be lying in some POS system such as Shopify during their social media data on Instagram, Facebook, Twitter and the like. Lead qualification is a crucial part of the sales process. Long customer decision time frames-Long sales cycles are a nightmare for all sales staff, which means delayed ROI and uncertainty about winning the deal. If the existing systems can’t provide complete visibility at each stage, the customer acquisition cycle prolongs. Disjointed sales processes- It results from the fact that every sales team member without unified software for streamlining sales management, applies different methods to handle the sales process. Bringing transparency to the sales process- Managing nitty gritty about deals, the accounts they are dealing, and the number of potential sales scattered can be quite a challenge for sales managers. The communication gap between sales and marketing- Sales and marketing being two different worlds existing in parallel with each other is the status quo. The lack of communication between these two important teams eventually leads to their poor performance. According to the [i]E-consulting study, four out of five US customers don’t believe that the common brand understands them as an individual. Personalization depends on your customer and in many cases, that’s where small and large companies fall short. Another report by a major analyst indicates that organizations that deploy CRM strategies such as Salesforce will return at least 25 percent better ROI than those that don’t. The Salesforce solution has the capabilities to enable you to successfully manage your sales process throughout the stages of qualifying to closing and follow-up. Understand your potential on Salesforce A robust 360-degree view helps businesses to reduce costs by providing a single source of clean, integrated customer data. Successful organizations understand the potential of Salesforce goes beyond it and know ways to integrate & enhance their existing system’s capabilities. It undeniably enables organizations to drive better marketing, increase sales growth, understand their customers’ behavior deeply and, most importantly, deliver unique personalized customer experience. In conclusion Investing in reliable automation software such as Salesforce undeniably pays off in the long run while providing employees with the right tools. Applications developed and integrated on Salesforce enable organizations to achieve shorter sales cycles, create efficient proposals, and improve sales and marketing collaboration. Tavant’s FinLeads product does all of this and goes beyond it. It’s a Salesforce-based customer engagement and aggregation platform that acts as one-stop-shop for Sales & Marketing teams to quickly convert your leads to customers. To gain more insights, visit our Salesforce partnership and FinLeads pages or just say [email protected] to schedule a meeting.    [i] https://www.mckinsey.com/business-functions/marketing-and-sales/our-insi…

Re-invent Dealer Experience with AI Platform

The automotive and automotive aftermarket industries are some of the oldest and most established industries. Historically, these industries have faced less disruption than their equally-established counterparts. But aftermarket industry as a whole is drastically affected by several major disruptions, in particular, digitization, shifting competitive dynamics, and changing consumer preferences. And, unlike other sectors, it is changing faster, and the shift has been dramatic. First, new players are beginning to enter the automotive market and established companies have been changing their business models – a trend that is expected to continue in the future. When it comes to consumer preferences, millennials are less interested in car ownership while stricter regulations on emissions are giving rise to electric vehicles. Additionally, with the sudden expansion of next-gen technologies such as AI, IoT, cloud computing, and human-machine interfaces, the automotive aftermarket is facing a wide range of challenges. Some challenges faced by enterprises today in the aftermarket industry include: Aftermarket processes suffer from high latency and lagged response due to legacy and disjointed systems, Lack of customer analytics across channels Increasing regulatory, quality and environmental compliance needs Long cycle time for ‘detection to correction’ in case of issues to be resolved Revenue leakage to spurious spare parts in the market Lack of feedback system for gauging the effectiveness of change management, warranty management Legacy systems are not enabling the customer to do self-service   Yet, along with these challenges, warranty management remains one of the industry’s most important and imperative issues. Auto manufacturers and their dealers must leverage an effective warranty management system to win and retain customers. Adopting a few important approaches can help businesses address these challenges, optimize their warranty costs, and enhance their customer experience. Consolidate warranty systems & processes Build extensive validations into the claims entry processes to capture accurate and consistent claims data to manage entitlement verification, pre-warranty authorization, claims verification, and approvals automatically. An efficient and streamlined claims process is important to automate warranty management. Instead of maintaining several systems, centralize all aspects of warranty management including analytics, registration, claims, part returns, and supplier recovery. An integrated system that provides a single view of all information will undeniably cut down duplicate manual efforts and also improve the data consistency. Minimize repeat part returns to reduce warranty cost Companies should only request returns if they need to perform failure analysis or drill down the trends in consumption to proactively identify future problems. For this, it is crucial to automate your supplier claim process to: Decrease the amount of time from failure to claim Minimize the corrective action cycle to avoid continuing to manufacture defective products Reclaim more warranty costs faster from a broader base of suppliers Create a more credible and cleaner supplier claim data Promote supplier collaboration in cut down warranty costs   Improve Warranty, Quality, and Reliability Analysis Gaining good failure data from customers, dealers, and distributors will enable brands to enhance product quality and recover a higher percentage of warranty costs from suppliers. Businesses need to analyze warranty data to identify and address emerging issues and factors contributing to warranty costs. Also, to prevent further warranty failures, organizations need to monitor key warranty metrics such as warranty as a percentage of revenue, cost per unit (CPU) Incorporate warranty management into your analytics and decision support systems Managing a warranty in a reactive mode is no longer adequate in today’s digitalized manufacturing industry, which is under a lot of pressure from evolving customers’ expectations. Companies need to react to customer demand more efficiently, and for this, they need to have proactive warranty management to make an analytics-driven decision in three significant areas, such as: Issue prediction, detection, and warning Warranty and accrual forecasting Service parts demand management and service contract optimization   Based on this data, organizations can anticipate emerging issues and determine potential recall, predict future warranty costs, scrupulously forecast spare parts demand, and subsequently, plan inventory and production accordingly. Build customer experiences from meaningful insights  Businesses must integrate the customer data, store the information in place and keep it integrated for a personalized experience to delight their customers. Get a unified 360-degree view of your data to enrich personalization, segmentation, behavior analysis, and loyalty programs to improve your customer experiences. The Road Ahead: The digital transformation can lead to a significant opportunity for aftermarket businesses to streamline their operations. It can be done by shedding non-value-adding functions and unlocking capital from redundant infrastructure while taking in a broader service portfolio that contributes to better margins. The task of optimizing controls on warranty spend is daunting. The needs of the dealer as well as customer experience, both are of paramount importance at every stage.  However, leveraging an intelligent aftermarket platform, organizations can realize a significant reduction in warranty costs, increase operational efficiency while improving product quality and customer satisfaction. Reshape business with AI Our customized warranty solution with its artificial intelligence and machine learning capabilities can help you increase aftermarket revenues, calculate accurate warranty pricing – as well as manage claims and warranty reserves. Tavant Warranty On-Demand is an AI-powered enterprise warranty platform offered on the Salesforce cloud. The on-demand platform offers end-to-end warranty lifecycle management and is the only solution of its kind on the force.com platform. It provides cross-functional integrations with legacy and ERP systems for data consistency and integrity and enables organizations to reduce warranty costs, increase supplier recovery, and improve aftermarket efficiency. Want to Explore More?  To gain better insights and to learn how to optimize your warranty cost mail us at [email protected].

How to Create a Targeted Ad Experience with Addressable Advertising?

The Changing Television Landscape Technology now exists to seamlessly integrate identified audiences (who have voluntarily declared intent to known data sources) with the content that these audiences consume. However, there are many challenges to be overcome both on the data as well the business side. The data available on households which consume TV (whether linear or through streaming options) is patchy at best. There are different techniques which data marketers deploy to make the data closer to reality. They deploy probabilistic and stochastic methods to project data audiences. However, with these techniques and such fragmented, time-shifted audiences, advertisers do not always reach the right viewers with the right message. Addressable Advertising is the Need of the Hour! One of the ‘buzzwords’ in recent days in the media world has been addressable advertising. Addressable advertising is a name given to personalized advertising or messaging sent to an identifiable audience segment referred to as addressable. Addressable advertising allows marketers to reach more specific audiences with greater creative flexibility, deep insights, and reliable ROI data. Moreover, with much more granular TV attribution and measurement, advertisers can understand the real performance of their ad – including engagement, brand lift, and conversions. The addressable segment is identified by specific characteristics that could be demographic or behavior oriented. It is coupled with the ability to deliver campaigns tailored to the desired demographic and behavior intent. Among all advertising’s holy grails, addressable TV is often held out as the holiest of them. The difference now as compared to some two years ago is that the inventory that was made available for addressable is hugely expanding. Earlier, there were only two minutes out of an hour’s programming was available for the MSO’s for addressable targeting. Now with the significant MSO’s owning much of the content as well courtesy their big-ticket acquisitions, they are continuously working to expand their addressable inventories. Not only this, all VOD available on STB or streaming box options is also identified as addressable. Final Thoughts: The business of selling inventory needs to change as well, to offer scale to the buyers as traditional media space selling does. Advertisers need to shift towards buying audiences than being media space. Addressable is an excellent option in this space as it offers exceptional value regarding money spent on buying audiences as almost no leakage takes place. The delivery is 100% to the desired audience as long as the data is correct. The hit rate in traditional advertising is supposed to be not more than 10%. This means that there is ten times more leeway that the broadcaster or owner of the inventory gets regarding addressable. Addressable advertising is the future of TV advertising. It might take a while more to become completely addressable, but the road is becoming more evident every day. How Tavant helps organizations deliver intelligent interactions with their customers? Tavant has been very actively involved in media measurement technologies for more than 12 years. Tavant has been working actively on addressable TV advertising with its clients who are in the data and broadcasting network space. Tavant also has a solution accelerator around media planning, execution, and measurement, which enables buying of the media space for the addressable audience. Ready to start talking one to one? Connect with us today at [email protected] to strategize your next addressable TV campaign.

Want to Accelerate your Sales Funnel? Ignite the Core!

Long lead conversion cycles are the biggest impediments to the sales and marketing professionals in the fast-paced technology-driven financial services industry. In the digital age, your customers are more capable, smarter and better informed than ever before. This modern breed of consumer needs a superior kind of marketing engagements and quick sales cycles. According to a research done by [i]Harvard University, over the past five years, the average sales cycle length has gone up by 22% due to more decision makers being involved in the buying process coupled with sluggish economic growth. Furthermore, over 25% of sales cycles take approximately seven months or sometimes longer to close. That’s why it’s important for salespeople and marketers to start thinking about the health of their sales cycle. However, an efficient lead management system can undeniably improve sales and marketing alignment and positively impacts the pace of the sales cycle. Is Your Sales Funnel Management Platform Really Customer-Centric? There are all the twists and turns that the average customer takes on his or her journey with your brand—various touchpoints including social marketing, call center, mobile apps, traditional advertising, and website interactions. While introspecting the current system, ask these questions: Do you have an efficient funnel management system? Does it help you in shortening the sales cycle and sell better? Does it provide a consolidated 360° view of operations and customer transactions? Is your sales process in sync with all other customer messaging across channels such as email, mobile, social, etc.? Top 3 critical Customer Engagement & Acquisition challenges to focus: Disparate Data – The customer information is often siloed in multiple systems, including data within the organization as well as from second and third parties. It may include e-mail messages, interactions from social forums, data from weblogs, and much more, making it extremely difficult to systematically collect, centralize, and share with other groups internally. Lack of control– Organizations often lack a practical means to analyze large amounts of structured and unstructured data resulting from customer interactions to surface customer sentiments, rising trends, competitive advantages/disadvantages, and other information for timely decision making. There is a scarcity of data scientists who could wrangle that data for you and already engaged with other requests from across the company. And the analytics tools that you have at your fingertips don’t have the comprehensive and up-to-date understanding of your customers that you need. That makes it too difficult to engage and acquire right people at the right time. Inconsistent customer experience– The rise of technology, the empowered customer, and legacy strategies and solutions have caused the customer experience to become fragmented and disparate. And we should face it that there are many fragile and often broken moments in the customer journey. Additionally, the change in the channel typically adds complexity and threatens to derail purchases and sometimes even hurts loyalty. How to Address Such Challenges? A single, unified view of all the disparate customer data can streamline your sales and marketing process and help you to build relationships throughout the length of your sales cycle with lead nurturing and automated follow-up. Needless to say, companies must apply advanced analytics to gain a 360-degree view of their customers and engage with them in the early stages of the cycle. They must act fast to streamline their prospect funnel management and have a complete integrated view that could help them to efficiently navigate while maintaining a strategic focus on maximizing sales performance. The Final Thoughts As the old adage goes, a leaky ship sinks an empire. It holds true in today’s competitive market as having a leaking sales funnel can be depicted as the difference between putting money in your pocket or handing it straight to your competitor. So, if you’re committing these blunders, it’s time to fill the gap now. Consider making use of the right blend of data strategy and technology to acquire, convert, grow, retain, and win back customers in one seamless omnichannel journey. Identify the “gray areas” in your processes and how they are they affecting your business. Dig deeply into the customer list, re-tune it through an easy filtering step to pair tactics to the specific customer cluster and ensure to bring at-risk customers back into the fold. How Tavant helped New Penn Financial to bridge the gap between legacy and digital Recently, FinLeads was selected by New Penn Financial to transform its digital lead engagement processes together. FinLeads helped their sales and marketing teams and loan officers to manage sales funnels more effectively. It also elevated their business performance with real-time analytics, enable a faster loan process, and digitally enhance their overall customer experience. Want to explore more: Tavant is committed to helping clients deliver next-gen customer experience. To gain more insights, visit our FinLeads page or just say [email protected] to schedule a meeting.

Empower your Aftermarket Business with Intelligent Decisions

The Changing Aftermarket Industry According to a global strategic business [i]report, the global automotive aftermarket industry is expected to reach $722.8 billion by 2020.  This rising demand for aftermarket parts and services is driving new growth and revenue opportunities for automotive aftermarket organizations. Moreover, digital transformation is re-imagining the automotive industry. Platform-based innovation and hyper-connectivity are shaping the new world of the automobile. Interestingly, aftermarket, the secondary market of the automotive industry is also experiencing this paradigm shift from traditional legacy systems to the digitalized world powered by AI, Machine Learning, IoT, Big Data, Analytics and Mobility. Rising Customer Expectations There is a significant change in the customer buying behavior which has acted as a catalyst in the progress of automotive aftermarket. Interestingly, today’s consumers are keeping their vehicles longer and are more aware of the importance of preventive maintenance and scheduled servicing to maximize the lifetime value of their vehicles. Furthermore, in today’s modern parts marketplace, the millennial customers have become more sophisticated and mobile-oriented while staying connected with their local automobile retailer. They are more in control of the buying process than ever before – with the ability to price, source, and obtain products and parts from a wide variety of sources, including spurious parts suppliers who don’t have the same overheads as the OEM. Needless to say, the consumers are now expecting a seamless experience spanning via omnichannel including physical retail supply shops, apps, websites and so on. Data, Data Everywhere The exponential growth of connectivity and data in manufacturing is drifting aftermarket services towards a new era. The next generation of tools and processes is equipped with next-gen technologies that enable unprecedented collection and transmission of data, which can be exploited to improve aftermarket operations. However, the aftermarket value chain is still highly segmented in disparate data silos. Each player focuses on its perimeter, where it exercises a strategic control thanks to its assets (parts IP, integrated offering, global network, and so on). Business models are still primitive and rely on service contracts (diagnosis, repair, parts, and maintenance) using a transactional mode (cost per operation). Artificial Intelligence & Machine Learning to Rescue Leveraging IoT technologies and platforms built into devices have increased the potential for new revenue streams through innovative data sharing/insight opportunities. And the good news is the large volumes of data generated by IoT devices can now be understood, acted upon and monetized with the help of AI and ML. Organizations can consider integrating IoT data with the existing warranty data to obtain new insights into their customers, products, and operations. In turn, this can lead to optimized product service, enhanced support processes, and the provision of new and differentiating customer experiences, all of which can help in driving revenue. Eventually, improved warranty performance has a direct impact on the customer experience; for example, if a consumer feels that a company acknowledges when products fail to meet up with their expectations, they are more likely to stay in the future, building brand loyalty. The time to act is Now If you are looking to implement a world-class warranty solution without investing heavily in infrastructure or the resources required to deploy & maintain the solution at your premises; our on-demand solution is tailor-made for you. By applying AI and machine learning algorithms to massive amounts of customer data, Tavant’s enterprise warranty solution TWOD on the Salesforce Cloud combines its warranty solution expertise with industry best practices to offer end-to-end warranty lifecycle management. It provides enhanced visibility and proactively populates business opportunities for the sales, service, and marketing teams in their CRM. Want to Explore More? To delve deeper, attend our engaging session on ‘Artificial Intelligence, Machine Learning and the world of making smarter, faster and better decisions’ at WCM 19 and learn how to unlock your sales and revenue potential or just say [email protected] to schedule a meeting.

Delivering Value Chain Performance Through AI in Warranty

Long gone are the days when we had to remember dates for our next free service, oil change, tire rotation, wheel alignment, etc., but time has changed now, these days we receive notifications about these things via SMS, calls or e-mails. Do we comprehend what happens, what components come together so that your mobile phone can chime in a juicy offer on your next car service? Let’s just clear the air about Artificial Intelligence (AI) first to completely understand why it is a driving force that every organization wants to do something with it. AI in its crudest form is reacting to scenarios with actions that are meaningful but isn’t that what we humans do! Yes, but when we consider thousands of customers with vehicles bought at various times, with multiple requirements and problems, the number of data points run into billions, and it becomes humanly impossible to make even a tiny impact. This is where AI becomes the pivot to recreate success in all the scenarios and for all the data points. With the vast amount of data, a human would get overwhelmed to act accurately, whereas an AI works better with a large number of data, it can create and evaluate patterns in the data that hold good and learn from them. It can create new actions and repeat old actions to any amount of data points without fatigue. Aftermarket is the perfect place for an AI system, the vast dealer network, millions of vehicles, each with thousands of components that are serialized, and many more non-serialized parts, numerous variants, and configurations. That’s all well and good only if it benefits all the stakeholders in the aftermarket process, customer, manufacturer and the dealer. As a manufacturer, they can benefit at every stage of the product lifecycle; AI can help with product design, product safety, R&D, Material design, and styling. It can speed up the design and production of new products; AI can manage the supply volume better and in a streamlined manner. All these improvements add up to provide an edge over other players in the market in cost and market share. Like all businesses, the dealer will always benefit the most from a returning customer than a new one. The real money is in service, spares, and campaigns on the existing models of vehicles. AI can help with a predictive and preventive plan for thousands of customers and help improve the total earnings of the dealership. Additionally, the credibility that the dealer amasses with these transparent campaigns would be immense and everlasting with the existing customers and new customers as well. Most of the customers know little about servicing the vehicle that they are driving, many would not even know all the components, this lack of knowledge is a curse to the customer. Customers will want to come back to authorized service centers if there is a transparency, constant support system as well as a positive experience with the brand. AI can help with all these scenarios, help in the analysis of the vehicle for faults, DIY repairs, deploying services such as road assistance and service dealers and make driving more convenient. In Conclusion: We are always in search of a better solution, a better way to deal with issues and problems, AI is that tool that can deliver at all the above and many more scenarios and help grow Aftermarket revenues, after all, everything from lead-scoring to car-sharing to vacation-rentals has been disrupted through AI. Customer Convenience is essential for brand survival. Artificial Intelligence and machine learning can increasingly be deployed to manage customer experience across ecosystems. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.  

Transforming Customer Experience with AI

Eighty-five percent of customer relationships will take place without human interaction by 2020 and AI-derived business value will more than triple to $3.9 trillion by 2022, according to Gartner. By 2019, 40 percent of digital transformation initiatives will be supported by some cognitive computing or AI effort as predicted by IDC. Furthermore, Servion has forecasted that AI will be able to power 95 percent of all customer interactions by 2025 and it will do it so effectively that customers will not be able to ‘spot the bot.’ Many organizations are at the center of this digital transformation and are turning to the emerging technologies such as chatbots, intelligent ad targeting, recommendation engines, personalized communications, and image recognition to gain business value, bolster their relationships, differentiate themselves from their competitors, and increase revenues. AI is quickly becoming a mainstream technology in consumer devices and services. In 2018, the business conversation in boardrooms on Twitter, LinkedIn, blogs, print media, and conference keynotes about AI, machine learning, RPA, chatbots, and virtual assistants have reached the new pinnacle. Surprisingly, nearly two-thirds of consumers are already using AI without even realizing it with products such as Alexa, Siri, Cortana, and Watson. Thanks to the adaption of AI into CX, we are witnessing how enterprises are attempting “true” personalization with predictive capabilities in real-time. This indicates better listening to your customers, understanding the context and providing them with a CX. Organizations are embracing AI to enhance the customer experience by: Intelligently augmented self-service technologies Collating data to enable price and feature comparisons Gathering data by smart assistants Using sentiment analysis to track customer emotions and respond accordingly Forecasting customer needs and then responding proactively Gaining more information about the customer based on data patterns Discovering user interaction on websites to determine if they need help Giving recommendations based on the behaviors of similar customers How can AI Enhance CX? Customer Insights Bring Important Findings for Businesses Leveraging AI can help unleash actionable customer insights that can help in driving impactful business decision-making. AI has also transformed how organizations get customer insights. Leveraging the vast amount of data that is available on customers today, AI can keep a track on trends and predict what customers’ need in the future. One of the best examples of this is Spotify, which used data from its more than 100 million customers to create a compelling ad campaign. The Use of Personalization Improves Customer Experience In customer experience, personalization is a significant advantage to AI. Modern customers expect offers to be tailored to their needs—a blast email with some general offer won’t appeal to nearly as many people as a targeted offer that directly addresses what precisely a customer wants. However, creating those personalized experiences is extremely difficult and tedious for humans. AI can quickly sift through millions of pieces of information to figure out exactly what matters to customers to create a personalized experience. Process Automation Increases Business Efficiency Streamlining repetitive tasks is a big change happening across industries. Deploying AI to automate the process efficiently and effectively can save time and increase efficiency. It provides a seamless experience for the customer by having a near 0% error rate. Additionally, it becomes easier for service representative relaying information and responding to the customer’s needs. This efficiency enables them to take care of more people in a much shorter amount of time. By automating processes and allowing for real-time data integration, communications are significantly improved. Looking ahead AI is undeniably a powerhouse when it comes to customer experience. This technology will not only allow organizations to create faster, more personalized experiences but will also help in gaining customer insights to deliver better customer experience in the future. Companies must unleash the potential of AI and act now to reap its possible rewards to gain a better competitive advantage in their business. Reach out to us at [email protected] to know how we can enable your business garner customer loyalty, improve experiences, and help you stay relevant in the business. 

How to Leverage Your Field Service Automation

From a system perspective, Field Service Management (FSM) is a combination of inventory management, vehicle tracking, scheduling, customer portals, and more. All these components work together to achieve optimized results in the field and seamless communication between the back-office personnel and the field technicians on the job. The cloud portal is accessed from the mobile devices for better flexibility. While most of the adopters are using field service automation just to automate a once manual process, other field service organizations have reached ‘field-service maturity’ already. The latter organizations are unleashing the transformation of the customer-centric cost centers into profit-making centers. These organizations have already achieved efficiency by automating their scheduling, dispatch, and routing by successfully leveraging FSM. Enhance Productivity of Both the Managers and Technicians Advanced algorithm task allocation and flexible access to real-time data on any devices save a plenty of time and energy for both managers and technicians. This advancement makes them available to perform the core job more efficiently and flawlessly. From the back office, managers can supervise technicians’ jobs easily through real-time analytics and live video feeds. FSM Helps Perform Numerous Tasks: 1. Create work orders from cases 2. Manage and monitor technicians 3. Scheduling and order management 4. Vehicle/technician location tracking 5. Job status updates 6. Route optimization and GPS navigation 7. Time tracking and driver logs 8. Knowledge and asset repositories 9. Parts and inventory management 10. Integrated invoicing/payment processing 11. Customer portals 12. Regulatory compliance measures   Anticipating Customers’ Needs – Making Customers Loyal: Predicting customers’ needs has become much more effective by analyzing patterns of existing customers’ service records, buying habits, and knowledge gathered during past field services. Inventing and offering products and services with the help of the analysis, even before the customers realize those needs, will bring in higher loyalty across the customer base. Upsell, Cross-Sell – More Sales FSM can empower the field technicians to become field salesmen. Enabling these technicians to offer additional products and services on the job, renew contracts and collect signatures will result in saving massive sales and marketing efforts and expenditures, as well as boost sales and increase customer loyalty. Information, Insights, Improvements Field services that include maintenance or break-fix operations are an excellent source for collecting relevant information about commonly occurring errors. This data can later be utilized to improve the products and services in-house and adds supportive insights for the other field technicians. Are we seeing an opportunity for an internal social network for the field technicians to share inputs on the kinds of issues they frequently encounter? IoT Augmentation Internet of Things (IoT) capabilities can bolster FSM efficiency even more. Introduction of IoT can remove unnecessary human interference for monitoring the emergency or maintenance needs of equipment. It is also useful in regularly collecting data on various parameters so the data can be analyzed to improve the products or services. FSM’s scope is wide open to increase profit and improve efficiency in the coming future. Companies that are still in the ‘thinking mode’ to make a strategic move into FSM, will fall back in the race. The global field service management market is estimated to reach $3.52 billion by 2019. That’s a compound annual growth of 17.3%. North America is expected to become the largest market in terms of the market size. Europe and Asia-Pacific are expected to experience an increase in market traction. Customers are expecting FSM as a standard feature for their service. According to market research, 89% of customers prefer modern technology, like the kind used in on-demand cab-booking services, applied to their technician scheduling, and nearly as many would be willing to pay a premium for it. How’s your organization pulling in the race? Let us know with your comments below. Sources: 1. https://www.salesforce.com/hub/service/what-is-field-service-management/ 2. http://www.oracle.com/us/field-service-cloud-ebook-3033411.pdf Meet Tavant at the Automotive Warranty Management conference. We are a Gold Sponsor at the event and Atul Varshneya, VP Artificial Intelligence, Tavant will be delivering a keynote on ‘Intelligent decision making using Artificial intelligence.’ http://bit.ly/2npFJWM

Why ‘Repeat’ Customer is the King

Post-sale services aim to create and reinforce positive brand image in the minds of customers. They are an essential component of the strategy to retain existing customers. Depending on the industry and customer demography, acquiring new customers can be 6-10 times costlier than keeping existing ones. A significant factor influencing the customer’s decision-making process is the ease of access to post-sale services and how promptly the current company has honored the warranties of the purchased product or service. Unreasonably long service time, or prolonged disruptions to the customer business or daily life due to faulty products, leaves a negative impact on the customer. As manufacturers consider post-sale services, the warranty is often viewed as a cost center, resulting in below par post-sale service offerings. Warranty Systems to the Rescue Warranty claims processing systems act as an essential enabler in achieving the post-sale service objective by providing the following benefits. 1.    Self Service: By leveraging customer warranty portals, the end customer can clearly determine the current warranty status of the product and the extent of coverage, thereby enabling the customer to approach the correct post-sale service provider for resolution, saving both time and money. 2.    Hassle-Free Warranty Claims: Simplicity in submitting and processing a claim, maximum automation in claims processing by using a business rules engine, and auto-detection of coverage and claims eligibility vastly reduce claim processing time, enabling service providers to provide post-sale services to the end customer efficiently and within a set time frame. 3.    No Ambiguity in Claims Processing: As the claim processing is driven by an automated set of business rules, clear and non-ambiguous reasons are provided when a claim is rejected, thereby reducing the chances to create a negative brand image in the mind of the consumer. 4.    Recall Campaigns: Warranty systems enable efficient handling and implementation of product recall campaigns. This allows customers to get defective products fixed free of cost, before experiencing a failure, which reduces the risk of the customer having a bad experience with the product, resulting in a positive brand image in the mind of the customer. 5.    Maintenance/Service Contracts: Service contracts enforce periodic maintenance and overhaul of the products, thereby extending the product life and performance parameters over an extended period. 6.    360-Degree Visibility: Warranty systems provide complete visibility of the service life-cycle to the customer, enabling the customers to know and predict the exact time frame in which the products will be serviced, allowing them to assess the corresponding business impact accurately. A Happy Customer Is a Repeat Customer Seamless post-sale services, along with minimum disruption in the daily life or business of a customer, add to the positive product experience. An integrated warranty claims management system plays an integral part in re-enforcing a positive brand image in the minds of customers. Considering warranty cost as a necessary expense to retain existing customers will bring a change in manufacturers’ mindset, encouraging them to build a robust post-sale services offering. Tavant at Automotive Warranty Management Conference 2018 Tavant is excited to sponsor the Automotive Warranty Management Conference and showcase our enterprise warranty solution, Tavant Warranty On-Demand, offered on the Salesforce Cloud. To experience precision and quality in aftermarket warranty, connect with our warranty experts today and schedule your PERSONALISED DEMO!    

How Can Salesforce Magic Transform Digital Customer Experience?

Businesses are in a constant struggle to improve agility and reduce costs. Organizations are looking to unlock the power of data for better customer management and great customer experiences to improve efficiency and increase productivity. Companies that are reluctant to put the customer first will surely strive for relevance in an increasingly competitive market. Moreover, they will also be threatened by the growing number of businesses that are leveraging customer experience to drive loyalty and adoption of their products. Needless to say, organizations must reinvent the power of customer experience in the era of the connected customer and need to embrace the customer-obsessed culture and create a single view of their customer. They should be able to understand their customers, resolve their queries, and anticipate their future need and understand the paramount value of following their customer’s point of view. However, for an organization to gather information on its customers in order to facilitate better a working relationship can be a daunting task. Salesforce makes it easier for businesses to sell more and grow. Benefits of CRM A CRM solution helps you focus on your organization’s relationships with individual people — including customers, service users, colleagues, or suppliers — throughout your lifecycle with them, including finding new customers, winning their business, and providing support and additional services throughout the relationship. Here’s how a CRM system can help your business today. Make direct improvement to the bottom line Adding a CRM platform to the business has demonstrated real results – including direct improvements to the bottom line. CRM applications have a proven track record of boosting: Sales by up to 37% Sales Productivity by up to 44% Forecast accuracy by 48%   Recognize and classify leads A CRM system can enable you to identify, add new leads quickly, efficiently and categorize them accurately. By focusing on the right leads, sales teams can formulate the opportunities that will close deals, and marketing can classify leads that require more nurturing and prepare them to become quality leads. Boost high-quality referrals from your existing customers Understand your customers better and drive cross-selling and upselling opportunities and win new business from existing customers. Provide Better Customer Support Customers expect real-time responses and interactions at every level. A CRM system helps you to offer the superior quality service that customers are searching. Improve Product & Services An efficient CRM system gathers information from multiple sources across your business and beyond. It gives you unprecedented insights into how your customers feel and what they are speaking about your organization — so you can revamp what you offer, recognize issues early, and identify gaps. Tavant will be showcasing Tavant Warranty On-Demand and FinLeads at the Dreamforce 18.  FinLeads is the mortgage industry’s first customer engagement and acquisition platform. It drives and automates a streamlined prospect funnel management process that helps engage prospects, educates them, qualifies them, and accelerates their transition from lead to customer. It integrates the omnichannel engagement across digital assets owned, third-party sources, call centers, and field operations. It brings together the best sources of industry data and leverages an intelligent algorithm to inform how the acquisition journey should be personalized for customers and recommends and automates next best engagement actions.  It supports multiple lines of business (Wholesale and Retail). TWOD– Tavant Warranty On-Demand, our enterprise warranty solution offered on the Salesforce Cloud, combines our warranty solution expertise with industry best practices. Tavant has experience of over a decade of working with leading customers to develop and implement enterprise-class warranty solutions. The On-Demand solution offers end-to-end warranty lifecycle management and is the only solution of its kind on Force.com platform. If you are looking to implement a world-class warranty solution without investing heavily in infrastructure or the resources required to deploy & maintain the solution at your own premises; our on-demand solution is tailor-made for you. Want to explore more? Meet our Tavant experts at Dreamforce 18 to learn how to unlock your sales and revenue potential, just say [email protected] to schedule a meeting.

Decoding 5 Key Digital Technologies Reshaping the Agriculture Industry

According to a 2015 report from the McKinsey Global Institute, agriculture is the least digitized industry; far behind healthcare, hospitality, and construction. Conquering agricultural challenges need to break through the weakest link of the food chain by using technology, with digitization as a keystone. In recent years, technology in agriculture, which is also termed as AgTech has drastically changed the agriculture industry. The digital agribusiness is undeniably real, and it’s here to stay. Digital will play a vital role in the agricultural value chain by providing targeted information, data-driven decisions, and recommendations, access to sustainable practices and finance opportunities. How do we do it?  Organizations must adapt to survive and thrive People in the industry—farmers, food producers—must embrace the digital transformation trends in agriculture. By leveraging digital technology as a sustainable and scalable resource, organizations can take agriculture to new heights, keeping farm to fork in our future. The overall food production needs to double in a relatively short duration, to support the growing world population. Digitization in the agribusiness sector significantly increases the ability to feed the rapidly growing world population sustainably. Aware but unsure Research shows 90% of CEOs strongly believe that the digital economy will have a significant impact on the agriculture industry; however less than 15% are funding and executing on the plan. It’s fortunate that digitization is helping to connect agricultural concerns across the globe. But what does the future of farming look like? A few significant AgTech trends that are shaping the agriculture industry currently: Artificial Intelligence and robots Agriculture is slowly becoming digital and AI in agriculture is emerging in three major categories, (i)    Agricultural robotics (ii)    Soil and crop monitoring (iii)    Predictive analytics. AI is bringing a revolution to the agriculture sector. Farmers are using AI technologies for sowing seeds using drones, soil mapping, and commodity pricing. Robots will soon be automating many farming processes and take over tasks such as weeding, fertilizing, seeding, or pruning plants. AI helps bring down the operational costs in farms, by reducing dependence on manual labor and allows agronomic expertise to make data-driven decisions. Use of robotics helps in reducing the use of harmful chemicals and contributes towards eco-friendly practices. Soil and crop monitoring by robotics helps in early identification of pest or disease attack and helps contain the damage and treatment costs. Blockchain Blockchain technology will also be a focus in the coming days. It is possible to have real-time monitoring of supply chain leveraging blockchain, and there will be more transparency in agricultural transactions. It is vital for both farmers as wells as consumers: it allows farmers to negotiate better prices throughout the supply chain while enabling consumers to have confidence in the knowledge of precisely from where the produce they buy comes. It is an essential aspect when considering the growing lack of trust in the sourcing of produce sold in markets. Analytics The agriculture sector is innately complex with a wide variety of crops, geographic environments, and climates. This industry has always been loaded with data but scattered across various channels; however, this is changing, and organizations have started unleashing the power of data and analytics. Organizations are now working with farmers to enable them to use data to better plan seeding, management, and harvesting. By making use of sophisticated computer algorithms to evaluate decades of the crop as well as weather data, these days farmers can easily predict crop yields with surprising accuracy, before planting a single seed. Internet of Things The Internet of Things (IoT) is allowing data-driven intelligent agriculture. Intelligent farming using the Internet of Things will enable farmers to reduce waste and enhance productivity significantly, ranging from the amount of fertilizer utilized to the number of journeys the farm vehicles have made. IoT can help in gathering real-time analytical data and take faster commercial decisions. Sensors Recent estimates indicate that in 2025 the global market value of agricultural sensors will reach 288.3 million dollars – a vast increase from its value in 2016 at 99.3 million. Farmers are increasingly using sensors and soil sampling to gather data, and this data gets stored in the farm management system that allows for better processing and analysis. Using sensors to collect data about crops – water requirements, humidity, soil temperature, etc. – is on the rise. Sensors in the field measure soil and weather conditions such as humidity, temperature, and livestock data, while sensors on farming equipment give real-time insight into yield and quality parameters. Agribusiness leaders are learning how to leverage these technologies to: • Increase farming efficiency • Enhance customer experience • Create transparent and sustainable food supply chains • Implement new, sustainable business models • Manage market and price volatility • Engage with the right partners in business networks Connect businesses to the world of agriculture, and the world of agriculture to your business Digital technologies and analytics are transforming agriculture, making a farm’s field operations more insight-driven and efficient. Digital-based farm services are helping to improve business performance and boost yield. Tavant has combined digital technologies such as the Internet of Things with AI capabilities, analytics and its in-depth industry knowledge to help farmers increase their productivity and profitability. • A global digital agriculture company increased the productivity of growers and turned data into actionable insights leveraging Tavant’s AgriTech solution. • One of America’s premier agribusiness and food companies improved processes, boosted their yield, increased profitability, and enhanced customer experience by using Tavant’s AgriTech solution. Want to learn more? You are just a step away. We would be glad to arrange a meeting with you. E-mail us at [email protected] for more information.  

Customer Experience (CX) – The Secret Sauce of Digital Transformation

The past few years have seen a tumultuous change in the mortgage industry as many servicers struggle to keep pace with stringent regulatory requirements, increasing per-loan servicing costs, operational challenges, fragmented view of the customer and rising consumer expectations. But what if this reaction could be less about just keeping up with the changes and more about a paradigm shift to a focus on what the borrower wants and needs? Recently, when Fannie Mae surveyed mortgage executives, one of the notable points of the survey was the use of next-gen technologies to improve the consumer experience across the loan life-cycle. However, most lenders agreed that there are many significant barriers including cost, implementation, and integration issues that are holding them back. 38% agreed high costs is the biggest challenge 23% said implementation of next-gen technologies is too difficult 20% found integration as a complex issue Two-thirds of lenders have not used next-gen technology vendors at all   So, what are the ingredients of the ‘Secret Sauce’? Well! It’s the Customer Experience, which is a Journey of Expectations Personalization is more important than ever Customers expect personalized services, and it can be difficult for consumer lending organizations to deliver. Not because they do not have the desire, but because of legacy systems and regulations that restrict them to the traditions of the past. These constraints hold them back, even while they recognize that location and products alone are not enough to attract and keep empowered customers. A Good CX Means a Loyal Customer The customer is equipped with loads of information before even making the first contact; they are not that loyal as they look for the best deal and are likely to maintain a relationship with more than one financial institution. A recent report from Deloitte (Reshaping the retail banking experience for the customer of tomorrow) reveals the importance of positive customer experience: 90% of customers trust a recommendation entirely; they are seven times more likely to trust a reference than an advertisement. If a customer encounters poor customer service, he or she may never come back and will advise their friends as well to do the same. Transform Destination into a New Beginning Of course, integration of multiple systems is complex and one of the major challenges faced by lenders. But that should not impact the experience when it comes to quickly processing a loan. Think if your customers get an even better digital experience towards ending of processing cycle, the chances are bright when they are looking for next loan. The opportunity for change: How many loans originated this year? How much do you want to grow loan revenue? How many loans are processing on average? Do you want to raise this number? How long does it take to process a loan? Average wait time and processing cost? Are you expected to reduce these costs? If yes, then by how much?   The answers to these questions can help you refine your vision for the future of your lending activities, and nurture discussions with your solution partner helping to pave the way to measurable improvement. LOOKING AHEAD The digital transformation underway in the mortgage industry is undeniably not a fad. Digital solutions address numerous industry challenges. The technology and process transformation will provide a single view of the customer and personalize the customer experience, spur innovation within services and products offerings, increase compliance and cut down origination costs. Lending companies and mortgage servicers must embrace digital solutions to stay relevant. Migrating from a legacy mortgage model to a digital-solutions-based model will require dedicated organizational alignment. Remember, digital is not just a box to be checked or leveraged only for pointed solutions to specific problems. Are you off late having a train of thought? How to modernize, measure and manage a mission-critical runtime environment and partner ecosystem that is high-performing, robust, efficient and responsive to change? Tavant’s AI-powered digital lending solution can help you: Reduce application processing time Reduce the cost of the overall process Better control over the process and reduced error rates High visibility on loan application status across the organization Increase customer satisfaction and business, and enhance employees’ efficiency   Do you wish to explore further? E-mail us at [email protected] to schedule a meeting. Innovative Lenders have altered their way of doing business to not only roll with the industry changes but also thrive in – and even help drive – the transformation. We will discuss this in our next blog.  

The Magic of Clubbing Customer Experience & Text Analytics

Analytics-driven customer experiences are redefining the Customer Journeys in the Digital 2.0 world now. According to Gartner, “By 2020, with the help of AI, customers will be able to manage 85% of their relationship with the brand without interacting with a human.” Today’s digital-savvy customers live in an omnichannel world and transact with businesses in many ways. When they set out to accomplish a task over time, they expect a seamless hand-off among devices and channels. The entire journey needs to be consistent, contextualized and connected to satisfy these increasingly demanding and fickle customers. Customer experience can drive superior revenue and is critical to growth and competitive differentiation for business. Data insight is one of the primary tools for CX enhancement. An enhanced CX clubbed with an in-depth data is an opportunity window for smooth customer journey. However, the practical challenge for organizations is to integrate all their digital and traditional channels to manage a friction-less experience. It is likely that data is trapped in siloed systems across marketing, sales, commerce, and service. Unlocking the potential of unstructured data hidden in the customer journey If structured data is so big, then unstructured data is enormous.  It is known that organizations exploit only structured data that represents only 20% of the information available.  That suggests that 80% of the data is lying mainly in unstructured form and there is a tremendous potential waiting to be leveraged in the analysis of unstructured data. Unstructured data usually includes comment boxes in feedback forms, is undoubtedly a significant way to gather consumer views on a brand or a service. Unstructured data is highly valuable when merged with structured feedback since it helps in visualizing the consumer’s journey with the brand. Making sense out of unstructured feedback is hugely complicated and organizations that decode this, gain a better grasp of the customer experience.  Moreover, when monitoring customer feedback, the element that brings a couple of benefits is Text Analytics. This Text Analytics can help bridge the gap between customer expectations and the experience provided during entire customer journey. These days customer feedback data are coming from the emerging channels such as social media and mobile devices enabling companies to rely more on text analytics. Organizations that are quicker to identify emerging trends have drastically improved the survey experience with much shorter questionnaires where their questions are getting answered easily and are also realizing the potential of non-verbal expressions like emoticons in conveying customer’s sentiment in feedback. Business Value of Text Analytics Analyzing the overall sentiment of the conversation and ‘what, who, where, when, why’ transforms the unstructured data into structured data and enables organizations to pay attention to all of the conversations. An essential goal of analyzing unstructured data such as customer complaints, opinions or comments is to catch the pulse on what users perceive about an entity. It also helps organizations recognize what do the customers think of the various attributes of a company’s product such as quality, price durability, safety, ease of use. The key to digital transformation lies in combining the Text analytics pieces together with a well-thought customer journey at a strategic level. In conclusion The use of text analytics is burgeoning quickly, and organizations are unleashing the potential that is possible if textual data are analyzed and integrated with decision making. Given the exponential growth of unstructured data both outside and within the organizations, text analytics will continue to expand. Organizations need better insightful text analytics to understand the most relevant drivers to improve the customer experience, ultimately leading to ‘Delightful Customer Journeys’. Text analytics is undeniably actionable if it supports decision making optimally and if the results of the analytics can be shared in a way the business is empowered to act.

How to Increase ROI by Efficiently Capturing Leads From All Sources?

Lead generation is as old as the book, but in recent years digital channels have added a whole new chapter. Industries like consumer lending, automobiles, software, manufacturing and many others have turned to the Internet to generate sales leads. However, according to Econsultancy, only about 22% of businesses are satisfied with their conversion rates. Furthermore, for every $92 spent acquiring customers, only $1 is spent converting them. A key challenge- Capturing and measuring leads from various channels Online leads go cold fast. These are sobering facts. Moreover, capturing and measuring leads from various marketing channels and campaigns is the key challenge for most of the organizations. Apparently, data is all scattered and organizations are finding it difficult to capture and measure leads from disparate channels. Leads are not automatically getting distributed to team members, and this is hampering rapid response and revenue. This also makes data sharing between teams extremely cumbersome. Why is Follow-up of leads faltering? The main reason behind this is that the sales and marketing teams are not on the same page. There is a double entry of data and scattered and complicated data management process that is reducing the response time drastically. There is no systematic process to create rules to distribute leads to teams and no automated follow-up task alerts or e-mail notifications to initiate e-mail drip campaigns; subsequently, these causes are leading to the time-consuming setup and costly maintenance. In conclusion The need of the hour- Your leads deserve timely follow up To speed up your borrowing experiences by overcoming the inadequacies of worn-out legacy systems, we will soon be launching a secure, reliable, scalable, interoperable, cloud-based solution. It is natively built on Salesforce platform, allowing for origination, underwriting, and servicing end-to-end. The system is economical to operate and can dramatically change your go-to-market strategy which helps users connect with consumers more quickly. This results in increasing the effectiveness of your sales operations & improves organizational efficiency. Want to learn more? You are just a step away. We would be glad to arrange a meeting with you. Say [email protected] for more information.

The Fusion of AI and Cloud Computing in Consumer Lending

Digital transformation is the key to any organization’s survival. Compared to other industries, the consumer lending industry is slow in the process of transitioning from legacy platforms to digitized environments, 87% of the banks still use legacy systems. Consumer lending and servicing is loaded with data, involves long process times, and is driven by stringent compliance requirements. There is an increased need to move away from manual lending process to a more automated, digitized consumer lending ecosystem to drive efficiencies, reduce costs, and streamline process outcomes. Enabling an end to end digital integration facilitates more seamless and engaging customer experiences, fundamentally changing the business core of the lending industry.     Leveraging AI and Cloud Computing to infuse sustainable value Though the permeation of these technologies is on the lower side in the lending industry, Cloud Computing and Artificial Intelligence are slowly playing significant roles in transforming the operational and business models of this space. According to a Gartner study, by 2020, banks can offer advice by using AI chatbots that can learn about customer’s user habits. Companies paying equal attention to security in parallel, when we see 65% of FS companies said they have adopted cloud-based security (source: pwc). Cloud Computing platforms are enabling the rapid deployment of services by seamlessly connecting and configuring virtualized technology resources, augmenting faster time to value and reducing the cost of ownership. Consumer Lending firms leveraging AI and Cloud While lending firms are building digital capabilities to harness more intelligence on customer needs, they are also actively leveraging Artificial Intelligence to deliver more personalized, context-aware services to their customers. An ideal mortgage lending scenario is loaded with data attributes, making it an ideal destination for AI algorithms to analyze customer behavior and buying probabilities, enabling lenders with more decisive insights for informed decisions. AI areas of impact include Compliance, Marketing, Portfolio Management, Origination, Capital Markets, and Servicing. What changes move to the cloud? Key benefits of adopting a cloud platform include higher participation levels across various businesses, quicker access to relevant information, and improved collaboration across time zones, enabling speedy decision making, cloud repositories are scalable, centralized, facilitating data integrity and security while preventing data theft. Centralized data access streamlines document management lifecycle and promotes transparency within borrowers, lenders, investors, and regulators. In conclusion, Consumer lending businesses can leverage the symbiotic power of AI and Cloud to drive business impact. With a huge amount of centralized data accumulated in the cloud, AI can access this data to develop better CX strategies. The merger of AI and cloud is bound to influence the evolution of intelligence-driven ecosystems and will lead the next wave of technological disruptions in the consumer lending space. FAQs – Tavant Solutions How does Tavant combine AI and cloud computing for consumer lending?Cloud-native AI platforms provide scalable processing, real-time analytics, and machine learning for high-availability, secure, and rapid lending decisions. What advantages does Tavant AI-cloud fusion provide to consumer lenders?Reduced costs, faster time-to-market, enhanced security, automatic updates, and the ability to handle peak volumes seamlessly. How does cloud computing benefit consumer lending?Reduces IT costs, improves scalability, enhances security, enables faster feature deployment, disaster recovery, and third-party integration. What is AI-powered consumer lending?Uses AI to automate credit decisions, assess risk, detect fraud, personalize offers, and optimize pricing with faster, more accurate results. Is cloud-based lending secure?Yes, through encryption, multi-factor authentication, audits, compliance certifications, and advanced threat detection.

Create a Smooth Digital Experience for Millennials on Cloud

50% of the world’s population is under the age of 30: these millennials are digital savvy, socially liberal, educated and excited about future. Organizations are leveraging digital technologies, such as cloud, AI, analytics, and blockchain to radically change the way they connect & innovate for millennials. Every element of business, from the supply chain to customer experience, business processes to finance, is getting disrupted. However, there is no aspect of organizational change that is more profound than the effect felt by the employees. It’s not just technology that is acting as a disruptive force in today’s business environment. The workplace itself has also changed with the arrival of millennials into the workforce. PwC’s research states that by 2020, millennials will form 50% of the global workforce. Millennial workers are entering the workplace and this generation is redefining and reshaping the workplace of the future. What’s new & quickly available in shelf to consume? These ‘Digital Natives’ were born into a world where technology played a vital role in all aspects of life – at home, school, work, and personal interactions. They have an admirably tech-savvy view of everything. However, as the number of millennials in the workforce grows, organizations are faced with a need to re-evaluate everything from organizational structure to business processes to fit with their inherently ‘digital first’ view. Millennials simply aren’t willing to adapt out-of-date technology. Whether it is hardware, software or services. They are shunning the enterprise traditions of PCs or laptops and bringing their own devices to work (BYOD) – and expecting the same levels of access to the network as ‘enterprise hardware.’ They are demanding access to social media tools – not only to keep in touch with friends during work hours but also to make them more productive and collaborative. And they will not stand for clunky software, unintuitive or confusing interfaces, or anything less than a smooth, fast, seamless experience. As part of the on-demand generation, they aren’t prepared to wait weeks – let alone months for anything. If their application doesn’t deliver immediate results, they head back to the app store to find one that does. Five minutes later they are downloading the next application that helps them do their job. This millennial instinct translates to enterprise apps as well, creating significant tension with IT procedures.  Following well-outlined security procedures, IT departments spend years selecting, implementing and testing complex business systems. It is understandable that they are then concerned about holes being blown in their carefully devised governance, compliance and security procedures by this new generation. How millennials can get business advantages across industries when companies migrate to the cloud Better scalability and Infra with the cloud:  Cloud has been a key driver helping lenders achieve scalability quickly while also helping lower the costs. More importantly, it provided the flexibility to innovate, launch products and structure deals quickly. Cloud infrastructure and modern lending services are thus interlinked to each other, supporting each other with the pace of innovation required by rapidly changing customer behavior in the consumer lending industry. Seamless integration with clouds:  Mortgage and Lending cloud solution with a design-thinking led approach using business process automation, robotics, analytics and cognitive capabilities ensure seamless integration. Create a consistent omnichannel experience with cloud – Get the information you need, when you need it.  It allows your users to search visually through your product catalog and enhance search and discovery, and subsequently, convert more customers using semantic and visual understanding. This involves a complete transformation of the core processing platforms, replacing legacy core processing engine with a cloud-based core processing engine. Create an omnichannel experience to innovate new product offerings in a meaningful way by leveraging highly accessible and versatile cloud models to get to market quickly that ensure innovative digital experiences for tech-savvy customers. A 360-degree view of all of their customers’ activities offers clear benefits. – Consumer lending companies can keep a watch on their users existing lending products, spending and income patterns, their savings profiles and by leveraging this information they receive across various channels, including social media and marketing campaigns, they can create a detailed customer profile with actionable intelligence. Making personalization easier with the cloud: Cloud and analytics provide meaningful and deep insights into customer preferences that can help in taking merchandising decisions. Personalization in retail banking entails a wide spectrum of offerings, as they say, ‘different strokes for different folks’. It extends beyond products and offers and is about providing a frictionless, seamless, and pleasurable experience to customers, while knowing who they are, what they like/dislike, predicting their behaviors, and optimizing their next best action. Management and operations. With the cloud, consumer lending companies can shift from highly manual to highly automated services. Self-provisioning allows business units request resources and build environments on demand, eliminating the need for IT to step in. Cloud platforms also enhance continuous application development and delivery. Workload management. Here, the shift is from a static approach to one that’s elastic. Cloud-based workloads can be moved from one computing environment to another based on the policies or conditions detected when the workload runs. This enables systems to strike the right balance, providing needed computing resources without any overcommitting. In conclusion Needless to say, millennials are a particularly important demographic for enterprises to pay attention. In all aspects, they are undeniably the first ‘digital native’ generation, and interestingly, the US Bureau of Labor Statistics has predicted that by 2030 millennials will make up 75 percent of the workforce. Moreover, as millennials grow into managerial roles, their priorities — i.e., working for their passion and more than just a paycheck — and leadership styles will have a significant impact on all organizations in the coming years. It means organizations need to innovate and be agile at the same time to meet millennials’ expectations when thinking about ROI on Cloud. Want to learn more about balancing various needs of your organization when using cloud?

Demystifying Digitization in Consumer Lending

There’s a continual rise of digital adoption, and the term “Digital” has been redefined in the consumer lending industry. Borrowers’ expectations have shifted drastically, and new opportunities are enabling lenders to provide additional value to borrowers, and they must focus on new sources of differentiation to gain competitive advantage. These changes are ushering in a true paradigm shift, creating a new generation of Digital Mortgage. Embracing these changes can be a good thing and is undeniably a big opportunity for lenders. Digitization should not be seen as a replacement of the loan officer but should be seen as an opportunity to increase efficiency and get rid of 100% of cumbersome transactional activities that the loan officer performs today, so they can spend 100% of their time on clients and enhance the customer experience. There are many trends that are dramatically reshaping the consumer lending industry; the more prominent ones among those include: •    Increase in automation– Consumer lending automation enables fintech enterprises to transform their cumbersome manual lending process to a truly digital process that not only increases productivity but also meets the expectation of today’s customers. The automation allows banks to enhance customer service, reduce costs, improve compliance and generate revenue faster. •    Emerging technologies like AI and machine learning leverages algorithms to enable lenders to predict borrowers’ requirements, inform underwriting decisions and accelerate the lending process. There is also a growing comfort with virtual assistance and Voice interfaces. •    Blockchain Technologies to add transparency and efficiency into the lending process while reducing risk- Making use of a distributed ledger, parties involved in a mortgage transaction process could witness what’s happening with the loan and have a cryptographic level of security to claims against that data. •    Adoption of design thinking approach and gamification techniques for more engaging customer experience •    Changing customer behavior due to Self-service–  Self-service technologies are changing customer behavior. These technologies now let service businesses streamline transaction processes, reduce overhead, and potentially increase revenue — all while giving the customer more control over the service process. •    Offering live chat services, Proactive advice, and recommendations by viewing the customer’s journey and predict borrowers’ needs even before they realize what they need. Delivering an excellent digital experience is no longer an option for lenders in today’s marketplace- it has become a strategic imperative. Lenders should no longer question whether or not to invest in digital but focus on how to deliver long-term value. Advantages of digitalization •    Processes/tech transformation– Digitization optimizes non-customer-facing processes through modern technology that is used to deliver superior customer experience. •    Better transparency– Digitization allows organizations to target their customers effectively with thoughtful, relevant, and more appropriately timed actions and offers. •    Innovative products and services– Inherent in digitization is innovation, which supports a redesign of products and services based on customer research, segmentation, and analysis. •    Delightful borrower experience– Provide delightful personalized experiences to borrowers across digital channels •    Faster processing with reduced operational costs– Back-office automation enables increased operational efficiency and reduced cost. Key opportunities for lenders 1. Know your customers 2. Put money into digital and time into traditional 3. Keep things simple 4. Make the customer experience your differentiator 5. Identify the gap and determine the top priorities Final thoughts Borrowers perceive home-buying as a single transaction and anticipate all stakeholders to work together effectively and seamlessly. Lenders who effectively serve as the central point to orchestrate the overall transaction can position themselves as trusted advisers and improve the overall customer experience. Are banks and credit unions keeping up with consumer demands when it comes to digital banking offerings? Are your competitors’ digital lending platform leapfrogging your online capabilities and customer experience? Is your overall digital lending transformation failing to achieve your desired goals? If your answer is yes, then you are not alone, and a strategic shift in your approach to digital can fix the problem. You are just a step away. We would be glad to arrange a meeting with you. E-mail us at [email protected] for more information. FAQs – Tavant Solutions How does Tavant simplify digitization for consumer lending institutions? Tavant provides end-to-end digitization frameworks including legacy system integration, cloud migration services, API development, and change management support. Their approach ensures smooth transitions from paper-based to fully digital lending operations. What digitization services does Tavant offer to traditional lenders?Tavant offers digital transformation consulting, platform modernization, workflow automation, customer portal development, and staff training programs. They provide comprehensive support throughout the digitization journey. What does digitization mean in consumer lending?Digitization in consumer lending refers to converting paper-based processes to digital formats, implementing online applications, automated decision-making, electronic document management, and digital customer interactions throughout the lending lifecycle. What are the benefits of digital lending?Digital lending benefits include faster processing times, reduced operational costs, improved customer experience, 24/7 availability, better data analytics, enhanced security, and the ability to serve customers remotely. How long does it take to digitize lending operations?Digitization timelines vary from 6-24 months depending on the complexity of existing systems, scope of transformation, regulatory requirements, and organizational readiness for change.

Knowledge Brief – Warranty Reserves and Accrual Rates Management

In the aftermarket business, the warranty reserves determine how serious a manufacturer is about their after-sales strategy. According to reports from Harvard Business Review, businesses are focusing on their after-sales strategy to generate additional business and improve customer satisfaction. Here are the top 3 things that give a better perspective about warranty reserve:  1. Basics of Warranty Reserve and Accrual Rates: Warranty Reserve is a fund maintained by the manufacturer to meet warranty expenses. The warranty reserve balance is the balance left after deductions of claim expenses for the year. The accrual rate is usually a fixed percentage of sales and is managed by the finance department of the company. The finance department can adjust the accrual rate to manage company earnings. Hence, management of warranty reserve and its accrual rates becomes crucial for a company. The finance department of a manufacturing company should ask these five basic questions to manage and analyze warranty reserve information: What is the opening balance of the warranty reserve? What is the current additional reserve including this year? What is the status of warranty expense? Are there any cost adjustments to the reserve? Are there any external factors, such as currency fluctuations, that impact the warranty reserve? 2. Influencers of Warranty Accrual Influencer 1: Manufacturing Quality The manufacturing quality of a product dictates the future course of a company. Companies have paid huge penalties and suffered declining market share due to poor product quality. Quality manufacturers invest in a robust Product Quality Management System that streamlines complex quality management processes and integrate efficiencies into the system. An efficient product quality management system helps identify and fix issues faster-providing insights into current manufacturing processes. These systems have a positive impact on warranty reserves by reducing warranty expenses. Influencer 2: Product Mix Every new product launched by the manufacturer could have newer product-related issues. These issues need to be analyzed and documented by the warranty teams to determine corrective and preventive actions and may see a surge in warranty expenses during this period of product stabilization. This influences the warranty reserve. If these scenarios occur for a series of products, the warranty reserves suffer substantially. The new product launch is probably the most precarious activity and can also be a challenge sometimes. There is no historical evidence of selling a product or offering warranty, upon which a manufacturer can rely to avoid the warranty impact. Influencer 3: Changes in Warranty Coverage Period Marketing initiatives can improve product sales by providing extra warranty coverage. The extra warranty coverage requires loading the company’s existing warranty reserves. This, in turn, increases the average repair cost per unit. Disclosing Accrual Information: Some U.S. regulations, such as FASB interpretation 452, require a manufacturer to disclose the warranty terms, accounting policies and sources of funding of the warranty accruals. U.S. regulations suggest that the Extended Warranty Cost reserve be handled differently than standard warranties, the costs of which are recognized at the inception of warranty. There are many accounting methodologies to manage accrual information; some of the popular ones are: Bornhuetter-Ferguson Test A Priority Average Age of Warranty Claim Times Annual Spend Active Life Approach Calendar Year Payments to Revenue Approach A good Warranty Management System must have reporting capability on metrics, such as: Average warranty cost per vehicle Breakup of costs by Parts, Labor and other Services  Relation between product failures and warranties Top product models causing major warranty expenses These metrics will give an in-depth understanding of failure information and expenses incurred against each failure and help connect the dots between reserves vs. expenses. Tavant Warranty is a flexible, user-friendly, and effective warranty management solution for the complete warranty lifecycle of original equipment manufacturers and aftermarket industries. Its unique cross-functional integrations structure connects business departments and leads to a rapid reduction in warranty costs and reserves, increased supplier recovery, and enhanced reserves forecasting accuracy. The system backed by Artificial Intelligence (AI) results in better workflows for manufacturers, which in turn improves cash flow, thereby improving financial health and profitability for the organization. “Tavant Warranty is a one-of-a-kind, AI-powered solution that helps organizations maximize their aftermarket revenues by over 2%, reduce claim processing time by 30%, increase supplier recovery by 50%, eliminate fraudulent claims, improve product-return cycle time by 25%, and helps you improve your warranty reserves” Information.

5 Things Worth Sharing from MBA Tech 2018

The Changing Aftermarket Industry According to a global strategic business [i]report, the global automotive aftermarket industry is expected to reach $722.8 billion by 2020.  This rising demand for aftermarket parts and services is driving new growth and revenue opportunities for automotive aftermarket organizations. Moreover, digital transformation is re-imagining the automotive industry. Platform-based innovation and hyper-connectivity are shaping the new world of the automobile. Interestingly, aftermarket, the secondary market of the automotive industry is also experiencing this paradigm shift from traditional legacy systems to the digitalized world powered by AI, Machine Learning, IoT, Big Data, Analytics and Mobility. Rising Customer Expectations There is a significant change in the customer buying behavior which has acted as a catalyst in the progress of automotive aftermarket. Interestingly, today’s consumers are keeping their vehicles longer and are more aware of the importance of preventive maintenance and scheduled servicing to maximize the lifetime value of their vehicles. Furthermore, in today’s modern parts marketplace, the millennial customers have become more sophisticated and mobile-oriented while staying connected with their local automobile retailer. They are more in control of the buying process than ever before – with the ability to price, source, and obtain products and parts from a wide variety of sources, including spurious parts suppliers who don’t have the same overheads as the OEM. Needless to say, the consumers are now expecting a seamless experience spanning via omnichannel including physical retail supply shops, apps, websites and so on. Data, Data Everywhere The exponential growth of connectivity and data in manufacturing is drifting aftermarket services towards a new era. The next generation of tools and processes is equipped with next-gen technologies that enable unprecedented collection and transmission of data, which can be exploited to improve aftermarket operations. However, the aftermarket value chain is still highly segmented in disparate data silos. Each player focuses on its perimeter, where it exercises a strategic control thanks to its assets (parts IP, integrated offering, global network, and so on). Business models are still primitive and rely on service contracts (diagnosis, repair, parts, and maintenance) using a transactional mode (cost per operation). Artificial Intelligence & Machine Learning to Rescue Leveraging IoT technologies and platforms built into devices have increased the potential for new revenue streams through innovative data sharing/insight opportunities. And the good news is the large volumes of data generated by IoT devices can now be understood, acted upon and monetized with the help of AI and ML. Organizations can consider integrating IoT data with the existing warranty data to obtain new insights into their customers, products, and operations. In turn, this can lead to optimized product service, enhanced support processes, and the provision of new and differentiating customer experiences, all of which can help in driving revenue. Eventually, improved warranty performance has a direct impact on the customer experience; for example, if a consumer feels that a company acknowledges when products fail to meet up with their expectations, they are more likely to stay in the future, building brand loyalty. The time to act is Now If you are looking to implement a world-class warranty solution without investing heavily in infrastructure or the resources required to deploy & maintain the solution at your premises; our on-demand solution is tailor-made for you. By applying AI and machine learning algorithms to massive amounts of customer data, Tavant’s enterprise warranty solution TWOD on the Salesforce Cloud combines its warranty solution expertise with industry best practices to offer end-to-end warranty lifecycle management. It provides enhanced visibility and proactively populates business opportunities for the sales, service, and marketing teams in their CRM. Want to Explore More? To delve deeper, attend our engaging session on ‘Artificial Intelligence, Machine Learning and the world of making smarter, faster and better decisions’ at WCM 19 and learn how to unlock your sales and revenue potential or just say [email protected] to schedule a meeting.

Digital Innovation FAQs Part II: Customers, Experience and Disruption in Consumer Lending

This is Part II of the Digital Innovation FAQs series. Part I talked about innovation, millennials and technology trends. You can find Part I here:Digital Innovation FAQs Part I.    #4 So where do you start? How do we define the digital transformation strategy? Every company has its unique brand values and strengths. They also have some vision and strategy in place. In our Digital Experience (DEX) workshops, we work together with our clients to find the synergy and opportunities… by understanding their customers, their brand values, we go on the discovery, a journey, to see the opportunities where digital experiences can create the most value and impact. There is a fair bit of research and homework involved. In fact, some of our clients proactively do their own research and have figured out customer journeys and digital opportunities and pain points as well. The digital strategy is aligned with the overall strategy. We help with the strategy to account for any digital considerations and many companies already recognize this very well. Understand the customers, embrace the brand values and keep it simple. Strategies built around that can then focus on execution and give great results! #5 So give us an example of brand values. For example, take “trust” as a brand value. It’s still a very people-centric industry… real people dreaming about their own home and they trust lenders… real people who help them. The people in any company work a lot to get the trust of their consumers. Every channel, retail or digital, every interaction, every experience should build trust. Trust is precious… very hard to build but fundamental from a value perspective. Transparency is another. Most customers cannot understand the lending process and regulations. Lenders spend a lot of time educating customers when their time could be better spent helping them buy their dream home. By being honest, by being clear about steps, fees, regulations, you build more trust. #6 And, what is simplicity? Simplicity here can help by making information simple and clear. Lenders can then spend their time helping customers. Customers feel empowered because they understand and feel in control. You get operational efficiencies just by simplifying the information. Simplify the process, reduce the steps, make it easy to use, easy to apply, easy to approve… make it simple. You get more customers, more referrals, more business… lots of happy people. That is what digital experiences and transformation all about… happy customers! #6 That sounds simple… Why don’t we see more of that? That’s why you need to be strategic… have a clear digital strategy with priorities in place. It’s human to want more.     The key is to focus on a few, experiment till you get it right and then scale it for your company. Listen to your customers. Look for business value and impact when evaluating projects and assessing where you are. #7 Can you show us more? Yeah, sure. Take a look at our Digital Practice @ Tavant for our offerings and case studies. We will be very happy to reach out and discuss… get it touch! ‘Customer journeys’ is one of the cornerstones of our Digital Experience offerings. We will be publishing a whitepaper on customer journeys soon. 

10 Ways AI Can Disrupt Consumer Lending

Artificial Intelligence (AI) and Machine Learning (ML) are having a significant influence on industries. From robotic process automation and speech recognition to virtual agents and driverless cars, the extent of its impact has moved us from a mobile-first world to AI first.     In a recent study of digital executives, the majority, 31%, said, virtual personal assistants following Automated data analysts (29%), automated communications like e-mails and chatbots (28%), automated research reports and information aggregation (26%), and automated operational and efficiency analysts (26%) rounded out the top five. Business leaders said they believe AI is going to be fundamental in the future. In fact, 72% termed it a significant ‘business advantage.’ AI enables enterprises to unleash the trapped value in their core businesses. Machine-based neural networks can comprehend a billion pieces of data in seconds, placing the ideal solution at a decision maker’s fingertips. Your data is constantly being updated, which indicates your ML models will be revised too. Your enterprise will always have access to the latest information, including breaking insights that can be applied to rapidly changing business requirements. No doubt, many FinTech companies have cut down the costs of credit underwriting to find the right customer through Machine Learning applications. How can AI help Consumer Lending? Consumer Lending (CL) of all kinds, such as mortgages, autos, credit cards, student loans, etc., is a data-rich environment. We can say, at its core, lending is undeniably all about ‘big data’. For example, in a typical mortgage lending scenario, we estimate that between the borrower’s credit history, property, employment, income, tax, and insurance information, more than five thousand data attributes are captured during the lending process. This is a time-consuming and expensive process and in case of many lenders, an extremely manual and cumbersome process. And it is difficult to predict how much of this data is even relevant? How much of it is useful in forecasting borrower behavior during the application processing, closing, post funding and servicing stages? By leveraging more data and analyzing customer default probability, the credit scoring systems can predict behavior, thereby helping lenders come to a more conclusive decision based on data. Fintech organizations need to drill into the insights to grow their business, manage risk, and capture more market share in the competitive consumer lending landscape. 10 ways AI can impact the Consumer Lending industry Below are just some of the ways that this technology is taking the consumer lending industry by storm. Lower underwriting and origination costs by machine Reduced credit losses Fewer Losses from fraud Decreased agency recourse risk Better risk-adjusted margins Less servicing costs Reduced Write-offs Greater Customer Satisfaction Higher origination revenue Lower due-diligence cost   The Road Ahead It is apparent that AI and ML are the future of consumer lending. Digital Transformation is drastically impacting the mortgage process, and it is imperative for lenders to stay updated with these changes and adopt them proactively. Technology is no more a roadblock and today’s customers are very receptive to digitalization efforts. Consumers no longer want the same old experience; they want convenient, secure solutions that meet their lending needs. It is therefore crucial for the lender to create digital mortgage experience that goes beyond an online application to offer a data-driven digital process through AI-powered automation. AI and Machine Learning have enabled key players across the consumer lending landscape to transform, both regarding their back and front-end processes dramatically. From cost reduction to streamlined operations to increased efficiency, both AI and Machine Learning will continue to pave the way for the consumer lending industry. The promise of AI has always been to make lives better and to enhance the way we work. AI can reverse the cycle of low profitability through intelligent automation and innovation diffusion. Advancements in ubiquitous computing, advanced algorithms, low-cost cloud services, analytics and other next-gen technologies are now allowing AI to flourish. However, AI’s full potential will never be realized until organizations take more risks and begin to experiment with AI technologies more aggressively Later this month, we will be releasing our white paper on “Reshaping Artificial Intelligence with Consumer Lending.” FAQs – Tavant Solutions How does Tavant implement AI to revolutionize consumer lending processes? Tavant leverages advanced AI technologies including machine learning algorithms, natural language processing, and predictive analytics to automate loan underwriting, enhance risk assessment, and streamline the entire lending workflow. Their AI-driven platform reduces processing time by up to 80% while improving decision accuracy and customer experience. What AI-powered lending solutions does Tavant offer to financial institutions? Tavant provides comprehensive AI-enabled lending platforms including automated credit scoring, real-time fraud detection, intelligent document processing, and personalized loan recommendations. Their solutions integrate seamlessly with existing banking systems to deliver end-to-end digital lending transformation. What are the main benefits of AI in consumer lending?AI in consumer lending offers faster loan approvals (often within minutes), more accurate risk assessment, reduced operational costs, improved fraud detection, and enhanced customer experience through 24/7 availability and personalized service. How does artificial intelligence improve loan approval processes?AI improves loan approval by analyzing vast amounts of data in real-time, automating credit decisions, reducing human bias, and providing consistent risk evaluation. This results in faster processing times and more accurate lending decisions. What challenges do lenders face when implementing AI technology?Key challenges include data quality and integration issues, regulatory compliance requirements, initial implementation costs, staff training needs, and ensuring AI models remain fair and unbiased across different customer segments.

Blockchain – A Boon to Consumer Lending

Blockchain: The Next Fintech Wave for Digital Lending While blockchain has attracted world’s attention because of its association with Bitcoin, it is now being seen as a viable technology for the financial services industry. According to a report by Santander, by 2022, blockchain technology is poised to save banks $20 billion a year in infrastructure costs. Recently, McKinsey published a blockchain technology report where the firm analyzed how the technology is disrupting a range of industries, emphasizing financial services organizations and forecasted commercial deployment of blockchain technology at scale by the year 2021. Furthermore, a report by IDC indicates that the year 2018 will be a crucial stage for financial services organizations as they would consider making a giant leap from proof-of-concept projects to full blockchain deployments. But, why Consumer Lending firms are still reluctant to adopt the Blockchain technology? Despite the seemingly profuse growth of bitcoin and other blockchain technologies, key industry players still appear to be averse to embracing this technology. Because currently there is no legal or regulatory framework for blockchain applications, which also reveals that smart contracts are not yet legally binding. Furthermore, data privacy also becomes an impediment to blockchain adoption as all distributed ledgers need to adhere to each jurisdiction’s data privacy laws, which can be tricky for publicly-viewable ledgers. The cost of storage on Blockchain Database– Though the blockchain technology adoption promises many long-term benefits regarding productivity, efficiency and costs, it is extremely expensive to initially put it in place. The software that is required to run blockchain technology in enterprises must be explicitly developed for the specific firm and is therefore costly to purchase, acquire or develop in-house. Consolidating with Legacy Systems- An organization must either revamp their legacy system altogether or seek a way to integrate their existing system with the blockchain solution to make a move to a blockchain-based system. Energy Consumption- The Bitcoin network as well as the Ethereum network, both consume the proof-of-work mechanism to validate transactions made on the blockchains. The entire mechanism requires the computation of complex mathematical problems to verify and process transactions and to secure the network. These calculations require a significant amount of energy to power the computers solving the problems. In addition to the energy used to run the computers, a sizable amount of energy is also needed to cool down the computers. Benefits of the Blockchain Technology in Consumer Lending Blockchain lends itself to some of the common use cases including regulatory compliance, settlements, cross-border payments, custody, asset tracking, trade finance and post-transaction settlements within the financial sector. Many financial organizations have already initiated projects based on blockchain technology for payments and securities trading and spending on blockchain technology to transform existing cumbersome and inefficient processes such as cross-border payments, provenance, and post-transaction settlements. These are crucial pain points for many financial services organizations, and thus blockchain offers an attractive value proposition, Amongst this, consumer lending is an important genre where blockchain acts as a key value driver—but how blockchain technology can help the Consumer Lending industry? A few benefits to consider: 1. Identity Authentication Blockchain networks build a robust system of member identification. This can considerably boost the processing times for stakeholder communication. For example, a single borrower can create a digital ID that contains all their information in one place. This includes information about their mortgage history, outstanding balances, credit score, and income, etc. When applying for a home mortgage from different banks, this unique ID can be used at multiple lenders and even for cross-checking with credit agencies and employment verification. 2. Transparency for Lenders In a digital world, where lending and borrowing happens on the blockchain, time and resource-taxing business rules and processes are taken care of by algorithms. Reconciliation no longer exists, because the data is authentic and the need for trust is virtually wiped out. The security is no longer in question, as key facts and changes are transparent and this creates a lot of transparency for the lenders. They can find out the transaction history for an applicant from the initial submission to actual fulfillment of the loan. 3. Improved Servicing Efficiency of Loans Loan servicing businesses face data management challenges during loan collection and transfer processes. Blockchain technology could help make the process more efficient and streamlined. It is even possible to eliminate the entire servicing industry and replace it with a blockchain. In case of changes in the regulatory rules, blockchain can be adjusted to the new legislation more easily compared to the existing model where each firm is open to a different interpretation of the new government policy. 4. A single version of the truth Moving to a single and shared view of the truth will allow each party in the value chain to remove duplicative processes and save money and time. This approach could also be used in the front-end processes to bring in third-party identity providers and other information providers, who can help to counter fraud and AML risks while making the process easier for customers and sales associates. 5. Empowered users and increased security Disintermediation takes off both the risk and expense of counterparties and enables more empowerment for users to control their information. Blockchain also enhances the sharing of common data by resolving data inconsistency problems that occur during servicing transfers. Exchanging data through blockchain makes the system more cooperative and adds security. 6.  Streamlined Operations and Enhanced CX Faster and smooth processing for swift banking experience and reduced costs, with lesser complexity in business operations, can be empowered with Blockchains while also creating avenues for evolving business models. Financial entities can reap many more rewards when the clutter and complications of multiple ledgers are taken off, and lower transaction costs can be tapped successfully. Endless days for clearing and final settlement are now a thing of the past as transaction times become real-time and available all the time. LOOKING FORWARD The stakes of blockchain are undeniably too enormous for financial services firms to seek a wait-and-see approach. The Blockchain technologies drastically streamline operations and cut down costs

5 Tips to Survive NAB Show 2018

It is that time of the year again. In less than a week more than 100,000 people will take the city of Las Vegas with a storm to attend the ultimate event for the media, entertainment and technology industry, NAB Show 2018. Scheduled for April 7-12, NAB Show will witness more than 100,000 attendees, 200+ sessions, over 1700+ exhibitors and above all, countless networking opportunities. This conference has a lot to offer to its attendees, and this year the event has been beefed up with a lot more of exciting stuff like new conference programs, international pavilions, community mixers, Braindate and much more. Here are some tips that might help you sail through the NAB Show without missing the essential programs. 1)  Getting started: The first step to getting yourself accustomed to the convention is to explore the floor plan which gives you an option to search for exhibitors, booths, sessions, speakers, etc. Also, you can save the things you want to remember with a free My Show Planner account. Download the NAB app on your smartphone to have it handy. 2)  New attractions: During the show, while you will be busy exploring the different solutions and tools, you should also check out the new things happening at the conference, and you should plan them before you get there. Here are our ‘TOP five’ picks for this year a. AI Experiential zone b. Ad Innovations c. Next Gen TV Autonomous Transport d. Braindate e. South Upper Draft House 3)  Bring these to the show  a. Comfortable bag: You will collect a lot of paper and swag at the show. The last thing you want is an uncomfortable bag that cannot hold it all b. Your badge: How else will you gain an entry into the show c. Other utilities: Business cards, phone charger, hand sanitizer, etc. 4)  Transport: The organizers at NAB have made it easy for you to get around this year. Have a look at the Shuttle Bus Schedule and save it for use  during the show 5)  Plan and prioritize: If you are attending NAB this year, you should spend most of your coming week in looking at the different activities, events, sessions, etc. happening in and around NAB. Register for the event as soon as you find them. Remember, you won’t get to see everything. The key is to plan and prioritize. Bonus Tip: Don’t forget to have fun Along with all the presentations, sessions, demos and thought leadership, NAB Show 2018 also has a lot of fun events that you should add to your plan. Here is a list of some parties and after-hour events to consider: NAB Show 2018 parties Most important for all, try not to get intimidated or overwhelmed with all the socializing happening around you. Instead, make the most of this opportunity by planning. Among all the exhibitors, Tavant will be showcasing its data-driven solutions for the broadcasting industry at booth SU7323AD. Drop by to see some cool demos or a casual chat with our experts.

Redefine Digital Mortgage Experience with Encompass® COE

Digital Transformation is set to transform the mortgage industry by addressing issues ranging from customer experience, regulatory compliance, asset quality & risk, to efficiency and cost management. Lenders must embrace digital transformation or risk becoming irrelevant. Organizations that do not formulate a comprehensive digital strategy may lose business to competitors. The National Association of Realtors® reports that 90% of all home buyers search online for their home. For 42% percent of that group, the internet was their first step in the home buying process — before contacting an agent. According to the Ellie Mae Millennial Tracker™ report, tech-savvy Millennials represent the primary home-purchasing segment of the population (Millennials accounted for 84% of closed home loans in January 2017). Millennials these days depend on intelligent personal assistants such as Apple’s Siri, Amazon Alexa, Google Personal Assistant, and Microsoft Cortana. Apparently, many of these assistants function on devices that don’t even have specific screens and provide only a single “answer” to the customer’s query — as opposed to a list of results. Put another way, if the online listings for your mortgage business don’t include things such as your specialties, your credentials, and the languages you speak, your customers will not be able to find you if they search using those parameters. Improving customer experience is paramount to meeting the expectation of today’s consumers. According to published reports, 48 percent of US consumers believe companies need to do a better job of integrating their online and offline experiences. Digital natives such as eBay, Amazon, and Google have been leading the pack in remodeling consumer expectations for cross-channel convenience. In today’s evolving mortgage industry, to gain a competitive advantage, organizations must transform the customer experience. Positive customer experience can have profound impacts on your organization’s growth. Delightful customer experience is a long-term competitive advantage you can leverage to differentiate yourself in the market. While this may sound little daunting, companies are in the process of revitalizing their customer experiences every day. Are you looking to capitalize on the digital transformation? Embrace digital transformation and enhance your lending experience with Tavant Encompass Managed Services Tavant is a trusted Pro Partner that helps lenders to accelerate the deployment, customization, and adoption of Ellie Mae’s Encompass® all-in-one mortgage management solution. Our seamless integration with the Encompass ecosystem enables operational efficiency, reduced cost, effective maintenance, enhanced performance leveraging custom applications. We help our clients to easily adopt and leverage Encompass upgrades. We offer flexible and transparent delivery models to provide them with a mix of onshore, near-shore and best-shore managed services. FAQs – Tavant Solutions How does Tavant help lenders redefine their digital mortgage experience through Encompass COE?Tavant provides Encompass Center of Excellence (COE) services that optimize mortgage workflows, implement best practices, and maximize platform capabilities. Their expertise helps lenders transform their Encompass implementation to deliver superior digital mortgage experiences through process optimization, automation, and user experience improvements. What specific Encompass COE services does Tavant offer for digital mortgage transformation?Tavant offers Encompass configuration optimization, workflow automation, integration development, user training, performance monitoring, and continuous improvement services. Their COE approach ensures lenders maximize their Encompass investment while delivering efficient, compliant, and customer-friendly digital mortgage processes. What is an Encompass Center of Excellence (COE)?An Encompass Center of Excellence (COE) is a specialized team or service that provides expertise, best practices, and ongoing support for optimizing Ellie Mae Encompass (now ICE Mortgage Technology) implementations. It focuses on maximizing platform capabilities, improving workflows, and ensuring optimal system performance. How does a COE improve digital mortgage experiences?A COE improves digital mortgage experiences by optimizing system configurations, implementing best practices, streamlining workflows, ensuring proper integrations, providing ongoing training, and continuously monitoring performance. This results in faster processing, better user experiences, and improved operational efficiency. What are the benefits of having an Encompass COE?Benefits include optimized system performance, improved user productivity, better compliance management, enhanced customer experience, reduced operational costs, and maximized ROI on technology investments. A COE ensures continuous improvement and optimal utilization of Encompass capabilities.

How Mobile Solutions Can Reduce Warranty Costs

As technology advances every day, so do customers’ expectations from manufacturers. To be competitive and to survive in the market, manufacturers must provide improved solutions with lower costs. These goals can be achieved through mobility solutions.   1. Maintenance of Accurate Data Unavailability of exact product and customer information is a major challenge in the warranty industry. Mobile solutions help in capturing that exact data. Field service personnel can visit the customer site and capture the proper customer address, contact information, usage details, and service information. Maintenance of proper data helps in providing the correct coverage and maintenance, which in turn, helps to reduce warranty costs. Proper data also gives insights about warranty problems. 2. Lower Transit Time Field inspectors visiting the customer site can check machinery, perform the repair at the customer site, and update the problems directly from the mobile. The warranty team can start working on the case immediately. This reduces delays between various departments, speeds up the process and reduces the warranty costs. 3. Improved Process Mobile solutions help in reducing paperwork. When using a manual process involving paperwork, there’s a chance valuable data could be missed. Mobile solutions help in avoiding duplicate entries and important data cannot be missed since everything is maintained electronically. Regular reminders are sent to dealers, contractors, and field inspectors. This improves the overall warranty process, which in turn reduces the total cost. 4. Real-Time Connectivity Mobility solutions help in managing the process from any location. GPS monitors can be integrated with a vehicle to track its location. Telematics help to monitor the driving pattern of the vehicle, which reduces fraudulent claims and parts and service costs. It also helps to identify failures earlier, which helps to increase warranty cost savings later. 5. Increased Productivity Mobility solutions provide an option for employees to contribute to business process even while not at the office, which increases productivity. For example, the warranty processes like Warranty Registration, Arrival Condition Report, and Inspection can be done during installation/delivery from the customer site itself. The warranty team can start working on the claims immediately. This helps in improving productivity, thereby reducing warranty costs.

Evolution of Automotive Ecosystem

Decreasing sales, environmental regulations and increasing demand for more efficiency and new features are challenges every other manufacturer is looking to overcome. These challenges may decide the future of the automobile industry. If powerful engines, composite material, and lighter weight engineering were the trends at the start of 20th century, going forward, what may disrupt the industry is electrification, connected cars, diverse mobility and autonomous driving. These changes are not only important from the perspective of the automobile industry, but will potentially impact multiple other industries, such as insurance, high-tech, and telecommunication, connected with these solutions. Electrification The electric vehicle market is forecasted to grow at a CAGR of 23% through 2021, according to market research firm Technavio. There are multiple factors that may push for electrification, such as a drop in the price of battery prices (prices may fall by 70% by 2030(1)), government support in the form of tax breaks, incentives and benefits, and most importantly lower maintenance costs. What could further support this change are government initiatives to build and maintain electric charging stations in major cities as well as on connecting routes. Connected Cars (Vehicle-to-Vehicle Communication) Vehicle-to-vehicle communication is one of the critical new changes that may have a huge impact on passenger safety. With vehicles communicating with each other to share details such as speed, the direction of travel, and traffic conditions over a dedicated network, the speed and response period of every vehicle on the same road could be synchronized to the vehicle in front, thereby reducing the probability of a collision. According to WHO, auto accidents cost most countries almost 3%(2) of their gross domestic product (GDP). According to the U.S. Department of Transportation, deploying vehicle-to-vehicle communication can reduce 80% of the accidents that occur on roads in the U.S. Diverse Mobility Consumers today use their all-purpose vehicles for a wide range of tasks, but in the future, they may demand individual solutions for specific purposes, on demand, probably via their smartphones. There are already trends that point toward this change, such as a 30%(3) increase in car-sharing members in North America and Germany over the last five years. According to McKinsey, one in ten cars sold globally in 2030 will potentially be a shared vehicle, which could also mean more than 30% of miles driven in a new vehicle could be from shared mobility. Autonomous Driving With commuters spending an average of 42 hours every week in traffic in places like North America, there is a huge demand for autonomous driving, which could help drivers refocus and invest their time in more productive activities. The time spent in traffic increases to 104 hours per week in Los Angeles, the highest in the world, followed by Moscow where a commuter may spend 91.4 hours per week during peak time, according to the INRIX Global traffic scorecard. The beneficiaries.  OEMs would now be looking at plethora of information getting generated from individual equipment to not only improve the product, but also to create a new set of complementing products and services, such as networked parking service, vehicle usage monitoring and scoring (a service already available in many markets), predictive maintenance, over-the-air software updates and add-ons that could become alternate sources of recurring income for the OEMs. Dealers may move away from sales of vehicles to a fleet management model, managing only the service part of the business, resulting in highly consolidated market players with huge fleets. The transportation sector will be able to optimize its operational expense with autonomous driving opportunities for faster expansion and cost-cutting. IT companies and semiconductor manufacturers may become the largest suppliers for OEMs moving forward. With digitization and the electrification of the automobile, the major components that would come into play are the electrical hardware that will run the vehicle, the semiconductors that will be the brain for operations, and the software that will drive the logic on how the vehicle will operate. Companies that are able to integrate these into a single package (auto vision, artificial intelligence, IOT, etc.) may develop more of an edge over other companies. What is in it for others? Nearly 1.3 million people die globally due to car accidents. In the U.S. alone, for every death, there are 100 treated in emergency rooms, with an annual cost of USD 33 billion (4) in 2012. Autonomous driving could help reduce health care costs and change the car insurance industry completely. The telecom sector would benefit from the increase in traffic on their networks because of vehicle-to-vehicle communication but may have to upgrade its infrastructure to support higher speeds and lower latency. Electric utility companies may be one of the biggest beneficiaries of electrification; according to the 2017 report by Bloomberg New Energy Finance (BNEF), electric vehicles could account for nearly 54% of new car sales by 2040, which could mean a requirement of more than 1900 TWh of electricity every day — equivalent to 8% of global electricity demand in 2015.     (1)       Electric Vehicle Outlook 2017 by Bloomberg New Energy Finance (BNEF) (2)       http://www.who.int/mediacentre/factsheets/fs358/en/ (3)       https://www.automotiveworld.com/analysis/eight-disruptive-trends-shaping-auto-industry-2030/ (4)       CDC 2014: Motor Vehicle Crash Injuries -Costly but Preventable

Digital Innovation FAQs: Customers, Experience, and Disruption in Consumer Lending

Digital Experience (DEX) decides your next strategic move today. Customer journeys have taken the front seat and are fueling the disruptive force. Consumer Lending organizations looking to connect the dots of digital innovation often ask me to address these FAQs #1 What is Innovation? This is the most asked question in our Digital Experience (DEX) engagements. Simply put, ‘innovation’ is about new ideas that generate value for both the customers and the organization. In our DEX workshops, which are typically about 4-6 weeks, we work with our clients to understand their customers and their brand values… it makes innovation and the opportunity space tangible. That helps us generate innovative ideas that unlock value for both customers and business and reflect their brand values. Everyone wanted to be Apple. Decades later, there’s still one Apple. Google, Amazon, Facebook… they all did what they are good at… even Microsoft now. The key is to stay focused on your customers, listen to them and stay true to your brand values and capabilities to deliver great digital experiences. A lot of the opportunities in regulated industries like financial services and consumer lending are in making complicated things simple: –    simple to understand, –    simple to buy, –    simple to sell. Beyond that, digital experiences are about helping borrowers focus on buying their dream home and helping brokers and loan officers to build relationships by focusing on what their customers want and freeing up time… instead of worrying about document verification, regulations or what are a hundred things to check. That’s where the most significant opportunities lie. #2 How do you deal with Millennials? Customer Experience starts with understanding customers, and it’s no different for millennials, though many of us are not millennials. Interestingly, we find millennials also want good relationships with their mortgage lenders and brokers. A recent Fannie Mae survey found that almost 2 out of 3 customers relied on real estate agents and lenders for information. Millennials are also looking for help from lenders, brokers first and they appreciate transparency. Of course, they are more than happy to use digital channels to complete forms, but they also want to talk to their loan officers and brokers. They complement each other quite well, and we try to ensure the digital ecosystem is there to enable this interaction and deliver exceptional experiences. …what about loan officers and brokers? Do you still need them? They are vital to the experience! Contrary to what many lenders think, our research finds most customers (borrowers) are commonly influenced by them, and they remain the most trusted, along with friends and family. They remain some of the delightful experiences that we design for. Fannie Mae also found that over 90% customers (including millennials) want to use in-person channels at key points…they want to talk to their lenders. This is not about a loan approval; this is about buying your dream home. #3 What about the latest trends… Design Thinking, Artificial Intelligence (AI), Big Data? It’s about the relevance of the trends. We stay on top of the latest trends and set some of them as well. At Tavant, we work with clients on defining customer-centricity programs and the omnichannel strategies that can enable great experiences. The focus is on customer and business value and finding relevant solutions; the digital experience is really about the customer experience. Lemonade, perhaps, is one of the better examples of that. They use chatbots to settle claims within minutes, and most of their customers are honest because of that little behavioral tweak where they ask customers to pledge to be honest first. Technology by itself uncovers excellent operational efficiency. That remains a focus but, in our DEX engagements, we try to define digital experiences that unlock immense value. We have been fortunate to work with clients who trust us with cutting-edge technology and setting the benchmarks in these areas. Design thinking, the blockchain, behavioral design, big data, AI…they will transform digital experiences as we know it today. We see tremendous opportunities in some of these trends and innovation, but it is all about what is relevant to our clients. The strategy has to be based on the company’s brand values, its culture and how it is most pertinent to their customers. #4 But where do you start? What next? More on strategy, simplicity, and execution in my next post. Stay tuned! Digital Innovation FAQs Part II  FAQs – Tavant Solutions How does Tavant enhance customer experience through digital innovation in lending?Tavant creates omnichannel experiences with personalized loan recommendations, real-time application tracking, instant approvals, and seamless digital onboarding. Their innovation lab continuously develops customer-centric features based on user feedback and market trends. What disruptive digital innovations does Tavant bring to consumer lending?Tavant introduces voice-activated loan applications, biometric authentication, AI-powered financial advisory, blockchain-verified credentials, and augmented reality property evaluations to revolutionize the lending experience. How is digital innovation changing customer expectations in lending?Digital innovation has raised customer expectations for instant responses, personalized offers, transparent processes, mobile-first experiences, and seamless integration with their digital lifestyle across all lending touchpoints. What digital features do customers want most in lending?Customers prioritize instant pre-approval, mobile applications, real-time status updates, digital document upload, rate comparison tools, and personalized loan recommendations based on their financial profile. How do traditional lenders compete with fintech companies?Traditional lenders compete by adopting digital technologies, improving customer experience, leveraging their trust and stability advantages, forming fintech partnerships, and investing in innovation while maintaining regulatory expertise. What advanced customer experience innovations does Tavant offer beyond basic digitization?Tavant provides predictive customer service, proactive financial health monitoring, AI-driven cross-selling, personalized payment scheduling, and integrated financial wellness tools that go beyond traditional lending to support customer financial success. How does Tavant help lenders stay ahead of customer experience disruption?Tavant offers continuous innovation programs, customer journey analytics, A/B testing frameworks, and emerging technology integration services. They help lenders anticipate and respond to changing customer needs before disruption occurs. What is the next phase of digital transformation in lending?The next phase includes hyper-personalization, conversational AI, embedded

Reasons Why Extended Warranties Are a Must

In 2016, $23 billion on protection plans and $17 billion on vehicle service contracts were spent by consumers on appliances, mobiles, electronic appliances and computers1. Extended warranties are one of the largest businesses in the U.S. How is an extended warranty beneficial to a customer and why should they opt for one? By ‘mobiles’ I think you mean smartphones, but we don’t say that in the U.S. Is that what you meant? When you want to keep your vehicle for a longer period of time: When we like our vehicle, we want to keep it for a longer period of time. An extended warranty helps by providing warranty coverage beyond the warranty tenure to help maintain the vehicle for a longer period. An extended warranty or vehicle protection plan helps to keep the vehicle running smoothly and hassle-free. Repairs are more costly than having an extended warranty on the vehicle: Repair bills on a vehicle can often be very costly. Service appointments are also tiresome and inconvenient. The more you drive your vehicle, the more you will pay for maintenance costs and you run the risk of more repairs. Having an extended warranty saves you money in the long run. Customer satisfaction through peace of mind: The most important aspect of an extended warranty is peace of mind. Owners pay a little more to have an extended warranty, but one of the main benefits of warranties outweighs the cost: peace of mind. That peace of mind assures owners that any needed repairs will be covered. Purchase options: Most consumers mistakenly believe an extended warranty must be purchased only from the dealer or the OEM for a vehicle — not true. Owners can purchase extended warranties from other companies that offer more competitive warranty terms. Consumers can analyze different coverage plans and shop for the one that best suits their needs. Coverage options: There are companies today that offer options to purchase an extended warranty even after the original warranty has expired for the vehicle. Owners are not compelled to buy an extended warranty only for the vehicles for which the warranty is going to expire. Owners have the option to purchase extended warranties for the vehicles for which the warranties have expired. Based on the owner’s needs, the owner can opt for better coverage that suits his requirements. Sometimes, the extended warranty is never used. Buying an extended warranty is similar to health insurance, which we might never need, but we all know that “precaution is better than a cure”. In cases of a large repair bill, an extended warranty acts as a savior and covers all the expenses. We’re listening.  Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. OR Please add your thoughts, ingenious analysis and novel strategies in the comments section below. We look forward to hearing from you. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11. References: http://www.jdpower.com/cars/articles/tips-advice/pros-and-cons-buying-extended-warranty-car

Transforming Customer Engagement Using AI

When you buy a new vehicle today, you automatically subscribe yourself to the usual ritual of taking the vehicle for the scheduled service so that the equipment is 100% operational and the warranty does not get void. This is not always a pleasant experience for the end customer since they have to keep track of the distance the vehicle has covered, or the days covered from the registration date to align with prescribed service schedules. Finally, when they take the vehicle for service, there may be a long waiting period, and in the end, the whole service may just be an inspection of the vehicle parameters and a basic preliminary service. This process creates apprehension in the mind of the customer regarding the whole process of scheduled service. OEMs focus a lot on customer engagement in the initial phase of the customer lifecycle, but there are little efforts to improve the experience once the sale is done. This in turn severely impacts the customer retention process. With the advent of new technologies, maintaining a consistent customer experience throughout the lifecycle becomes easier for the companies.   Let’s look at a few existing solutions which can change the customer experience drastically while improving efficiencies upstream in the supply chain. Vehicle Telematics combined with Artificial Intelligence (AI): Most of the modern vehicles today come with an inbuilt telematics solution from the factory floor or at least have it as an aftermarket option. This system can capture and transmit the real-time information of the vehicle to an AI solution which will identify when exactly the vehicle should be brought to a service center and at the same time communicate the same to the customer. This will not only reduce the burden on the end customer to keep track of the scheduled maintenance but can also help to reduce the load on the service centers due to visits which may not be warranted. The solution can further suggest servicing slots (like booking movie tickets) to end customer so that load can be balanced across the complete servicing capacity. This will also have a significant benefit upstream in the supply chain with parts supplier being able to predict the possible demand for their parts at various geographical locations during specific time intervals in the future, based on the real-time data while removing the total dependency on the historical data for production planning. The solution once developed needs to be delivered to the end customer in a robust and scalable platform. Mobility: With over 37% of the world’s population expected to use a smartphone by 2018 from the 10% in the year 2011, this is a platform every company should take advantage of to reach to their end customers. By going mobile, companies can not only reap the benefit of being connected 24/7 with their customers but can use it as a platform to deliver wide array services both free and on demand. Companies can also use the mobile platform to communicate with their customers, provide a snapshot of the vehicle performance, help the customer book the servicing slot as per their convenience and provide customer support using integrated chatbots. Integrating all the key stakeholders with such a solution can help improve the operational efficiency as well as the customer satisfaction. Customers get notified when a service is due and get an option to quickly schedule it in advance, while the servicing centers can see the expected number of vehicles for the future dates and have the resources allocated to get the most optimum results. For all the stakeholders upstream in the service chain such as the parts supplier, this could help them move from the demand push to a demand-pull model wherein their production plan is synchronized to the predicted service schedule and the part replacement. Hence, it is a kind of win-win situation for all the stakeholders in the service chain ecosystem. Final Thoughts AI-powered customer service is a new reality. Customers aren’t waiting for companies to catch up, they simply shift their loyalty to a competitor with superior experiences. Companies hesitating to adopt, or even experiment, with AI, are already losing the innovation game and losing customers. AI is the future, and the future is now. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.

7 Mistakes That Are Stopping Your Retail Customers to Digitally Connect With You

Do you think you know your customers well? Are you confident you understand how they experience your brand in a myriad of online and offline interactions? Well, here’s some sobering news: Even if you think that you have a firm grasp on your customer experience, that’s all going to change sooner than you think. Customer experience has undeniably become the next battleground for business and the quintessential scale to pick between brands.      According to Forrester, 72% of businesses say that enhancing the customer experience is their top priority, while only 63% prioritize on implementing technology investments to reach their goal. Interestingly, market leaders are decided based not just on which company has the superior product at the lowest price, but on which company manages its connection with customers the best as ‘The Great Wall of Digital’ is being built between organizations and customers. How are you responding to the change? Do you feel disconnected with your retail customers? A few considerations  1. Not keeping it Simple  Solution– Today’s customers expect an easy-to-use interface across all channels, an exciting in-store experience, and fast service 24/7. Yet many organizations, especially incumbents, struggle to meet these expectations because of not so it user-friendly and intuitive interface. Remember, a simple user experience unfolded with crisp and clear user interfaces goes a long way in the saga of man-machine interactions. 2. Not establishing an emotional connection with your customers digitally Solution: Embedding technology in your day-to-day marketing operations doesn’t mean you’re sacrificing a human connection. That indicates treating every touchpoint with your customers like a face-to-face conversation. With customer data easily available, organizations can tailor their content to connect with customers on a more personal and emotional level. 3. Not getting a unified view of the customer Solution: Remember that data integration is the secret sauce to the customer journey. You should connect disparate data silos for a comprehensive look at your customers—gather customer insights and interactions in one place and get a 360-degree view of your customers to anticipate their requirements and optimize the customer experience. 4. Not providing consistent omnichannel CX Solution: No matter what industry you operate in, your customers expect to be able to engage with you effectively across multiple channels. Why? Customers typically want to get what they want from your business quickly, efficiently and on their terms – be this on your website, via your mobile app or by engaging with your customer-care team by phone, live chat or any other means. Creating a single, uniform face to your customers that delivers them with a consistent experience as they move across channels is, therefore, key to success in this omnichannel world. 5. Overlooking Personalization- the hidden ingredient to engage your customers Solution: You need to build an insightful and personalized shopping experience that connects digital, in-store, and back office operations. You must adapt to constantly changing needs, and provide phenomenal customer service by leveraging the Next-Gen technologies and innovation. Consider offering more product selection and recommendations by combining the best of online and in-store shopping. 6. Not building immersive retail experiences Solution: You need to elevate buying behavior by deeply engaging your customers through personalized retail experiences.  Seek more control over store operations through automation and advanced analytics capabilities. You must provide your customers the adaptability to make a purchase in-store, pick up in other locations, or have it delivered to their doorstep. 7. Not calming your impatient customer Solution As it turns out, intensely digital customers are also intensely impatient. They’re also not as wedded to digital experiences as we would like to believe. To retain this fickle and fast-moving group engaged, you need to focus more on dazzling them with superior digital service across all channels of interaction. In Conclusion: If you think the digital era is causing a disconnect between your brand and your customer, think again. Too many companies squander the treasure that is customer feedback.  Remember, customers feel disconnected when you fail to think through the degree of effort it requires to do business with them, you don’t provide user-friendly technology solutions, you don’t simplify every touchpoint and you don’t provide the personal touch. That’s what causes a disconnect between the two of you! The solution is to get closer than ever to your customers and that too, so close that you tell them what they need well before they realize it themselves. Create a digital strategy that places customers at its center to drive innovation that they will value, and then operationalize the model consistently. Extraordinary digital connections can undoubtedly deliver extraordinary results.

Things to Consider Before Replacing Google DSM

July 2019 Isn’t That Far Away Deprecation of DoubleClick Sales Manager (DSM) has caused some consternation in the media and publishing industry. After July 31, 2019, DSM users will no longer have access to the tools and data in the system, which enable publishers to manage digital channel sales. The reactions to this vary from frustration to denial. And like other deprecations, it’s something publishers must get past. For millions of affected organizations, the search for a replacement order management system has already begun, whether they are happy about it or not. Amidst the short timeline, you need to ask a few right questions while choosing a replacement for Google DSM. Below is a list of factors, a company should consider  1. Accomplishing the purpose served by Google DSM: Streamline business by providing media workflows with minimal to zero changes to the existing process a. Create proposals b. Create products and categories c. Manage rate cards 2. Automation and accuracy: Eliminate manual processes and errors by automating the business rules a. Data validations b. Calculate the metrics based on budget and product c. Dashboard for monitoring and reporting d. Define targeting rules 3. Integration with DFP: Implement a bi-directional integration with DFP for ease of managing campaigns a. Tracking the campaign status and basic reporting within the tool b. Integration should be updated with new releases of DFP APIs 4. Integration with Salesforce: Provide the ability to exchange information with CRM a. Access lead information b. Update opportunities automatically 5. Number of integrations: Create a seamless experience by connecting to in-house or third-party services a. Ad-exchanges/products: Provide a choice of ad platforms for bridging campaigns b. Data providers: Access audience segments c. Inventory Forecasting: Plan budget judiciously d. CDN: Upload assets e. Payments: Allow invoicing and acceptance of payments 6. Designed for digital ecosystem: The UX should be built keeping in mind the digital ad ops team a. Frictionless navigation b. Accessible from desktops, laptops, tablets 7. Cloud or on-premise: Can support your deployment model a. One click builds and deployment b. Easy to roll-out upgrades 8. Customers: Extent of experience with other advertising companies a. Domain expertise b. Depth and breadth in terms of technology choices available 9. Cost: The migration away from Google DSM needs to be cost-effective a. Application maintenance and enhancement b. No hidden costs 10. Customization and extensibility: Make changes based on the roadmap and additional requirements a. Add new modules b. Integrations with new services or platforms c. Modify existing workflow or UX based on the custom needs of the team 11. Value-added services: This can act as a differentiator amongst multiple available options a. Proposal templates b. Advance reporting and analytics c. Insights into the progress of media proposals d. Advance UX controls to improve operational efficiency Step Forward Don’t wait until 2019 to plan your transition.  So as DSM gets ready to kick the bucket, what a perfect opportunity to upgrade to a smarter and more powerful solution. Backed by more than a decade of experience in building digital solutions for top media companies, Tavant deeply understands the specific needs of the advertising industry and have successfully delivered solutions to complex business problems ranging from media sales to advertising and reporting. Companies can now utilize Tavant’s media planning and sales manager for complete control and flexibility over your order management process.

Reverse Logistics Function – A Strategic Review

It’s June, the end of the planting season of the corn crop (i.e., one of the crops contributing to most of the farm incomes in the United States and our client), and a farm equipment manufacturer is loaded with a lot of warranty cases for repairs of its farm equipment. The timeline to deal with these warranty repairs is a few weeks before the harvesting season in October — when the manufacturer’s customers are expecting the defective farm equipment (for which he raised a service request for repair) to be up and running. If you closely look into the problem, there are a lot of things that should have been taken care of by the manufacturer before the planting season, even before planning the sales of its farm equipment for the year. The diagnostic areas for our client, the manufacturer, could be the development of a robust dealer network to deal with warranty repairs in locations near to the concentration of large farms, availability of technical expertise in dealerships to repair the high-tech farm equipment unserviceable by technicians without special training; logistics and technology capability for part returns to cater to the high seasonal demand; and above all, the customer service centers to ensure the process of a repair request to delivery of the farm equipment back to the customer location is smooth and hassle free, to prevent the farm owner from having second thoughts when he considers buying farm equipment from you next time. These are just broader areas of concern in reverse logistics. If you delve deeper, there are other problems — unpredictable demands that may eat into profits of any big organizations if not handled well, like the geographical separation of the supplier network; transportation and labor costs; recalls; disposition strategies of the returned goods; and government regulations affecting the reverse logistic functions, to name a few. The reverse logistics look more complex, and are more an area of concern as compared to the forward logistics, which are more organized and also a part of planned strategies of any organization in the business of manufacturing, selling, storing, distributing and servicing its goods. Historically, reverse logistics is one area that is often an overlooked and disorganized function of any manufacturing organization. But not anymore. For the organization that does not have a planned strategy for reverse logistics, the trends of its financial performance and market share may be a gloomy picture. Statistics show how “Reverse logistics—the management of returned and recyclable goods” is, in fact, an important business activity. It is more expensive than expected, costing companies approximately US $100 billion per year in the United States alone. Costs associated with returned goods can be anywhere from 8 percent to 15 percent of a company’s top line. In fact, the cost of processing a return can be two to three times that of handling the original outbound shipment. Product returns exact a toll not only on a company’s financial performance but also on its image and sales. A major recall done by any automotive company can spread the negative sentiment about the company brand image like wildfire. So, the way of the future is looking at reverse logistics as more of a strategic and diagnostic tool to differentiate from competitors. The strategic approach demands strong infrastructure backed with the technological capability to have data visibility throughout the reverse logistics cycle. Big data and predictive analytics can be used to make important strategic decisions in network planning and cost optimizations. Many organizations have chosen to outsource their reverse logistics function completely to optimize cost. But choosing a third-party service provider is a big decision, before which a company needs to understand its current returns flows, identify the total cost of returns, profile the end-to-end returns, and quantify and categorize its return flows. The diagnostic tool approach demands looking at the root cause analysis of failures in logistics and manufacturing, recalls, and repairs to come up with metrics of predictive analytics and performance management that can identify areas of risk, improvement, and performance in both the forward and reverse logistics. The reverse logistics function should be viewed more as a profit center than a cost center. Companies should develop a financial framework to look at all financial transactions in the reverse supply chain and map them to the P & L and cash flow statements. Last but not the least, performance management of the reverse logistics functions using key performance indicators (KPIs) and metrics to ensure that the function is performing consistently and is in line with the strategic planning of the organization is important. Financial KPIs can include return costs as a percentage of sales, return processing costs by category/channel/supplier, shipping costs, inventory levels and carrying costs, and write-offs. Sources: http://www.supplychainquarterly.com/topics/Strategy/201201reverse/ http://www.supplychain247.com/article/managing_reverse_logistics_to_improve_supply_chain_efficiency_reduce_costs/fedex_supply_chain Meet our Warranty Experts at Booth #11, WCM Conference 2018 to learn more! CLICK HERE to schedule a personalized DEMO. 

AFTERMARKET 4.0 – A Consumer’s Perspective

Who hasn’t heard of IoT? Who doesn’t know about Cloud Computing? Everybody, right! It is everywhere — in our homes, cars, home assistants, and phones. But did you know that it’s what organizations are using to improve their processes, their productivity and their profits? Probably not. With the advent of the steam engine came the first industrial revolution — the 1.0, so to speak. When we found the assembly line production technique, mass production became the foremost manufacturing process — the 2.0 of the industry. The invention of computing and computer-based machinery was the third revolution of industry, which was, until very recently, the dominant feature in manufacturing and many other industries. The current and ongoing revolution in the entire industry is the Industry 4.0, which is the culmination of Cloud Computing, IoT, Analytics and Cyber-Physical processes to automate, improve and manage entire businesses and manufacturing processes. A selective case of applying all these technologies and perspectives to the aftermarket and warranty is “Aftermarket 4.0”.   Let’s say you own a brand-new BMW i8 with all its bells and whistles. It’s only natural that you would have a mobile app to manage your car. You have sensors in all the nooks and corners of the car to measure the speed, check if your seat belt is fastened, check the tire pressure and balance, know the number of people in the car, check the parking sensors, check the fuel mix sensors, and even check if you’re sleeping at the wheel. All these sensors would collect data and pass it on to an intelligent machine that would make a decentralized decision on what should happen next. And you would see all the collected data in a report in your mobile app. Then, when you take the car for a service at your dealership, which, by the way, was notified to give you a discount, for routine maintenance. Because the dealership has data about your good use of the car, you get an additional 5% off. Feels good, doesn’t it? Through this process, you’ve used cloud computing and mobile technologies to access the car details and take actions. You’ve experienced IoT with the full set of sensors in your car, and the analytics on the data, which provided actions you needed to take. There were cyber-physical processes that were managing the whole thing for you with little intervention. Welcome to Aftermarket 4.0! Imagine every need that you can regarding your vehicle. Fuel? Check! Engine Health? Check! Part Change? Check! With intelligent systems and sensors, the manufacturers and dealers can proactively help with any and every need of the vehicle. This doesn’t just start and end with servicing your car. Warranties, replacements, returns, trading, bartering and much more can be accomplished with standardized and identifiable sensors and nodes of a IoT. Not only does it create a much easier way to identify assets, estimate the value and trade them, it also creates a better environment for transparency for the customer and an experience that is much easier for purchase, repair, and return of vehicle necessities.

Aftermarket 4.0 – A Manufacturer’s Perspective

How many times have you faced the scorn of your customers because they could not return or claim warranty on your products? Have you ever wondered why, or if there was something you could do about it? As a customer myself, I have had more bad experiences than good when it comes to product returns and warranties. I always used to think, “Why doesn’t the manufacturer want his product back? After all, it is an opportunity to resell it, understand the product failure, retain a customer and create higher brand value. But many companies trade all this for the MRP of the product — seems out of balance, doesn’t it? But I don’t exactly blame the manufacturers because it is not that easy to quantify the benefit in a streamlined aftermarket process, a product return process, or a warranty process. On the other hand, the costs are easy to identify, but the benefits outweigh them three to one in this case. What if there was a way to reduce the cost of returns and warranty management, and be the brand your customers love most? Wouldn’t you be the first one to grab the opportunity? Let me introduce you to “Smart Factories”, an ongoing 4th revolution in the industry earmarked as “Industry 4.0”. Manufacturing always seems to be the first industry where optimization and profitability methodologies are created. Every business and processes in it are going to a level of automation, decentralized and intelligent decision making by machines and robots. This is not happening somewhere in the future; this is happening as we speak.Factories that can understand the objective of their operation, organize the raw materials and machinery, analyze the requirements, make in-production decisions and create the most optimized output are at this moment in use across the world. IoT is not new, but it is a new perspective on how the information captured by objects that are interconnected is used for improving the entire system. We need a core structure that can analyze and decide on what to do with the information. This is achieved by a Cyber-Physical system that can monitor the network of IoT nodes, make a digital copy or map of the entire physical process, and make decisions that optimize the use of all the resources available in the factory. loT has not been discussed yet, so will readers know what it is? The scope of such a system is not bound by just the confines of the factory or the manufacturing facility. The interconnected system of things (IoT) can consider the market demand in real time — the spikes and dips and the geographical distribution and many other factors — and feed this back to the system to be analyzed. The system can then decide which product, at which time, in which location must be produced at the optimum use of all the raw materials and resources. The system can extend up to any level of penetration across the value chain from the raw materials to the end consumer. Consider the flipside, which becomes the – the current industry state that is creating great opportunities in cost reduction and revenue generation for manufacturers and other players in the value chain.  Consider a product recall or even a repair due to an isolated incident or part breakdown. Because you know the specifics of each product and where has it been dispatched, a major recall and assigning a nearby technician to resolve the broken/worn out part become easy to manage. Many lives can be saved by just knowing the condition of your product at a given time; the lack of information is what causes most accidents. Many lawsuits can be averted by just analyzing what happened to the product at the time of failure. Lives saved? What types of products are we talking about? Assessing product quality has multiple advantages. Not only does it tell you how well your product’s performance is in the long run, it will indicate when a failure may occur, what conditions might create the failure, how much impact your product has on the environment, and the product’s total footprint. Analysis alone is not the objective of such a system; decision making based on all the information and inferences is. Designing, prevention, and correction become that much easier with a smart factory. Industry 4.0 and Aftermarket 4.0 are only the start of what is going to be the norm for managing your business processes in all business functions and levels. It is up to you where and when to start adopting it. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.

4 Ways to Harness the Power of Digital Transformation in the Aftermarket Industry

Reality Check The automotive aftermarket is undergoing dramatic changes in evolving customer expectations, acceleration of technological innovation, and shifts in competitive power. These changes are revamping the way business in the automotive aftermarket is conducted and value is created. Interestingly, Auto aftermarket sales becoming an omnichannel experience.Today’s consumer visits and researches a variety of channels — including websites, catalogs, social media, advertisements, and stores — before making a purchase. Furthermore, online channels are also giving customers quick access to the information on the prices of parts while online forums are giving customers a peer perspective on the quality/value of workshops. Before moving further, let’s quickly define “Aftermarket” The aftermarket, which is a broad term, can be at times, an afterthought in the automotive world and many consumers may not even be aware what the term means. However, it represents an enormous industry that offers significant value in improving the driving experience. Aftermarket parts are replacement parts that are made by a company other than your vehicle’s original manufacturer. The need of the hour is modernization of legacy systems in the aftermarket industry With legacy and disjointed systems, aftermarket processes suffer from high latency and lagged response. This may be because of restrictive technologies and interfaces or high cost of wrap up solutions. For example, waiting time for a service ticket is so high that it may dissuade the customer from reaching out to OEM or their dealers. Moreover, legacy systems do not enable the customer to do self-service. Additionally, without digital technologies, the customer interaction with a dealer or an OEM is constrained by time and geographic reach.   This is where the digital transformation plays a vital role!!! It leads to a better understanding of the customer, helps in personalizing responses, streamlines operations, enhances customer experience and improves revenue by serving manifested and latent demands. Digital technologies allow companies to derive total life cycle value of their incumbent customer base. So how digital transformation is changing the Aftermarket landscape 1. Social Media UPS Online shoppers survey shows online buyers are diligent about research, extensively use online reviews, ratings and social media. Interestingly, 70% of business buyers purchase from an online catalog rather than through another channel. By using social media and analytical tools customer touch points can be identified as social media has made customers more comfortable with connecting and engaging with one another and sharing their concerns and thoughts. Also, with the help of social media, aftermarket suppliers can eliminate the unwanted waiting and reneging. 2. Mobility and connected devices High smartphone adoption, millions of connected devices using IoT and other technologies and ubiquitous connectivity are creating new opportunities at multiple levels for OEM’s. These technologies are reorganizing and redefining internal and external structure and the process of companies. Mobile is critical in the shopping journey and mobile phones account for 34 percent of retail e-commerce sales transactions. That percentage is expected to increase to 48 percent by 2020. Needless to say, to stand out from competitors, a business needs to provide a smooth, frictionless experience and customer engagement by providing quick product searches, delivery and in-store pickup options, and mobile-friendly access to online sites. 3. Cloud Cloud and IoT enabled infrastructure enables a highly cost-effective, rapidly responsive and elastic IT, better aligned with the business needs. Cloud enables aftermarket business to innovate faster while leveraging existing systems and capabilities. Cloud-based tools provide visibility to aftermarket suppliers for every party in the supply chain to look at same data and analytics so that defects can be detected and corrected early in the chain. 4. Data & Analytics Digital technologies connect ecosystem-wide processes so that assets are efficiently managed using predictive analysis of potential errors and initiate. Aftermarket digital transformation pushes business strategies to evolve from selling a product or service to a customer experience-centric value proposition. By using data and advanced analytics, aftermarket suppliers can accurately forecast demand, deepen customer engagement and can also drive loyalty and sales. The Bottom Line: Digital Disruption is forcing companies to recognize the aftermarket’s enormous potential and understand the entire lifespan of a sold product, including supplies, repairs, selling and servicing spare parts, installing upgrades, handling inspections and add-ons, training, and customization. To stay competitive, it is imperative for aftermarket suppliers to change their mindset, create a vision, invest in digital content and analytics, lean on data to stay in touch with customers permanently, provide service through the traditional and digital channel and deliver exceptional aftermarket capabilities coupled with self-service.

Why Cloud Paradox in the Digital Age?

Don’t let fear keep you from harnessing the power of the cloud When cloud computing was initially introduced, many organizations didn’t understand the capabilities of this technology and were extremely apprehensive about placing their data on an external server mainly due to security reasons. As technology has improved and as the business world has become increasingly dependent on remote teams and off-site workers, accessing critical company data from the cloud has become crucial. Organizations are still unsure about moving to the cloud. Are you concerned about having your data in the cloud? If yes, then discover the truth about cloud computing! According to the study by Cisco, more than 83% of all data would be based in the cloud within the next three years. While a study by Gartner reveals that by 2019, more than 30 percent of the 100 largest vendors’ new software investments will have shifted from cloud-first to cloud-only. Gartner also predicts more cloud growth in the infrastructure compute service space as adoption becomes increasingly mainstream. Furthermore, a recent IDC survey on cloud market predictions indicates that 50% of IT spending and 60% of IT spend will be on cloud-based infrastructure by 2020. Additionally, rising demand from the migration of infra to the cloud as well as from compute-intensive workloads such as Artificial Intelligence, Analytics, and the Internet of Things— both in the enterprise and startup arena — are further driving this growth. Sadly, in a world where security breaches at large organizations dominate the headlines, the ambiguity that encloses cloud computing can make securing the enterprise seem daunting and a few organizations are still apprehensive and not able to maximize the full value that the cloud offers. And some businesses still remain apprehensive. Common Concerns In no particular order, businesses hesitant to adopt cloud computing are often concerned with: Security. By far the biggest concern. Are you afraid that your data will not be as safe in the cloud, as it is in on-premise systems? Control. Do you feel that you will lose control of your data if you move it to the cloud and it’s more assuring to know that you have it nearby? Compatibility. Do you fear critical applications will not be compatible with cloud computing solutions? A Passing Fad. Apparently, Do you strongly feel that the cloud is just another passing phase? Put your doubts about the Cloud to rest Cloud is undoubtedly a way for your organizations to cut down your operational cost and streamline your business process. However, before jumping on a bandwagon, it is better if you look at some of the key benefits of transitioning to the cloud: Cloud is secure: Surprisingly, according to Gartner, through 2020, public cloud infrastructure as a service (IaaS) workloads will suffer at least 60% fewer security incidents than those in traditional data centers. While 60% of organizations that implement relevant cloud visibility and control tools will experience only one-third fewer security failures by 2018. Needless to say that the cloud is more secure than traditional approaches. Reduced cost– A study commissioned by Cisco shows that on average, the most “cloud advanced” organizations see an annual benefit per cloud-based application of $3 million in additional revenues and $1 million in cost savings.  These revenues boosts have been largely the result of sales of new products and services, acquiring new customers faster or due to accelerated ability to sell into new markets. Decreased headcount: With significantly fewer servers to look after, and with standardized platforms, you will subsequently find you require fewer IT staff. In fact, many organizations figure out that they can reduce their staff maintenance by 50 percent. Quicker deployments: Cloud may or may not have a drastic impact on application performance, but in just about every case, you’ll be able to get them up and running much sooner. Creating—and eliminating— environments for new applications is a much faster process, allowing your development team to use their time most efficiently. Improved Agility. Cloud computing drastically increases application delivery as there’s no associated waiting time to access or allocate the infrastructure. Subsequently, by embracing continuous delivery and cloud DevOps, your business can significantly improve its agility. 20%+ faster time to market for new services 50% fewer application failures and faster recovery time (in 10 minutes or less) 30% more frequent new code deployments and a 38% improvement in overall code quality High Availability: The complete cloud computing facilities are routinely protected from system failures and outages using redundant network switches, servers, and storage facilities. In particular, by leveraging off-site backup and redundant servers and storage facilities make these well-equipped cloud computing facilities less vulnerable to disaster or malicious attack. Fewer servers: Moving infra, application, and platforms to a cloud model can undoubtedly help you with enormous savings, as you can stand down or redeploy servers that were previously hosted applications now moved to a shared model. Final Thoughts It’s time to try the cloud! Legacy systems often prevent responsiveness and derogate service levels and a lack of speed or agility often results in inconsistent and disconnected experiences for users, partners, and employees. Moreover, aging systems should not prevent you from harnessing digital technologies. However, the big question that often worries every business is what should and what shouldn’t be moved to the cloud. The answer has proven to be remarkably simple. Everything is potentially cloud-able – bizarrely, even mission-critical survival solutions like disaster recovery. The need of the hour is to focus on delivering solutions faster to meet customer demand in today’s hyper-competitive market and make a big difference. Stay tuned for Part 2 blog post on Application Modernization and Cloud Connection of our Cloud Computing blog series!!!

Automation Solution for Network Calls Validation

Requirement Traditionally all the network calls related testing has been performed manually. A proxy was created for the device/app and then manual verification was done for every parameter for the various calls generated for both HTTP and HTTPs throughout the application. Because there were more than 150 calls, with every call having more than 30 parameters to verify, the manual effort required to validate these calls was quite high and sometimes prone to manual errors. Thus, there was a need to develop an open-source packet analyser that could automate the validations of any type of network call, supporting both HTTP and HTTPs protocol, and on any type of application (mobile web, app, and desktop based). The focus was on implementing a solution that could easily be integrated with any of the popular automation tools and techniques, and in turn, could be easily used, modified, and maintained per the need, and that additionally supports network throttling and analysis of base64 encoded network calls. Implementation Approach Our team at Tavant developed the framework/solution using Browser Mob Proxy (BMP), an open source tool, to achieve automation for network traffic validations. The developed framework consists of the main layers below: Test Case Layer: Test cases are developed using appium/selenium automation tools based on Java. Implementation Layer: This layer consists of the re-usable methods, utilities, appium and selenium/webdriver APIs and the BMP server. The BMP server is available in two flavors: embedded and stand-alone utilities. The embedded version is mainly used for selenium-based desktop web network call validation/automation, whereas the stand-alone version can be used for creating a proxy to be used by any 3rd party medium/applications like the mobile web and mobile apps. The BMP server creates a proxy to route and captures the traffic from the application to the internet and further export the performance data as a HAR/JSON file. The BMP server uses certificates to be deployed on mobile for capturing SSL-based network calls. Click here to understand how the BMP server works completely. The framework also implements a common object repository to maintain the objects and their types at a single location, which makes test case scripting easy and maintainable. Execution Layer: The framework initiates the test case execution two ways: test rail and command line. The tester needs to provide the platform and application information based on which execution is triggered. Every test case initiates/launches the BMP server for any functional verification, and quits once the generation and analysis are complete for that scenario/network call. This takes cares of the unique call validations generated with different scenarios. Test Data: This is the expected data that needs to be validated for network calls stored primarily in the form of Excel. Reporting: JSON parsers are developed to parse the HAR/JSON files generated using BMP and were compared and validated with the expected/test data. The detailed test results are stored in the Excel sheet and pushed back to the test rail. The framework can be easily integrated with different 3rd party tools like Jenkins, TestRail, Github, etc., depending on the project requirements. Further, test reports can be easily integrated with the test management tools. It also supports the analysis of encoded calls using base64 decoding techniques. Tools and Technologies BMP Server, Appium/Selenium-Webdriver, Jenkins, Maven, TestNG framework, Github, Test Management Tool, etc. Challenges Faced The curl command, used for generating the HAR data for network calls, was not providing the data consistently. So, we used some of the options/switches provided by curl and stabilized the network call generation process. We faced issues with generating network calls for mobile-based applications, which got resolved by deploying SSL certificates on mobile devices. Benefits Reduces the manual effort substantially to test the network calls/traffic routing to the app Provides a common end-to-end solution to analyze all types of network traffic (videos and non-videos) for various applications Integrates easily with any automation tools and technologies Enables reusability of test scripts/methods Saves significant effort in test case creation/update Keeps testers from having to resolve framework level complexities Provides an easily modifiable framework Delivers a solution that is stable, efficient and robust enough to drive the complete end-to-end approach with minimal maintenance Provides a solution that can be supported on any of the test environments and network settings Provides a solution that has active support for various online forums

To Risk It or to Identify and Fix It? What’s Your Take?

What is a risk? Risk is a potentiality of failure. When the risk is discussed in the software industry with respect to delivery, it is either attributed to project risk or product risk. How do we avert risk from our project delivery? This can be taken care of by the testing team, but how? With today’s dynamic market, where the technology is made obsolete by a better technology being developed, Time to Market has become most vital. To meet these shorter time spans, organizations are adopting agile concepts. Iterative models are being followed for incremental deliveries based on the priorities defined by the stakeholders. The answer to all this from a testing perspective is risk-based testing. Identifying and mitigating risk play vital roles. When was the last time you approached testing using a risk-based model? This is possible by designing a test plan that aligns with delivery and operations. Risk-based test management is the solution to achieve timely delivery, focusing on business-critical requirements. The methodology that provides an evaluation of requirement risk (business risk or technical risk) as an input to test planning is a full-lifecycle proposition. Sometimes it becomes difficult and taxing to identify the potential risk(s) as things might not be evident and straightforward. Locating the upfront risks is as important as contemplating the potential risk(s) based on the market in which our client operates. Potential risks can be the technologies involved, the current competition and potential competition, and the possible security issues around the flexibility and limitations of the software being designed. The substantial uncertainty that may occur in the future can endanger the project objectives. All the potential risks are not for the vendor to solve unless if the client provides enough data and looks forward to such consultancy from the vendor. Projects will never be subject to the same kind of risks, so the risk management exercise should be conducted each time. Nothing remains constant and risks change over time; hence, the need for organizations to forecast and assess the potential risks before the critical decisions are made. There are two dimensions to potential risk. We can qualify the risk as well quantify the risk. If we have to analyze something we need to know the volume of occurrence as well as the level of impact. If we prioritize the identified risk on a scale, considering the probability of occurrence against the level of impact it can have on the project, particularly on the key attributes of budget, schedule, or quality — this is a qualitative approach of analyzing the risk. Now, this prioritization is in turn consumed, and additionally, highly processed data is used to quantify the probability of the high-priority risks numerically. This acts as input to make decisions amidst the uncertainty; to verify the alignment towards specific project objectives; and to compute the achievable margins, release date, and the scope. This is a quantitative approach to analyzing the risk. Identifying and Analyzing Risk – There are certain methods that can be used to identify the risk and impact, and for analyzing the probability of recurrence based on past data. Cause and Effect Matrix. This is a useful method for the root cause analysis conducted at the end of the project delivery. To identify the possible causes, the participation of all stakeholders is essential for brainstorming and forming a Fishbone diagram. Assigning scores to each of them helps to understand which activities created the risk and the critical steps present in the process. Why and how? Control Manage Cause Controlling the risk cause Pre-impact recovery planning and preparation Cause-Effect Linking Delinking the cause and effect Identifying post-impact recovery measures Failure Mode Effect Analysis (FMEA). It is a systematic and qualitative tool, widely used in early development cycles for analyzing potential reliability or quality problems. FMEA is measured by 3 factors: Frequency: Tracked on a scale of 1 to 10, indicate how frequent a discrepancy is likely to occur. Severity: Factor that determines the possible impact on the client. Detection: The probability of the discrepancy event getting detected.   Prioritization based on the 80-20 principle proposed by Pareto is done for each of the criterion identified. Risk Control. This method is used to define an acceptable level of risk for an organization. Senior management sets this by having thorough discussions with the stakeholders. Once the risk tolerance level is earmarked for the organization, called Risk Appetite, the assessment is carried out to identify if the risk foreseen is exceeding the defined value. If so, mitigating actions are taken accordingly. Respond to Risk. This is more of a corrective-action-taking method to mitigate risk and eliminate what has gone wrong in an effective way. These are the most effective actions to take towards a potential risk: Accept: To perform risk assessment, not do anything, and continue the same way, which is accepting the risk. Avoid: To identify the risk and prevent it by not taking part in any risk-causing act. Transfer: To avert and transfer the risk to a different entity altogether, if possible. Mitigate: To mitigate risks by adding suitable controlling measures or by manipulating the risky behavior by modifying its probability or at least the level of impact.   The main aim of risk mitigation is to reduce the probability of occurrence to a manageable level of impact. The process is structured in the below steps: Discussing the probable controls. Measuring benefits. Estimating the associated cost. Evaluating the resultant probability. Effect and residual risk.   So, in short – Identify the critical blockers as quickly as possible (at the lowest price). Target the business-critical area first and provide confidence to the business. Justify testing effort + cost of business and technology risks.   The solution as a Tool to Manage Risk – Taking the current market into account, there is an acute requirement for a tool that could at least do the following activities, while managing the risks and helping us with the risk-based test management: Synergetic Review and Feedback – A platform to have collective review and feedback by all the

Doing the Analytics Right for Video Platforms

Today, almost half of the population in the US streams Over-the-Top (OTT) content directly to their television for an average of 1 hour 40 minutes daily. Users are getting inclined toward having an increasing number of live experiences for sports and news content. OTT has inevitably unfolded into a multi-million-dollar industry and it is set to grow at 17.2% CAGR to 2020. Most of the OTT platforms provide video metrics out of the box. However, the broadcasters, content providers, and advertisers face inimitable challenges when they try to holistically comprehend the performance of OTT content, target the right segments and formulate an effective business strategy. Also, compiling data from numerous data sources to make meaningful insights can get quite exhaustive and time-consuming. The existing platforms fall woefully short to help uncover intelligence and insights to drive effective business outcomes. The key stakeholders for this OTT analytics comprise of 1) Advertisers – who want to get associated with the right content and target the right audience segment 2) Content Providers – who want to understand and invest in ideas  comprehending audience behavior and interests 3) Marketeers– who want to be enabled with the right audience to succeed in their marketing initiatives. There are various challenges faced by these stakeholders while analyzing the OTT content. How to retain and increase subscriber growth rate? Who is watching and how are they getting disengaged? How do we measure the performance of content across audiences? Which region drives the most engagement for the content? How can the audience be segmented to offer personalized programs or ads? Which platforms provide the best ROI? How do we effectively market the content? To overcome the above challenges, we need to start with access to quality data. Lack of quality data is the biggest challenge in data analytics. Initially, broadcasters’ view was limited to Nielsen ratings from sample audience data. However, data collection has expanded progressively with the launch of streaming services like YouTube, Netflix etc. and now spans to thousands of parameters of metadata collected. The clickstream data has transformed into big data. Data that gets generated directly from the video platform is the key data for content analytics. This is also called first party data. In addition to this, the second and third-party data collected from data management platforms help to provide correlations and enrich the analysis. Once we have access to the right data, we can perform predictive analysis engaging techniques like data mining, statistical modeling, and machine learning to take the data to the next logical level. We can create a dashboard that can help in formulating the content strategy, promotions, personalization of content, etc. The success of any OTT solution lies in being able to decipher customer behavior and optimize omnichannel marketing efforts to explore better business direction, garner personalized insights through segmentation and predictive modeling to boost operational efficiency and extract value from copious data for smart decisions. It should also be able to automate data aggregation and empower us with better decisions. The objectives of the OTT solution should be clear. The approach, cost, and complexity will vary based on the objective. We can get profound insights by applying techniques like Machine Learning (ML) and Artificial Intelligence (AI). We need to remember that, data may not contain the answer, but if you torture it long enough- it can tell you anything. Garnering data is not the end objective; neither the reporting or building of dashboards. Rather, it all starts with asking the right questions. What are you looking for? What is it that you want the data to answer? Only if you have the right question, you can derive answers from the available data. We’re listening.  Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. OR Please add your thoughts, ingenious analysis and novel strategies in the comments section below. We look forward to hearing from you.

Can Dynamic Pricing Work for the eCommerce Segment?

Dynamic pricing, a strategy which enables businesses to provide flexible prices for products and services is now catching on across hospitality, retail, travel and entertainment industry segments. Whether the aim is to stay profitable, fill up an airplane or sell as many sports tickets or products online as possible, companies today are using dynamic pricing to achieve their business goals. While this model has been in existence for several decades, it is only now that is gaining momentum, and is likely to grow more pervasive in the years to come. How effective is dynamic pricing? In 1978, the airline industry in the U.S. was deregulated and this gave companies the freedom to follow different pricing models. Some companies adopted a dynamic pricing model, and were quite successful. Other companies held on to the standard pricing model and tried to find loopholes in their competitors’ marketing strategies. Many of them went bankrupt! Does this essentially mean that the company which offers the lowest price for a product will win over their competition? Fast forward to the 2000s when Buy.com used a dynamic pricing strategy which relied on a software agent to search its competitor’s websites for competing prices, and in response, reduced its own prices. This approach helped Buy.com gain significant customer traction, but its profit margins suffered. To summarize, it is crucial to arrive at a balance between having competitive prices and maintaining healthy margins. Some pertinent questions to be asked when considering this model are: What should be the cost of the product? What should be the duration for an offer? And, How to arrive at that point?   The answers to the questions above depend entirely upon the individual businesses and their respective products. This is because inventory, demand and competition are individual attributes which differ from product to product and company to company. Nevertheless, in general terms, the factors which might drive a company to opt for dynamic pricing are: Sectors with relatively high start-up costs compared to operating costs Sectors with finite markets, i.e. markets with finite time horizons, finite seller inventories and finite buyer population   This model has actually helped industries with high perishables like the airline industry, sports ticketing companies etc. to improve profit margins. If it works for others it should work for eCommerce too, right? The eCommerce industry is not a finite market and it does not have a finite time horizon, finite seller inventory and finite buyer population. Also, start-up and operational costs are considerably lower for eCommerce companies because of technological advancements. So, the question is, does the eCommerce industry really need dynamic pricing? The answer is `yes’ and, in this case, the business’ goals might not be tied entirely to improving profit margins, but also to build a unique brand identity and gain a competitive edge. The good news is that customers have reacted well to dynamic pricing models over the years, as seen in the deregulated airline industry where the technology is perceived as offering lower prices in many situations. In summary, by implementing inventory-based, data-driven, game theory or, simulation models, eCommerce companies can capture the volatile internet market and get to a consumer-centric, product-specific dynamic pricing strategy.

Machine Learning in Lending Summit Recap and Key Highlights

Last Wednesday, September 27th, we at Tavant hosted the first ever Machine Learning in Lending Summit at the JW Marriott in San Francisco Union Square.   This was an exclusive leadership summit – invites were extended to key executives in the mortgage and consumer lending industries.  This one-day summit consisted of keynotes, workshops, a panel discussion, and interactive sessions that showcased the practical applications of Artificial Intelligence and Machine Learning in the mortgage industry.   The summit began with a welcome address by our CEO, Sarvesh Mahesh. Next on the agenda was R.V. Guha, a renowned scientist, who spoke on accelerating digital transformation with AI and empirical modeling.  He began his keynote by defining what exactly the buzz is around data science and the importance of empirical modeling.  While analytic models have limitations, empirical modeling has had a lot of success in the past decade.  He continued on to state that “datasets drive research” and deep dives into the varieties of data sets, available databases, current resources (i.e. Schema.org), and proposed future solutions (i.e. datacommons.org).  Key takeaway:  Empirical modeling is for complex systems what calculus is for classical engineering.  This new class of models can handle complex phenomenon that has a significant social and behavioral component. The next speakers featured Manish Arya (CTO, Tavant) and Aseem Mital (Tavant Founder), who had an interactive session on Applications of Machine Learning in Lending and how these applications and concepts can be applied to the mortgage industry. Prasun Mishra (Senior Director, Tavant) and Harsha Naidu (Director, Tavant) led the Lending Club Workshop which demonstrated a general approach for creating decision models. Prasun and Harsha used publicly available Lending Club data and created a stepwise approach that used Machine Learning to develop a credit risk model and predict loan performance.  They also introduced supervised learning techniques. Next up was an engaging panel discussion featuring Robert Carpenter (Principal in Technology, CoreLogic), Nick Stamos (CEO and Co-Founder, Sindeo), Brian Pearce (SVP, Wells Fargo), Ronald Olshausen (Managing Director, HedgeServe) and Gabe Minton (CIO, Guild Mortgage).  The panel provided key insights into problems and challenges that businesses currently face with AI and Machine Learning in respective industries. The final session featured Mohammad Rashid (VP, Tavant) and Matthew Wood (Senior Director, Tavant) who discussed blockchain 101, applications and case studies, and how blockchain technology is disrupting industries globally.  Key takeaway:  Overview of the Tavant digital mortgage landscape, and how to disrupt the mortgage process and lifecycle. The summit concluded with a closing session presented by Hassan Rashid (CRO, Tavant). The summit was highly successful and attendees found the content thought-provoking and valuable.  We wanted to express our concerns with how AI and Machine Learning were being applied in other industries at a rapid rate, but companies in the mortgage industry are falling behind by not utilizing the newest technologies.  We wanted to demonstrate to senior leadership that it is now easier than ever to apply AI and Machine Learning in the mortgage industry.  It is imperative for companies to apply this technology, accelerate innovation, and strengthen their competitive advantage.  The summit concluded with innovative and disruptive ideas that senior business executives were able to take back to their respective organizations. Watch a recording of the live stream of our Machine Learning in Lending Summit here. We’re listening.  Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. OR Please add your thoughts, ingenious analysis, and feedback in the comments section below. We look forward to hearing from you. FAQs – Tavant Solutions What machine learning insights did Tavant share at recent lending summits?Tavant presented breakthrough applications in predictive underwriting, automated document intelligence, real-time fraud detection, and adaptive risk modeling that are transforming lending operations and customer experiences. How does Tavant stay at the forefront of machine learning innovations in lending?Tavant invests heavily in R&D, participates in industry conferences, partners with academic institutions, and maintains innovation labs focused on emerging ML applications for financial services and lending automation. What are the latest machine learning trends in lending?Current trends include explainable AI for regulatory compliance, federated learning for privacy, automated model governance, real-time decisioning, and the integration of alternative data sources for more inclusive lending. How is machine learning changing credit scoring?Machine learning enables dynamic credit scoring using alternative data, real-time updates to creditworthiness, more accurate risk assessment, reduced bias in lending decisions, and personalized credit products. What machine learning applications are most valuable in lending?Most valuable applications include automated underwriting, fraud detection, customer segmentation, price optimization, default prediction, and document processing that significantly improve efficiency and accuracy.

Improving Buyer Experience through Customer Data

Gain more insights into the aftermarket space to improve how a customer feels – every time you send an offer, provide a service, or advertise a new product. A digital registration platform integrated with analytics can make an organization’s customer-experience strategies powerful.  Organizations often lack one-on-one interactions with the most important stakeholder in their business — the customer. Businesses need to capture detailed customer expectations of the products they use. When businesses understand those expectations, they will understand the users’ experience and know what proportion are going to become brand ambassadors. Registering products after sale requires just a mobile device. Even without one, a simple online data-capture form links the organization to a host of information, which can be used to improve customer experience in the aftermarket. However, to add value throughout the product lifecycle, companies need to interact with customers, analyze the data, and determine what customers want and what they may need in the future. Then companies need to respond to that data and improve services and new products accordingly.  I think the part about a mobile device is unnecessary. Why a Digital Platform? A digital platform cost effectively streamlines omnichannel communication. Using the data, you can inform customers of the latest mix of offers, insider information, warranty liabilities, and product-use tips — exactly how the customers prefer. This increases customers’ tendency to rely on one brand. Capabilities to integrate the product-registration process with analytics gives an organization the power to improve customer experience holistically. A multi-channel brand engagement model is the foundation for cross-selling and improving customer confidence and brand loyalty. Stressing Simplicity The importance of convenience can never be under-estimated in a customer-facing process. The registration, and all the activities thereafter, should be designed accordingly. A single digital platform at the OEM’s end that allows the customer to interact with the company is increasingly being considered. After the sale, a customer should be able to register the product by QR code or bar-code scan, social media plug-ins, a mobile app, or a website. These channels provide the company with a data set, which includes valuable personal information, feedback, and customized tips on benefiting from the product. The communication from the OEM’s end may differ depending on age, location, product details, etc. Analytics and Automation Software capabilities to use the information gathered are vital. Analytics helps an organization use registration-level information to deliver improvements and customized information. From identifying issues customers may have to cost-effective changes in product design, analytics plays an instrumental role in converting data to insight-driven actions. With simple dashboards supported by back-end intelligence, a product-registration platform, integrated into the rest of your technology setup, improves brand image. The AI-automated, customer-facing activities align services and products to market preferences. Organizations need to ensure simplicity in the customer’s registration platform interface. The challenge with that is striking a balance between respecting the customer’s time and maximizing the feed for analytics. The value generated each time is directly proportional to the depth and accuracy of data discovered. Interactive data also plays a vital role. With data backing your organization, you can maximize the engagement through the right content and improve insights in the long run.  

Chatbots and Their Role in Consumer Lending and Warranty

I joined Tavant in the Mobile Team and soon got an opportunity to get my hands dirty on Chatbots. As we all know, Tavant is a leader in providing services in Consumer Lending and Warranty, I decided to pursue a chatbot for each of these use cases. I plan this series over 3 parts, where towards the end we will have a fully functional chatbot Mobile app. Let me first define a Chatbot. How do they work? How do I build my own ChatBot? How can I use chatbots to help my clients? I’m sure these questions are running through your mind since chatbots have gained popularity and occupy a niche space in user engagement. You might have already interacted with chatbots on social media and not realized it! Now is the perfect time to delve into this exciting world of chatbots. So what is ChatBot? A ChatBot is a program that simulates human conversation or chat through Artificial Intelligence. Typically, a chatbot communicates with a real person to provide the services like customer care agent, e-Commerce or any Hotel/Cab booking, etc. The chat interface can either be Facebook Messenger, Twitter, slack, or even your own custom chat messenger. Let’s see some examples of ChatBots that are currently available. Example:  1. Book a cab: Want to book a cab? Don’t have a cab booking application on your phone? No problem! Uber has recently launched their Facebook chatbot, which helps you book your Uber cab without installing their app. Experience the chat by clicking here. 2. Ordering a pizza: Hungry? Want to order a delicious pizza? Just use your messenger to chat with a Dominos bot. You can place an order, track your order, and cancel your order by just chatting with a bot. https://vimeo.com/179171202 3. Weather application: These bots are designed to get the weather report of your location. They can also suggest whether you should bring your umbrella before you leave your house. https://www.youtube.com/watch?v=m5ViHWRo9KU 4. News bots: These bots allow you to be updated on your News. You can subscribe to your favorite news topics or you can ask for the latest headlines on a specific topic. https://www.youtube.com/watch?v=8iwxWU-8cuM 5. HDFC Bank OnChat: This is a facebook messenger bot, which helps you recharge your prepaid and postpaid bills, book event tickets, book cabs, etc. https://www.youtube.com/watch?v=6bnKhqZmdsw You should definitely do some research to get an idea of how chatbots are used by companies to provide better customer service. Why chatbots? You must be thinking “Why is this sudden popularity of chatbots in an enterprise?”. Let me explain… According to “Chatbots magazine”—“This is for the first time ever people are using messenger apps more than social media apps. So logically, if you want to build a business online, you want to build where the people are. That place is now inside the messenger apps. This is why chatbots are such a big deal. It’s potentially a huge business opportunity for anyone willing to jump headfirst and build something people want.” So now that you understood the importance of chatbots, the next question which comes to your mind would be “How does a chatbot respond to your message and does the business for you?” To understand their functioning better, let us first see the different types of chatbots out there. Well there are 2 types of chatbots. 1. Chatbot that functions based on a set of rules: This type is limited to certain transactions. It can respond to only a few specific commands based on a decision tree, which a developer may have built. It has limited to no understanding of natural language and contextual meaning. If you say something vague, it doesn’t understand what it is.   2. Chatbot that functions based on Machine Learning: This bot has an artificial brain called as artificial intelligence. You don’t have to be specific. It also understands the natural language along with commands. This bot continuously gets smarter with previous conversations.   Bots are created with a specific business purpose in mind. For example, an online store is likely to create a chatbot, which helps you purchase something. UPI apps like Paytm is likely to create a bot for bills payment, mobile recharge, etc. Artificial intelligence: The next riddle you must be thinking over is if bots use artificial intelligence to make them work, isn’t it hard to do so? Do I have to be perfect in AI? The answer is NO, you don’t have to be an expert in AI. Now, we have many readily available AI solutions that are backed by giants like Google, Facebook, IBM to name a few. Things to do for building your first chatbot Figure out what business problem you are going to solve with your chatbot. Identify the right messenger platform(s) like Facebook, Twitter, or design your own interface in an existing mobile app. Set up a server to run your bot. Choose a service to build your bot.   Time to see an example of a Chabot in Warranty Management System I went about writing a chat interface in one of our Mobile App for Warranty and was pleased by the final outcome. The chat feature in my mobile app got the user engaged longer than my normal screen flows to complete the same task. Let’s consider a use case where the mobile app has to provide a feature to the user for creating a service request, collect feedback for the last service and allow the user to register complaints and suggestions. Users can also request information about their next service appointment. Also, I built a service to provide guidelines/help from an agent, etc. You can do all this by chatting with a bot. It is an easy, automated, secure way of executing these business services rather than the old traditional way. It’s cool. Isn’t it? I have posted the demo for the same video here Now let’s see the example of Consumer Lending application. The user can ask your assistant bot about home loan services and its eligibility criteria. He can also request for a home loan. And

The Countdown is On for UCD and HMDA

Regulations are constantly changing in the mortgage industry. Lenders are under continuous pressure to meet fast approaching deadlines on UCD and HMDA. The Uniform Closing Dataset (UCD) is a standard industry dataset enabling information on the CFPB’s Closing Disclosure to be communicated electronically. The first deadline of 25 September 2017 mandates lenders to deliver borrower data and Closing Disclosure in the UCD file. UCD improves loan quality through increased data accuracy and consistency. This is of interest to the GSEs as it enhances the loan’s eligibility for sale in secondary markets.The year 2018 brings updates to the HMDA. The new HMDA rule requires over 48 data points to be collected, recorded and reported. This includes multiple new data points and a few modified from the previous rule. New fields include credit scores, CLTV ratio, DTI ratio, detailed demographic data etc. The CFPB asserts that the changes improve the quality and type of data reported by financial institutions leading to greater transparency. The updated regulations bring a new set of challenges to lenders. Investments in technology systems and processes can potentially increase the ever-rising loan origination cost. Data privacy and security is another concern. With the increased number of data fields, protecting sensitive borrower information is a priority. Additional data can also be used in fair lending claims thereby increasing litigations risks and costs. Since 2008, the mortgage industry has been taking giant strides in improving data reporting and compliance standards. TRID rule impacted the industry at almost every point along the transaction, and UCD/ HMDA will change the way data is collected, recorded, reported and delivered.Over the years, Tavant’s mortgage expertise has helped lenders implement regulatory changes with cutting edge technologies. In 2015, we helped multiple lenders achieve TRID compliance ahead of schedule. In 2017, we are doing the same with HMDA and UCD. It’s time to achieve Accelerated Compliance with Tavant Testing. The countdown is on!To learn more about our testing solution please visit: UCD/HMDA Compliance Testing by Tavant FAQs – Tavant Solutions How does Tavant help lenders prepare for UCD and HMDA compliance deadlines?Tavant provides automated compliance management systems with built-in UCD (URLA Conversion Deadline) and HMDA (Home Mortgage Disclosure Act) reporting capabilities. Their platforms automatically generate required reports, track compliance metrics, and provide audit trails to ensure lenders meet regulatory deadlines and requirements. What specific UCD and HMDA compliance features does Tavant offer?Tavant offers automated URLA form generation, HMDA data collection and reporting, compliance dashboard monitoring, exception tracking, and regulatory change management capabilities. Their system ensures accurate data capture, timely report submission, and comprehensive documentation for regulatory examinations. What are UCD and HMDA requirements for mortgage lenders?UCD (URLA Conversion Deadline) requires mortgage lenders to use the redesigned Uniform Residential Loan Application (URLA) form. HMDA (Home Mortgage Disclosure Act) requires lenders to collect and report detailed mortgage lending data including borrower demographics, loan terms, and decision outcomes for regulatory analysis. What are the penalties for UCD and HMDA non-compliance?Non-compliance with UCD and HMDA requirements can result in regulatory fines, enforcement actions, reputation damage, and operational restrictions. Penalties vary based on the severity and duration of non-compliance, with potential costs ranging from thousands to millions of dollars. How can mortgage lenders ensure UCD and HMDA compliance?Mortgage lenders can ensure compliance by implementing automated data collection systems, regular staff training, internal audit processes, compliance monitoring tools, and working with experienced compliance technology providers who understand current regulatory requirements and changes.

Incur Lower Warranty Cost While Generating Greater Revenue

In dealing with warranty packages, organizations around the globe are known to suffer leakage, of thousands of dollars, due to unoptimized claim-processing loopholes. It is advisable to improve how you administer and track all your warranty claims holistically, leaving zero room for false claims. To be able to improve profits, a closed-loop warranty system is necessary. Customer satisfaction is the key. Every organization strives for it in innovative ways. But the best hassle-free claim processing experience customers can get when you have a robust warranty-operations controlling system. Rules-based technology Most of the time, organizations tend to bleed valuable revenue through fraudulent claims, as they have limited control over warranty operations with rigid and worn-out systems. Therefore, it is advisable to implement IT for it to function according to business rules and organization policies accurately. A typical claim process starts from submission, pre-approval, claim evaluation, final approval, and disbursement. The entire cycle needs to be automated along with the incorporation of an EWS (early warning system) to capture real-time cost drivers. Field-asset tracking and management As business dynamics have grown complex, organizations need a system to facilitate field-asset tracking and management better. It is the most important part of warranty management, as it keeps account of the replaceable parts at any given point of time. That, in turn, helps the organization to make provisions in the accounting books to allocate funds and other resources optimally. Orchestration with real-time data For businesses to reap maximum benefit from warranty software, they need to implement a technology which perfectly syncs dealers, service centers, suppliers, service providers, and of course, the OEM itself. Orchestration of the sort makes it possible to share real-time data on business activities across global locations and assess the associated warranty liabilities. An integrated data-driven workflow is essential to limit warranty spend and enhance customer satisfaction. A cut in the warranty spend, in turn, can release more funds, which a smart organization may use to improve product quality through investments in expertise and state-of-the-art R&D facilities. Final thoughts Organizations can use automated analytics and reports to re-evaluate their spend analytics from time to time. It will help them create room for extra savings. After all, the ability to improve aftermarket services in the manufacturing sector depends on how well you optimize other costs. Claim processing itself can be cumbersome and incur massive overheads, with tons of unmanageable paperwork and countless phone calls. Such processes are nothing but revenue killers, which can be streamlined to help focus on productive operations. It is important to stop possessing expensive workforce and spend less time on processes—an attainable goal for manufacturers, which is fast becoming a necessity at all altitudes of the industry.

Extended Warranties: A Retrospection

In today’s competitive business environment, organizations are concentrating not just on sales but the aftermarket promises – made in the form of extended warranties. While people have been reading the abundant material, written by skeptics, on why extended warranties might do more harm than good, efficient contract management has proved to be the key to retaining and expanding customer bases for many manufacturers. There is a growing need for organizations to imbibe technological innovation into their culture. The current drive is to automatically manage rules for payouts, contact deadlines, rates, replaceable spares, and so forth. A better customer management process can only be achieved with an end-to-end support system in place. What customers like Customers relentlessly crave for more – every time and are willing to spread the good words about your brand if you make them feel that warranty claims are a cakewalk. Organizations, thus, use intelligence-driven claim-submission modules for customers to avoid unnecessary interactions. Extended warranty holds the key to OEM profitability, and in the long run, helps achieve deeper market penetration with smart, dynamic pricing. It is a general observation that a field asset may not be replaced entirely, especially if the spare is not included in the warranty. That is where you can create the scope for a customer to benefit from the power of dynamic pricing while offering extended warranty. You can offer to replace the spare for a one-time payment, which optimally covers the cost and doesn’t create a burden on any of the sides of the business. Thus, extended warranty helps attract more customers and improves ROI, while increasing your aftermarket profitability. Offering what customers prefer With artificial intelligence and machine learning enabled features like new quote management, OEMs and dealers can reach out to customers with new offers and promotions. Preparation of such offers can be a daunting task for sales teams, but with smart technology, they can progress efficiently. In this way, customers can benefit from real-time pre-approved discounts, pricing updates, the latest products and services, and more. Organizations are looking for platforms which can deliver such information in a customized way and increase the real value offered to customers. Extended warranty a common practice Decentralized operations of extended warranty have reaped benefits for organizations as well as customers. It is standard practice these days that dealers or distributors offer extended warranties in addition to standard warranties provided by OEMs. This approach creates value and captures market share by extending goodwill toward the market. And, finally It is imperative for OEMs to monitor their internal policies continuously and maintain their command over operations. That would prevent leakage through fraudulent claims and add to customer delight. Organizations require a flexible technology arm, which can modify, cancel, and alter contracts as per client demands, but in line with the organization culture. Get to know more about ideas and thoughts from a team that is passionate about delivering artificial intelligence and machine learning solutions that impact customers’ core businesses. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.

HVAC Profitability: Defining Destiny

HVAC users are spread across the globe, living in diverse climates with increased maintenance requirements. The service contract for each machine often excludes vital parts, and that can be a cause for great customer grievance especially before certain seasons. Service charges also ply, as manufacturers are unable to provide free labor to their sizeable markets. As in many other sectors, the HVAC aftermarket can feel burdensome, leaving little scope for CXOs to launch new offers and improve services. Need of the hour  HVAC manufacturers need to plan for improving aftermarket satisfaction at a competitive cost meticulously. Predicting service requirements, utilizing resources cost effectively, and finding better techniques to improve product life are essential for that purpose. It can lead to long-term advantages, as more customers are likely to buy from a manufacturer that has a good reputation for its aftermarket services. The good news HVAC manufacturers need not offer free services to the aftermarket but should strive to provide lower costs and better outcomes than their competitors. It requires streamlining the workflow for every HVAC maintenance date in the calendar through early preparation. Customer satisfaction Customers expect maintenance services to lead to lower electricity bills, better durability, improved performance, and above all – peace of mind. Using historical data related to services, you can identify frequent problems, and their root causes to improve future maintenance. It is also important to have seamless data connectivity over a shared platform in your value chain so that you can help prepare your suppliers and service teams well in advance of scheduled maintenance. Customers also want transparency during the service and around the year. A mobile app with customized interaction can live up to their expectations. Value-chain satisfaction As a result of optimizing the workflow based on data-driven analytics, you can ensure a less stressed-out workforce. You can acquire better leads for equipment and accessories by looking at intuitive dashboards conveying and predicting the requirements, based on real-time data, including everything from weather patterns to customer budgets. Staying prepared for service deployment also lets you offer flexible work hours. Social impact The HVAC sector has a tremendous opportunity to make a difference to the global energy spend. While the primary focus will always be on more profits, what matters is the path they choose to achieve it. Service and product design improvements, to control energy spend, can be carried out based on service history. All you need is a technology to convert those huge piles of data into insights for engineers. Sustainable growth will be the result of competitive aftermarket services and how you use the data generated there. If your database has the relevant details, technology can continuously give you the right insights to make your products more reliable, improve savings during services, and market the right offers to the right people cost effectively.

Introduction to Precision Agriculture Technology

Humankind has witnessed great progress, first with the industrial revolution, healthcare, information technology, followed by biotechnology, and more recently the big data revolution. Increased life expectancy, growing population and shrinking arable land have contributed to mounting pressure on traditional agriculture (and somewhat led to dispelling the agrarian myth). The adoption of technology and extensive potential application of information technology to agriculture has led to the evolution of agriculture into Precision Agriculture. Precision Agriculture may be defined as a set of technologies that help farmers to adopt farm operations to manage field’s variability. The goal is to optimize yield (having better nutrient content) and maximize return on investment. Often Precision Agriculture involves satellite farming or site specific crop management based on observing, measuring, and responding to inter and intra field variability in crops.   Steps in Precision Agriculture: The Precision Agriculture cycle can be considered to consist of the following steps or processes: Gather (Data Acquisition) Analyze (Processing the data and transform it into relevant analyzable information) Decide (Analysis of this data for decision making, sometimes via the agronomic model of utilization) Execute (Implementation/Adaptation)   Approaches in Precision Agriculture: Broadly, Precision Agriculture can be practiced in either a Predictive Approach or a Control Approach. The Predictive Approach is when practices are adapted prior to start of the season. The predictive approach steps can be categorized as: 1. Gather—Use historical data, such as weather data, soil data, crop status during a period; as the starting inputs. 2. Analyze—Characterize the agro-climatic context, define management zones. 3. Decide—Adjust the crop inputs, such as seeds and fertilizer source. 4. Execute —Vary the sowing density of seeds; vary the Nitrogen application within the fields.   The Control Approach is when adjustments are made during the season with an aim of optimizing the yield by reducing/optimizing the in-season requirements. The steps in control approach can be: 1. Gather—Monitor crop growth conditions during the cycle. 2. Analyze —Assess current intra-field variability. 3. Decide—Adjust the crop inputs to crop needs. 4. Execute —Vary the Nitrogen application and chemical application within the field.   Tools to Gather Data Precision Agriculture adapts multiple technologies for advancement and betterment of agricultural practices. The tools for gathering the data that can then be analyzed for Precision Agriculture applications are as follows: – GPS to geo-localize the field boundaries, to geo-tag field observations – Sampling (Plant tissue sampling, Soil sampling [texture and nutrients levels analysis]) – Hand sensors and In-vehicle sensors (proxy detection) of crop status (biomass) – Aerial/satellite remote sensing of crop status (biomass, growth anomaly) – Combine (Yield monitoring) Tools to Analyze Data Based on the approach and the type of data gathered for the purpose of Precision Agriculture, processing of the data and transformation into relevant information can be done with the help of information technology and big data processing tools. Tools to Decide On the basis of the inputs from analysis step various models for decision making can be applied. For example, an averaging out approach may aim at averaging the yield throughout the field by reducing/controlling the field variability, this can be achieved by applying or pointing more resources, to regions where the growth lags behind the average field growth. The opposite approach would be to direct more resources (water, fertilizer, chemicals) to regions which already show better growth/performance compared to the field average. Tools to Execute Once the decision has been taken, various tools that can be adapted to meet the goals can be summarized as below. This can be done with the help of tractor combines, robotics or manual intervention. What can be adjusted: Inputs Dose – Seeds – Fertilizers – Chemicals – Irrigation Field Operations (time, fuel) – Tillage – Planting – Fertilizing – Spraying

Being an SFDC Tester

Salesforce is a very hot topic now-a-days in the market. It is a really refreshing change with clean and spontaneous user interface. Testing plays a key role for any application or software, as proper testing of application increases the quality. Typically, salesforce testing of apex classes and triggers is done by the developers themselves as it is necessary to write the test classes (for apex classes and triggers) to move the code from sandbox to production. Developers must ensure that their code coverage is minimum 75%.  But, the functional testing should be done by the QA team to strengthen the quality of application. A lot of things can be done in salesforce via point and click administration which include automating the tasks like – automatically creating the tasks, making the field updates and sending the emails. This makes salesforce so interesting that a tester feels that he is working like a developer. What is the role of QA Team?  Understanding the Business Requirement Document. Brainstorming sessions for functional understanding by the QA team members. Configuring and setting up of data.   Assumption before configuration and data setup:The QA team has basic knowledge and role in salesforce comprises of: Creation of Accounts Creation of Contacts to relate with Accounts Enabling created contacts as External Users Assigning Product licenses to External Users Creation of Internal Users Assigning SFDC and TWOD (Tavant Warranty on Demand) Product licenses to Internal and External Users If we are using Public groups and queues in project, then once users are created, assigning those users to respective queues and public groups. Checking all page layouts All related lists of detail pages All the columns in the related list Creation of test data to perform testing. Working closely with the development team to design, build and test the application Providing the direction for system enhancement and defect fixes Providing the new ideas and information Prioritising and estimating the critical deadlines across the project Providing detailed documentation to business and developer team Organising the training session/demos for the customers   It is not always true that a QA must do the configurations.  If you don’t know the configuration part, you need not worry. Any one member of your team can do the configurations who is having salesforce’s basic knowledge. You just need to set up your data and perform testing. Testing in Salesforce Testing in salesforce includes the following features:  Manual Testing in Salesforce is performed by the QA team which includes happy path testing, functional testing, integration testing, regression testing and system testing Automation Testing in Salesforce can be done by any of these tools available in the market—Provar, AutoRabit, AssureClick, Selenium and QTP. Selenium is the best choice as it is the open source tool. Use the Selenium web driver for automating the browser. Use eclipse for running the selenium code Functional flows report based on status of test cases, where testers are required to create the functional flows to understand the functionality of application Process builders to check the behaviour of the system, by giving different entry and rule criteria  Workflows to check the functionality of time based events   The few challenges that we faced during the testing of salesforce application include: Testing of Visualforce pages through automation. The issue lies in creating field locators reliably on a page. Salesforce will generate the Ids at runtime which means any change to our APEX code, leads to the change of field locators based on Ids which need constant maintenance Writing test cases with different roles and mentioning the settings Some of the standard functionalities although not in use, can’t be removed GUI tests don’t work when we switch the test environment. Automated tests need to work in all our test environments. Field Locators are how you automate tests, find the field or button on a page. There is an issue creating field locators for the Salesforce screens as some field IDs differ between Organisations.   Last but not the least, the most important thing is to understand the Salesforce administration without fail. The beauty of Salesforce lies in its success community. Community is a great way to share the information and collaborate with the people outside your company. There is a help guide which is written by the salesforce admins and developers. One of the biggest advantage of Salesforce is that you can perform testing any time. For this, you don’t need any VPN access.  You just need a web browser with a reliable internet connection. Just sitting at your home, you can do testing.  Salesforce1 Mobile application is also available which is a better way to experience Salesforce using your mobile. Cited below are few links to study the Salesforce Concepts: https://developer.salesforce.com/trailhead/en http://www.djmlab.com/moodle/   -> For salesforce admin 201 certification. Salesforce, Making Life Easy……………     

Solving the Problem of Inefficient Handling of Goods Return with Reverse Logistics

Have you felt that overlooking reverse logistics puts your organization behind others who master the discipline? If yes, it is not you alone, as there are many organizations yet to decide that reverse logistic is a strategic concern. However, it involves adopting the most efficient way of dealing with the process. Reverse logistics is the practice of efficient transportation of finished products from their destinations back to their origin to capture residual value (or salvage value). Manufacturers want to retrieve defective products efficiently from their valuable customers, and IT solutions are a definitive boost in that context. By improving the process, manufacturers can save costs and speed up their service to ultimately promote a positive brand image. It also helps the marketing aspect of business significantly. How large organizations tackle typical challenges To pursue the fresh stock, companies have started focusing on clearing old inventory. However, to do so, there are several hindrances for enterprises. Due to huge sales volumes and extensive customer bases, organizations face the problem of many unidentified and unauthorized returns. Warranty management software establishes total control over such processes by maintaining unique product identification numbers and tracking mechanisms. As all the claims are managed automatically through software, there is no scope for unauthorized system inventory movement without a laid down protocol being followed. Most of the time, organizations are unable to write off a significant amount of returned inventory held in their warehouses. The claim processing rate is far lower than the inward-inventory movement rate, which results from a lack of return agreements with retailers. The latest warranty software are a step ahead in this aspect, as they manage the return policies and protocols laid down right from the beginning of product life cycle. Software capabilities Warranty systems come with cross-functional integration capacity and minimize the claim-processing time, helping improve customer confidence. With high-level integration, the collection of goods from customers can be managed automatically. The software can be synchronized with third-party logistics providers (3PLs). As soon as a claim is processed, an automated intimation is sent to initiate the collection sequence. After the inventory arrives at the organization premises, it is accounted for automatically against a goods receipt note (GRN), leaving no gap for fraudulent claims. With traceability to this extent, organizations can extract maximum value from salvage of the returned goods in parallel to a hassle-free operation. Companies can also re-engineer and reintroduce the product back to the market and derive profit. Meet Tavant warranty experts at Booth #3 at 13th Annual Warranty Chain Management Conference (WCM) on Mar 7th-9th, 2017 in Tuscon, AZ

Do Away with Unwieldy Warranty Challenges: BI & Data Warehousing

Business processes become complicated due to inefficient data sharing. Smart technologies may have brought some improvements with real-time insights about your business ecosystem, but it gets much tougher in the case of an organization dealing with large after-market operations, like in the event of warranty administration and management. A more efficient warranty management system should enhance OEM’s reach to its customers. Numerous factors are affecting the warranty domain. And managing these factors becomes the primary goal for any manufacturing organization. The latest advancements in technology use cutting-edge Business Intelligence (BI) and corresponding Data Warehousing (DW) techniques. These methods are bundled into an advanced management system, which can help you establish more control over your business. Stakeholder experience Business houses willing to maximize their revenue function must empower their stakeholders such as service centers, dealers, distributors and so forth. It is necessary to exploit the capabilities of a linear integrated workflow, which will help reduce warranty costs. Organizations need to aim for maximum customer satisfaction, which can only come from a pleasant, hassle-free warranty claim experience. Automation Technology in this context should be advanced enough to process 90% claims in an automated manner with the help of BI. Such a system can manage complex processes, application data inputs, historical claim trends, data mining, and predictive analysis, and churn out useful results and reports in no time. DW for benchmarking With the use of advanced data warehousing techniques, coupled with robust warranty technology, your organization can keep track of the entire life cycle of the product, which would be beneficial in benchmarking the critical quality parameters. Where does your current technology stand? Does your warranty technology automatically draw references from the claim data with the benchmark data and churn out automatically generated quality reports? That is not the only aspect which is necessary. The technology should aid product development based on claims history as well. Setting up the technology should also be simple. Industrial standards require it to be carried out within minutes and without the support of your internal IT department. A modular, in-house, platform-based architecture should help speed up the integration of the software with your organization’s IT landscape, which might include legacy systems. It should allow cost-effective future expansions as per your business needs as well. It’s all about customer experience! With the use of BI and advanced analytics, your customer can get the power of a mobile-based application, which can be used to make a claim. After a claim is recorded, instantaneously the software should analyze the policies and the procedure for claim disbursement. If it is necessary to summon a field staff, then the message should automatically be sent to the nearest available personnel. Only then can the customer walk away with an enhanced experience, helping create a better brand image and market reach. Meet Tavant warranty experts at Booth #3 at 13th Annual Warranty Chain Management Conference (WCM) on Mar 7th-9th, 2017 in Tuscon, AZ

Gaining Competitive Edge with Connected, Data-Intensive Warranty Systems

Since raw data hardly serves any purpose, it needs to be analyzed. One of the biggest problems with the warranty business is huge quantities of data getting generated every second. This data, coming from various sources, needs to be analyzed. Data makes sense when channeled to specific points of input. Once the data is processed into actionable insights, business units can develop enough intelligence to improve the decision quality. How intensive is data? Data normally comes from business transactions and social media, besides other capture points like sensors. Ideally, reporting and analysis should match the speed at which data is being generated. Data proves useful when various thoughtful points are developed for it to be translated into effective strategies—through real-time knowledge of workflow and market parameters. Data-intensive warranty management solutions                                               The term data-intensive computing is used to describe applications that are I/O bound. Such applications devote the largest fraction of execution time to leverage the movement of data. They can be identified by evaluating “computational bandwidth”—the number of bytes of data processed per floating-point operation.[1] Warranty management applications being conceived these days belong to that category and handle big data to help us understand the core problems. One of the most important goals of such technology is to reduce decision-making liabilities. For good decision making, it is preferable to use data that covers a vast expanse of the warranty ecosystem and enables insights closer to reality. Without big data, quantity and quality of insights become limited to just some transactions and the immediate ecosystem. A warranty management application must be connected to the maximum possible data input points for the best possible analysis. The application should cover all possible operational, monetary, mechanical, and market-oriented data sources such as public records of other competitors, telematics, manufacturing methods, and customer touch points. Gaining customer preference, cutting cost, and competitive edge With the help of a connected, data-intensive warranty management system, it is possible to cut down costs. Once we have surrounded our system with a set of technologies to handle big data, and thereafter enabled warranty analytics, we are able to analyze the causes of product failures and make improvements—eventually cutting the cost borne during warranty services. Data and analytics also help us to predict optimal allocation for warranty reserves more accurately. This results in smoother business operation with accurate budget allocation. Data-capture technology also let us implement customer service through faster electronic medium such as mobile apps and cut a great amount of service costs. In the long run, the right analytics platform helps us facilitating effective monitoring of all activities, including Claims registered per user, in real time. With software improving real-time visibility into our ecosystem and customer-facing processes, the rate of success in decision making is growing manifold. It also helps us in identifying the genuine claims faster with more accuracy. Eventually, generating more values for customers by reducing the endless waiting time for the verification to be completed. The damaged parts can be tracked and recovered, if needed, using radio frequency. And therefore, the supplier performance can be rated accurately. Using such an approach, notifications are timely, allowing value chain partners to identify areas of improvement and realize more value. This helps in improving overall efficiency to bolster the bottom line for a business.   [1] Reagan W. Moore, Arcot Rajasekar, et. al., Data Intensive Computing, (Association for Computer Machinery Digital Library, 2007), Chapter 5.1.

Unlocking Revenue Potential with Header Bidding: What, Why & How

To bid or not to bid? The challenges Consider yourself a publisher who has ad space to rent out on your website. Now the usual process is that your site reaches out to its ad server to get an ad and the ads served first are generally those that are handled by your marketing team. The leftover space is then made available through an ad server in way that is referred to as the waterfall sequence. Unsold inventory is offered to the top-ranked ad exchange, and then, if it remains unsold, it goes to the second-ranked and so on. This process is far from flawless as it works on volumes where the highest bidder gets allotted the best space. Also, several publishers who use Google’s DFP ad server employ a setting that allows its Ad Exchange (AdX) to outbid any of the winning waterfall bidders because AdX gets the last bid. All this adds up to inefficiencies in the entire buying process.  This model thus ignores high-value inventory opportunities and reduces competition. In short, advertisers don’t really get the space they are worth and publishers’ capacity to earn revenues becomes artificially capped. Making way into programmatic buying:  Header Bidding To make operations fairer, transparent and far more effective for all parties, the digital ad industry is delving into technologies that allow exchanges to bring in demand before the ad server call. To achieve this, publishers insert a piece of java code in the header of their site pages. The code then reaches the supply side platforms (SSPs) for bids, even before its own ad server’s direct sales are called. Here the entire process of bidding is simultaneous. Here’s how it works: User requests a website After request, the user is redirected to one or many SSPs User reaches out to one or many SSPs in parallel SSPs conduct auction with DSPs and internal network demand DSPs respond with bids SSP determines winning bid value and returns to the user User passes bid value into ad request and calls Publisher Ad Server Ad server determines final line item to serve and redirects User to Marketer Ad Server User calls Marketer Ad Server Marketer Ad Server returns final creative/advertisement User calls track back to SSP Transparency, visibility & efficiency: Benefits unsurpassed With header bidding, publishers have the option of letting any ad exchange beat a direct impression. This entire process focuses on all available impressions and not just direct sales. Programmatic buyers can now have visibility into publishers’ inventory. Increased visibility into premium inventory greatly increases conversion rate. Also, more accurate inventory insights build better forecasting capabilities to understand the true availability of a buyer’s target audience. By opening up inventory in smaller global markets, or highly specified ones, header bidding enables buyers to achieve campaign goals in a highly targeted and efficient manner. Another great advantage is that programmatic buyers no longer have to depend on Google’s ad AdX and DoubleClick. Google’s dynamic allocation is a huge obstruction for publishers and with header bidding, the latter can bypass this and not depend on AdX to cherry-pick inventory. The future is here Many in the ad industry contest that header bidding increases the load time of webpages for me and many others like me vouch for such advanced technologies that enable visibility into premium inventories and allow for developing disruptive forecasting and predictive models, that can unlock valuable insights into high value marketplace. Header bidding is the future of programmatic buying and the future has indeed arrived.

5 Tips to Survive Dreamforce 2016

It is that time of the year again. In less than a week more than 150000 people will take the city of San Francisco with a storm to attend one of their favorite software conferences, Dreamforce 2016. So what are you to do if you are one of those enthusiasts?  Here are some tips that might help you sail through dreamforce without missing the important activities. 1) Read and study the FAQs diligently before you enter the conference arena. The FAQs on dreamforce website cover everything from badge collection to the shuttle timings. This will save you a lot of time and avoid last-minute confusion. The last thing you want is to end at the wrong location for badge collection. 2) Plan the sessions that you are interested in attending much in advance. With over 2000 sessions in this year’s dreamforce, it is very important that you make optimum use of the agenda builder and mark the sessions that you would like to attend. 3) Preschedule your meetings with key vendors that you would like to meet during the conference. The expo hall is huge and spans across multiple floors. With all the different activities and sessions happening around the event, it helps to have at least 5-6 confirmed vendor meetings on your agenda. 4) Study the trail map before getting to the conference and follow the trail while stopping at the signposts. By doing this, you will not only do a huge favor to your feet but also make sure that you don’t miss the important salesforce solutions. 5) Make sure you download the Uber app. If your hotel is not connected by Shuttles to Moscone Center, it can get quite challenging to get around. To avoid the frustration, Uber app on your phone can come in very handy. Among all the dreamforce enthusiasts, Tavant Technologies will be showing its salesforce solutions in the Meeting room- MR124B. We will also host an Industry Session on ‘Streamlined Warranty Management: What your customers want’ on October 4, 3.00 p.m.-3:35 p.m., Moscone, South, Industry partner Theater. Drop an email on [email protected] or call +1866-9-828268 to pre-schedule a meeting.

Measure What Matters: 8 Ways to Measure Content Marketing Success

Content marketing has become so crucial, that it alone can make or break a brand. As consumers go through blogs, websites, and social media before investing in new products and services, organizations need to figure out what people want to know. It is vital to drive content that answers questions, even if they are lurking only in the minds of people. Good content tells consumers what adds value. It helps understand what you offer and drives your audience toward purchase. Marketers use white papers, infographics, visuals, videos, and slideshows depending on who is likely to use it and when. Such content is distributed on suitable platforms for maximum reach, but you need to check if you are actually getting closer to your goals. Here are 8 ways to do it! 1. Website metrics The website is one of the first channels that prospects visit to gain more information about a company. Web analytics helps you understand the kind of content that works and pages that need improvement. It helps you understand what fluctuations in Page view, Time on site, Crawl rate, and Bounce rate actually mean for your business 2. Organic traffic Content is distributed across various platforms for people at large to reach your web assets. Measure the organic traffic from search engines for specific keywords. It will give you an idea about how well users connect with your content. Utilize a healthy mix of SEO and social media to get your content before the right audience. 3. Social media metrics Social media is one of the largest sources of big data. Analyze social activities to get a pulse of the kind of thoughts users might have. Analyze metrics such as likes, shares, comments, reach, re-tweets, mentions, and views to understand what will make your content click. 4. Email metrics Organizations regularly send brochures and newsletters through email-marketing platforms. Media technology can help you understand metrics such as email opens, CTRs, and conversions to gauge how relevant your campaigns are proving to be. 5. Qualitative analysis Combine dry data with qualitative analysis. Incorporate sentiment-analysis tools to scan conversations. Turn your web properties into communities, and foster discussions and brand loyalty by incorporating their feedback. Aim to convert all negatives into positives, thereby becoming a responsive, responsible customer-centric brand. 6. Leads and subscribers A heavy fan following is not good enough. Users need to be converted into revenue generators, and hence, measuring leads becomes essential. One way to gather leads is using sign-ups on websites, sending emails, and freemium offers. Such information can be part of your CMS and those prospects can be cultivated into paying customers. For example, it is reported that a company enjoyed 35% conversion by combining email with valuable content as a strategy. 7. Thought leadership A good content marketing strategy can turn a business into thought leaders. Informative case studies, high-quality brochures, and out-of-the-box whitepapers can give businesses an edge. Measuring thought leadership is important. Monitor if other companies reference your content and how often experts wish to contribute to your content base. If you are receiving requests to participate in thought leadership and panel discussions, it is a sign that your content is generating enough traction. 8. Conversions The ultimate measure of success through content marketing is revenue. Conversions can occur across many touch points in the consumer marketplace and they help drive your brand value. Keeping the strategy in focus will ultimately lead to sales. Like any other strategy, a content strategy needs to be aligned with its objective. Organizations need to define a string of metrics that can give insights into how well the company’s content is performing. By referring to analytical reports on a frequent basis, the strategy can be altered and redesigned to align with the end goal of revenue.

6 Easy Steps to Create Mobile-Optimized Copy

With desktop websites going obsolete and Google’s ‘mobilegeddon’ setting the stage for some revamped algorithms, it’s time for marketers to shift focus. The world of communication has undergone a vast transformation. Mobile devices rule the roost and research reveals that consumers spend close to 6 hours a day on the internet. Of those, 2.8 hours are spent browsing on mobiles. Marketers are in no position to ignore it. But if you believe simply maintaining a mobile-responsive site is enough to help you through Google’s war zone, it’s time to think again. Content is king, even in the mobile space. But how do you create mobile-optimized copy? Here are my picks on what makes mobile content something very easy to create: 1. Use condensed headlines Copywriters should stick to short, but strong headlines. When reading on smartphones, users want to get straight to the point. There should be ample focus on font size and headlines to make them easy to scan instantaneously. Using a content management system gives you the WYSIWYG advantage and copywriters can see how their ideas will finally appear on the mobile screen. 2. Short paragraphs work the best Long paragraphs demand effort—something people using a mobile device generally don’t like. Too long a paragraph can lead the interest to fizz out. On a desktop website, six to seven lines of copy may not seem much. But on mobile devices, that kind of length can appear tiresome. Copywriters should focus on shorter paragraphs that carry the entire message. This might take a bit of practice. Blaise Pascal once said, “I have made this longer than usual because I have not had time to make it shorter.” Mobile content writing is all about writing less, but writing better. 3. Crisp and clear call to action Good content invokes certain behaviors. Whether it is a newsletter subscription or a purchase, the call to action must be clear, concise, and easy to locate on a smartphone screen—preferably without scrolling. Whenever content is time-bound, use appropriate messages by including the date and time. The whole point of a good copy is to induce the right action from readers. 4. The zing and links Mobile devices are very personal. Use that to your advantage and add a personalized touch by using more of ‘you’ than ‘I’. Psychologically, it works well. Use appropriate fonts and formatting to appeal to your audience as well. 5. Fitting the screen Instead of trying to fit large pieces of content on a single page, make space for buttons saying ‘Click here to read more’. Optimized layouts are available in content management systems, and they can help you figure out the best strategy from a host of options—depending on your campaign style, target audience, objective and other factors. 6. SEO for mobile content The content of a mobile website should be search-friendly. For that, relevance and customization are paramount. Desktop users may find it convenient to scroll through the first search results page, even the next page perhaps, but for mobile users, you’ve got to be extra lucky if they did that. Keeping it relevant will help improve your search ranking. To ensure you do it consistently without fail, you can measure your content’s relevance score through add-on features in your platform. Rethinking copywriting Research shows that 38% users are impressed when a local business has a mobile site, and 61% of users are more likely to contact a local business having a mobile-responsive site. This reflects how important it is to write engaging copies for smartphones and tablets.  

Importance of Platform Specific UI Design for Mobile Applications – Part2

Here, I have listed a few basic differences between iOS and Android screen layout and will explain how minor changes in the design will affect user experience and adaptability. 1.Back Navigation – Usually, an Android user expects the hardware back button to be functional for him to navigate to the previous screens in the app. The system back button present on the Android phones helps them to go back to the previous screens, but this is not possible with iOS. There is generally a back button on the header bar (or as we call it a navigation bar) which helps the user to navigate to the previous screen. Back Button in app (iOS)  vs. Hardware Back Button (Android)          An alert pop-up shown (iOS) vs. Toast shown to notify user (Android) 2.Gestures – There is always some user expectation set for gesture based interaction on each platform. Unlike iOS user, Android users expect a horizontal swipe to switch between tabs. If we consider deletion of an item from a list view/table view, Android incorporates horizontal swipe on the item to delete it from the list whereas iOS has a pre-defined right to left swipe which makes the “Delete” button appear on the list item to delete it. Unlike iOS, there is no snapping or bounce back effect on the page boundaries while scrolling to the end on Android. Unlike Android, where left to right swipe is reserved either for opening the navigation drawer or any on screen swipe-able content, on iOS, this gesture can be utilized to take you back to the previous screen.   Menu items present as tabs in the bottom vs. A navigation drawer available on left-to-right swipe 3.Segment Controls – When it comes to switching between different content in a single view, iOS uses Segment control whereas Android has a visually very distinctive style to show the same control as shown below.     Segment Control vs. Tabs in the form of line buttons  These are a few basic examples which I have discussed here. There are a lot of other important differences in terms of user controls like drop down in android vs picker in iOS, action sheets, icons, button styles, search bar, animations, etc. For an iOS developer, replicating native controls of any other platform on iOS might increase the complexity of the development. Users’ Perspective – When people tend to go with a common design for both the platforms, it leads to user experience problems irrespective of how aesthetically the app is designed. Users will expect an experience aligned with their platform. Finally, it is the end-user who decides how to navigate to a certain page, how to slide the menu in, what effect to expect on tap of any button etc. When the users get to interact with their platform specific components, they need to spend less time and effort on learning how to use it. The reason being that they are already quite accustomed to the platform conventions. Important takeaways from the blog – The app should use the UI components which follow the platform-specific guidelines just like the rest of the system. It’s a bad idea to redefine them. It is important to achieve consistency across apps for a single platform. It becomes easy to revise or expand the design if the specifications are kept separate. The focus should be on making the UI different and unique to the platform while retaining the look and feel that unifies the brand.   Creative and a good design comprises of a balanced combination of brand and platform-oriented design. The focus should be on both – the look and the usability of the app. Custom experience is good to have feature in the apps for the users to explore something new or different from their default platform, but sticking to the standards always wins when it comes to saving the time of the product team and saving the time of the products’ end-users by not making them spend extra time in learning the new controls. Please note we, as developers, have a clear understanding of UX and UI. They are not the same. UX Design refers to the term User Experience Design, while UI Design stands for User Interface Design. When we design any app, we should remember that our prime focus should be end-users, their ease to use the app and how quickly and well they adapt to the app. References – https://www.prlog.org/12254771-top-7-reasons-why-mobile-users-uninstall-apps.html http://iosdesign.ivomynttinen.com/ http://webdesign.tutsplus.com/articles/a-tale-of-two-platforms-designing-for-both-android-and-ios–cms-23616

How Automation of Warranty Processing Adds Happiness to Dealers, Service Centers, and Customers

The sheer volume and complexity of warranty claims can be overwhelming for organizations. This makes fraud-check on claims quite cumbersome. But if precision is your motto, start by re-engineering slow manual processes. Wonder why? They are prone to errors and impede productivity. Strive to achieve maximum automation in the processing of claims. Automation comes with several benefits: Meticulous data extraction followed by validation Quicker claims processing Single version of truth across manufacturer/dealer/service-center locations, thus improving efficiency Transparency in the process Let’s examine how the process flows with automation The automation of warranty processing begins with claims. Technology allows customers to submit claims from any location. On the other hand, the organization is able to track the claims. The system allows manufacturers to set rules, so that claims on every product can be processed in a unique way, if necessary. With the system automated, it is easy to validate claims efficiently. Minute details across various dealer locations can be confirmed in real time. It is also possible to route the claim details according to specific job codes, both within and outside the organization due to the cross-functional integration available in the system. The system helps track suppliers of the parts under claim. This speeds up parts-return and supplier recovery, and upholds trust and transparency. Further, payments can be made according to specific prevailing rates for a particular claim. Here is how you stand to gain A user-friendly platform that simplifies and speeds up the claims process makes life easier for customers and service providers Total automation of processes that were earlier manual—submission of claim, routing of claim, parts return, supplier recovery, payments—ensures accuracy, reduces the drudgery of long-drawn paper work, and improves efficiency Accurate and reduced warranty payouts result in optimal performance Visibility and communication at every step of the lifecycle is improved The overall cost of warranty processing and associated administration comes down Errors related to warranty claims are reduced to a minimum Parts return and supplier recovery are made simpler and more transparent Fraudulent and duplicate claims are actively screened and totally eliminated   Down there, do you see happier customers, saved money, and more efficient delivery of services? I think I do.

Configure SSO on AEM instance: AEM- Shibboleth integration

Objective: To create a Single Sign-On platform for web applications developed through AEM. The entire blog is divided into three parts: Part I: Pre-installation: To make the user familiar with the technologies used and the underlying architecture. Part II: Installation: The next part, i.e., part II of the blog provides a step by step installation guide. Part III: Configuration: Part III lists the basic configurations that need to be done to integrate all the entities. Part III: Post-Installation: The last part, i.e., part IV of the blog provides post-installation guidance and description of major challenges faced during the integration. Part I: PRE-INSTALLATION Entities involved: Shibboleth(Idp): Shibboleth is an open-source project that provides Single Sign-On capabilities Open-DS: OpenDS Software implements a wide range of Lightweight Directory Access Protocol (LDAP) and related standards. Tomcat Server:  To host the shibboleth application. AEM:  Adobe Experience Manager, where the application is deployed Technology/Standard/Protocols Security Assertion Markup Language (SAML) is an XML standard that allows an online service provider to contact a separate online identity provider to authenticate users who are trying to access secure content. The Lightweight Directory Access Protocol (LDAP) is a directory service protocol that runs on a layer above the TCP/IP stack. Underlying Architecture: II. INSTALLATION 1.1  Install OpenDS-2.3.0-build003 Launch control panel and Configure user id and password and business group 1.2  Install Shibboleth IDP (shibboleth-identityprovider-2.4.0-bin) Unzip the provided binary and run install.bat 1.3 Install tomcat (specifically apache-tomcat-6.0.37-windows-x64.zip) 2. Configure Tomcat 2.1. Create an SSL self-signed certificate Run the following commands: Openssl genrsa –des3 –out tomcatkey.pem 2048 Openssl req –new –x509 –key tomcatkey.pem –out tomcatcert.pem –days 1095 openssl pkcs8 -topk8 -inform PEM -outform DER -in idp.key  -nocrypt > pkcs8.key When you are asked for password, Put Your name everywhere for simplification 2.2.  Apply the certificate to  apache-tomcat-6.0.45/conf/server.xml. <Connector port=”8443″ protocol=”HTTP/1.1″ SSLEnabled=”true” maxThreads=”150″ scheme=”https” secure=”true” clientAuth=”false” sslProtocol=”TLS” SSLEngine=”on” III. CONFIGURATIONS Configure Shibboleth IDP 3.1  Open attribute-filter.xml and add the following tags: <afp:AttributeRule attributeID=”uid”> <afp:PermitValueRule xsi:type=”basic:ANY” /> </afp:AttributeRule>   <afp:AttributeRule attributeID=”group”> <afp:PermitValueRule xsi:type=”basic:ANY” /> </afp:AttributeRule>   <afp:AttributeRule attributeID=”mail”> <afp:PermitValueRule xsi:type=”basic:ANY” /> </afp:AttributeRule>   3.2 Open attribute-resolver.xml and configure in the following way: Uncomment attribute definition tags with id : uid, group and mail. Add LDAP credentials in the data connector tag: <resolver:DataConnector id=”myLDAP” xsi:type=”dc:LDAPDirectory” ldapURL=”ldap://localhost:389″ baseDN=”ou=People,dc=example,dc=com” principal=”cn=Directory Manager” principalCredential=”YOUR PASSWORD”> <dc:FilterTemplate> <![CDATA[ (uid=$requestContext.principalName) ]]> </dc:FilterTemplate> </resolver:DataConnector>   3.3  Open handler.xml and uncomment UserNamePassword Login handler and comment RemoteUser login handler 3.4  Open login.config and add the following entries for LDAP configuration ShibUserPassAuth { edu.vt.middleware.ldap.jaas.LdapLoginModule required ldapUrl=”ldap://localhost:389″ baseDn=”ou=People,dc=example,dc=com” bindDn =”cn=Directory Manager” bindCredential=”YOUR PASSWORD” ssl=”false” tls=”false” userField=”uid” userFilter=”uid={0}”; }; 3.5  Open relying-party.xml and add the following tags: <rp:RelyingParty id=”tavant.com” provider=”tavant.com” defaultSigningCredentialRef=”IdPCredential” defaultAuthenticationMethod =”urn:oasis:names:tc:SAML:2.0:ac:classes:PasswordProtectedTransport”> <rp:ProfileConfiguration xsi:type=”saml:SAML2SSOProfile” includeAttributeStatement=”true” assertionLifetime=”PT5M” assertionProxyCount=”0″ signResponses=”never” signAssertions=”always” encryptAssertions=”never” encryptNameIds=”never” includeConditionsNotBefore=”true”/> <rp:ProfileConfiguration xsi:type=”saml:SAML2ArtifactResolutionProfile” signResponses=”never” signAssertions=”always” encryptAssertions=”never” encryptNameIds=”never”/> <rp:ProfileConfiguration xsi:type=”saml:SAML2LogoutRequestProfile” signResponses=”conditional”/> </rp:RelyingParty> Add the following tag under the metadata tag: This is for further linking to AEM which we’ll discuss later. <metadata:MetadataProvider xsi:type=”metadata:FilesystemMetadataProvider” xmlns=”urn:mace:shibboleth:2.0:metadata” id=”AdobeCQ” metadataFile=”c:\saml_idp/metadata/adobecq.xml”/>   3.6 Open saml_idp\metadata\idp-metadata.xml Replace the certificate with the value present in saml_idp\credentials\idp.cert 4 Configure AEM 4.1     Create a new file adobecq.xml under saml_idp\metadata with the following text: <md:EntityDescriptor xmlns:md=”urn:oasis:names:tc:SAML:2.0:metadata” xmlns:ds=”www.w3.org/2000/09/xmldsig#” entityID=”tavant.com”> <md:SPSSODescriptor protocolSupportEnumeration=”urn:oasis:names:tc:SAML:2.0:protocol urn:oasis:names:tc:SAML:1.1:protocol”> <md:KeyDescriptor> <ds:KeyInfo xmlns:ds=”www.w3.org/2000/09/xmldsig#” Id=”SPInfo”> <ds:X509Data> <ds:X509Certificate> Put your certificate value here </ds:X509Certificate> </ds:X509Data> </ds:KeyInfo> </md:KeyDescriptor> <md:AssertionConsumerService Binding=”urn:oasis:names:tc:SAML:2.0:bindings:HTTP-POST” Location=”localhost:4502/saml_login” index=”1″/> <md:SingleLogoutService Binding=”urn:oasis:names:tc:SAML:2.0:bindings:HTTP-POST” Location=”tavant.com:8443/idp/Authn/UserPassword”/> </md:SPSSODescriptor></md:EntityDescriptor>   4.2  Under /etc/key in the repository, create a node called “saml”. Inside this node, add a new binary property called  “idp_cert” of “BINARY” type for the public certificate of the IdP.  That is, upload the file from C:/saml_idp/credentials/idp.crt. 4.3  Add a new binary property called “private” of “BINARY” type containing key for public certificate of the metadata (adobecq.xml) file.  That is, upload the file: C:\saml_idp\credentials\pkcs8.key 4.4  Open localhost:4502/system/console/configMgr ->Adobe Granite SAML 2.0 Authentication Handler   4.5 Now open Apache Sling Referrer Filter in the same console and configure it likewise.   SSLCertificateFile=” C:\demo\appserver\apache-tomcat-6.0.45 /tomcatcert.pem” SSLCertificateKeyFile=” C:\demo\appserver\apache-tomcat-6.0.45 /tomcatkey.pem” SSLPassword=”<Your password>” /> 2.3 Copy “idp.war” from saml_idp/war/idp.war to apache-tomcat-6.0.45/webapps 2.4 Create the directory  apache-tomcat-6.0.45 \endorsed and copy the .jar files included in the IdP source endorsed directory into the newly created directory. Hit  localhost:8443/idp/profile/Status, it should return OK. IV. POST INSTALLATION Configure domain name Open C:\Windows\System32\drivers\etc\hosts Add the following entries: 0.0.0.0 tavant.com 127.0.0.1 tavant.com localhost Make a request to AEM at http://<host>:<port>/, it would redirect to this login page: Login with the user name and password that you configured in Open DS Successful login will take you to the AEM home page. If you want to get the details of the user, its login session, login/expiry time, you can create a filter/servlet. One of the biggest challenges was to capture the SAML response since it always got redirected. Therefore, login time and expiry time could be picked from the user node that’s under “home/users” and user configured through Open DS is throughout referenced as remote user in AEM. Major Issues faced while integration: >       Troubles in integration/interaction of software. —    Get the right combination of software versions. —    Incorrect LDAP URL, user name and password in configuration files >       Deployment of Shibboleth war at Tomcat. —    Corrected by taking care of jar files >       Right permissions for the user group in AEM configuration. >       Unavailability of required SAML jars at run time —    Create a bundle of those jar files using bnd.jar and upload the bundle in felix to remove this error. >       Problem in fetching SAML Response in AEM from Idp since the response got forwarded always Used CRX node structure to fetch the response.

No Business for Intuition: Marketing is All Data-Driven Now

In the old days, data played a major role, but business decisions often had to be based on intuition. But data technology has improved so quickly in the last few years, businesses have had to revamp the way they think. Although marketing experts cannot afford to leave their intuitive skills at home, even the smallest decisions are expected to be backed by data. Big data analytics has become pivotal for driving decisions. Companies need to rapidly respond to market changes, understand customer preferences, and focus on targeted efforts. This calls for technology to channel customized marketing campaigns to the right users at the right time. Companies are excited about data-driven marketing. With Internet of Things and digital and mobile devices, the amount of data making way into companies is huge, and going to be even bigger. But it’s not just about gathering data; it’s about using it effectively. Although sales, online surveys, and consumer feedback produce boatloads of data every day, not all are of equal value. Marketers need to separate good data from bad ones. Here are three simple steps that outline how brands can build an analytical culture to reap more value from their repository of data: Set the objective of the business. It’s not about what data can do – it’s more about what the business goals are. Work out which questions need to be answered in order to achieve set business goals. This way, companies can focus on the kind of data required. After setting the questions, identify the data that would be needed to fill the gaps.   Tools & technologies Programmatic solutions make extensive use of data to track and target customers. With immense brand proliferation, consumers don’t only want precise information, but content that is relevant. Ad-tech and content management solutions churn huge amounts of data to present valuable insights on consumer behavior, market conditions, and trends, and also automates what has to be done based on that data. Thus, with a programmatic platform, a brand needs to click just a few buttons to ensure the right people view the right ads. Building relationships Big data works great for reaching ideal customers, but it has the scope to take marketers beyond prospecting—building lasting relations is just a part of it. Analytics makes it possible to have consistently personalized marketing. Thus businesses can connect with prospects intimately, offer them services that blend with their lifestyles, and build a loyal network of satisfied customers. While it is difficult to tell whether or not new technology will be prone to errors, programmatic software is designed to scan cloud and real-time data, and avoid the mistakes humans tend to make with large data. Programmatic solutions are also designed to automate buying, selling, and publishing of ads while keeping the accuracy of insights absolutely intact. That is why data-driven marketing is good enough to replace intuition.

Engaging the Modern-Day Consumer

At a time when consumers are ruling the roost, advertisers find it challenging to attract tech-savvy, on-the-go prospects. Customers don’t want to spare their precious time viewing advertisements that simply don’t make the cut. People expect succinct, precise, and exciting information at the right time. Today’s consumers are an unforgiving lot. A bad experience with a brand is enough to storm social pages with negative reviews until it bows down. There has never been a tougher time for companies to make their presence felt. That is why they need solutions that can delve deep into consumer behavior. The modern-day challenge Modern-day shoppers can be a mystery to marketers. Tech-savviness, social media involvement, and ever-changing demands are a lethal mix. Buyers don’t want to simply purchase products. They want to be part of certain lifestyles. Products don’t guide them to purchase. It is a heady mix of variables like alternatives, overall brand experience, peer reviews, social status, and state of mind that matters. Emotions play a major role and greatly influence and even determine consumer decisions. The AIDA (attention, interest, desire, action) model, which in many ways was the ‘go to’ model for advertisers, seems to have lost its sheen. With solutions like transactional ads emerging, consumers often jump from attention to action. The span of attention is now less than 30 seconds and within this tiny window, brands have to struggle—not just to capture attention, but drive positive action from consumers. Relevance and action Staying relevant, precise, concise, and targeted is of prime importance today. Ad-tech programmatic solutions are bringing to the fore technologies like ad-server solutions that help companies automate their content and ad-exchange processes. These solutions help in customer profiling, targeting, and proliferating the right messages in a small window of time. That implies getting the job done with automation. By implementing ad-tech tools, companies are able to circumvent labor and focus on important aspects like targeting and metric tracking. The digital landscape often throws a curve at marketers. To add to that are finicky customers and the job of offering relevant and updated content at the same time. That is why programmatic solutions make life easier. The days of ‘spray and pray’ marketing are long gone. The modern-day tactic is taking care of customers and approaching them with the right information at the right time, all the time.

Making Sense of A Media-Fragmented World

Technology is evolving in leaps and bounds. What was innovative yesterday is obsolete today.  With traditional channels like OOHs and TV slot ads fast being replaced by CMS and media planning solutions, all media channels have become fragmentized. The internet is possibly the most splintered of them all. While technological advances are the prime consequence of the ‘digital era’, they also pose a challenge to marketers. Consumers have become early adopters. They seek activities that can add value to their lifestyle. This being the mantra of today’s tech-savvy generation, marketers need to invariably work on new products and services. Added to that is the evolution and fragmentation of the media world. Companies need to keep pace with emerging channels of communication. Only then can they deliver device-agnostic, exciting content to touch base with audiences. Reach out and resonate Here are the top 5 ways how businesses can stay relevant in the market and resonate with their prospects in a media-fragmented space: Target audience: Companies need to understand who their customers are. Segmenting and selecting ideal customer groups is no longer simply based on demographics. Brands need to understand their audiences on psychographic and behavioral levels. Content rules: Customer engagement is the key to marketing success. At a time when prospects don’t want to be spammed with unnecessary advertising, marketers need to focus on engaging, and more personalized marketing collaterals. The need of the hour is creating fresh, relevant content to drive in-bound traffic. Go social: Firms need to incorporate social media strategies relevant to their business. Social media plays an integral role in shaping opinions, behaviors, and perceptions around brands. Social media is a great way to remain connected with their audience in real time. Think mobile: With websites soon to be phased out, firms need to start going mobile. Mobile internet and programmatic solutions will dominate the digital age. Big on big data: Companies need to make use of raw data to come up with business-relevant information that can result in effective marketing campaigns and increased ROIs. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter.

Inventive Mobile Apps are Keeping Audience Glued to Content

5 Secrets to Make Your Mobile App More Engaging Right from the way we peruse the morning news, to how we catch up with friends, technology has entirely transformed the way we do things. One of the channels of communication that is gaining a fair share of attention is mobile. Its presence has become all-pervasive. Most of us will accept, without our smartphones, we do feel less able. Mobile applications have become an integral part of our lives. According to Nielsen, people spend close to 30 hours a month on their mobile apps. This figure reveals the high marketing potential apps have. They play a key role in building sustainable relationships with brands. When used with the right CMS and programmatic tools, mobile apps can combine the brand’s offline and online efforts into a powerful marketing solution. Together with mobile websites, they’ve become important to both, consumers and marketers. Try these five ways to build an engaging mobile application:  1. Enhance the mobile experience Developers need to think how they can make apps interesting, easy, and compatible with all devices. It’s a competitive market out there, and so, it’s of utmost importance that the app stands out from the rest. Amplifying mobile experience should be a priority for businesses looking to jump in the mobile marketing space. 2. Advertise on social media With thousands of users logged into social networking sites, investing in social media is a good idea. Businesses can segment and target special clusters of customers and send them individualized messages. 3. Explore mobile payment Mobile payment is an area that has immense scope for exploration. Brands can reach new heights of innovation by working on solutions that can ease out online purchase and make payments simpler for customers. 4. Digital messaging Another way by which apps can have high customer re-visits is through digital messaging. Push messaging is an effective way to reach users who have already expressed interest in receiving updates from a particular brand. Businesses should make use of customer data and send regular communication to inform and remind people about new app enhancements, products, offers, etc. 5. Content is king A good app is defined by the kind of content that it offers to the user. Marketers should strive to present engaging, personalized content to users to keep them glued to the app. Interactive videos and vines are taking the app world by storm. Consumers, new and old, will keep flocking to an app that is content rich and engaging. The idea is all about creating what people need. Although they may not feel the need, it is the developer’s job to identify how lives can be improved and people can be delighted. That is how every successful business idea begins to take shape.

Leveraging Multiple Channels to Build Engagement in the Digital Ecosystem

The digital decade has revolutionized the marketplace. With a plethora of products, brands, and companies, consumers indeed have the last say. And it is becoming increasingly tough for marketers to attract them. Content is weaving consumer behavior every second. With a host of channels at their disposal, consumers are depending on smartphones, laptops, and tablets to remain connected. Marketers need to continually renovate and innovate to satisfy their customers. Leading enterprises understand the importance of keeping customers engaged. This means companies must strive to offer fresh content to their prospects and keep them informed and engaged, even as they move from one channel to the other. The need of the hour is a content management system (CMS) that offers richer and broader range of capabilities to manage and optimize visitor experience across all channels—including the web, mobile, and social networks. Is engaging over multiple channels a must? Over the last couple of years, the internet has undergone a mammoth change. It’s no more about just disseminating information. The demarcation between online and offline has vanished. The immersive Web 3.0 has given birth to an “always online”, more participative era. The web has become increasingly social and personalized, and smartphones are conquering the laptop and computer space. Users sometimes log in to social networks for making purchasing decisions. Mundane advertising doesn’t excite them. They seek interesting information, visuals, and infographics. They seek personalized, coherent, consistent content and customer experience regardless of whatever channel they use. Today’s customers are smart and always on the go. They want brands to inform, engage, and excite them 24/7. To effectively tackle this challenge of serving the audience with real-time ads and encouraging participation, enterprises require relevant tools to help get the job done. They require CMS tools that allow companies to create, manage, and deliver dynamic and targeted content across various online channels. Features that CMS tools should incorporate: The tools should allow delivering of targeted, personalized, consistent content and services across various channels to drive meaningful conversations. It’s critical to incorporate a mobile-first strategy. The tool should leverage responsive web designing and take advantage of the capabilities of HTML5 and CSS3. The tool should allow publishing content in all channels so as to facilitate and engage in conversations across all channels. The solution should provide robust integrated web-analytics capabilities to capture feedback and measure response.   Closing thoughts Selecting the right tool for CMS is crucial for organizations trying to engage and respond. By selecting the best-of-breed tools that combine advanced software and professional expertise, companies can manage vast amounts of content, become innovative, more agile, and deliver consistent messages across a host of channels.

Selecting the Right CMS Platform for Your Business

Today, a website is no more a static page. From informative and e-commerce websites to blogs and social media, they have become a reflection of the brand and business. Creating a website requires expertise but it is equally important to keep it sparkling fresh with regular updates and engaging content that will keep attracting traffic. Maintaining a website requires knowledge on coding. This can become quite a task for businesses. One cannot expect content developers to become HTML experts too. This means keeping techies handy 24*7 to make even the smallest of updates. This is certainly not feasible. That is why investing in Content Management Solutions becomes a great choice for businesses. Nowadays, content management agencies are surfacing in every corner. It can be overwhelming for businesses to sift through the flimsy ones and partner with the best CMS provider. So how do you choose the best CMS for your specific business needs and goals? These are the top 5 questions to ask: 1. Do I know my business? Businesses need to define in clear terms what they expect out of their CMS platform.  Decide ahead of time what you can do without and cannot. This is the first step towards choosing the vendor that suits business requirements. 2. What functions do I require in the CMS? Businesses need to assay important criteria like the number of people using the platform, ease of use, flexibility and customization.  Be proactive in taking inputs from all concerned internal stakeholders as they would be the ones working on the platform 3. Is the CMS mobile responsive? It has been a lightning transition. Desktop-based use of websites is diminishing fast, giving way to devices of multiple form factors. In this context, it is crucial to choose a vendor that provides a mobile responsive CMS platform.  A mobile responsive website has become the basic necessity these days. 4. What about scalability & security? To keep consumers engaged, regular content and design updates would need to be done routinely. Hence it’s imperative to choose a CMS platform that is enterprise-ready and can be scaled up easily. Also, CMS platforms should be built with strong security features to counter malicious attacks. Multi-level firewalls and authorizations should be embedded. Attacks on digital assets have increased and businesses need to put in place strong security measures to protect their websites. 5. Does it make financial sense? Don’t forget to factor in the budget. Evaluate whether it would make sense to employ a CMS solution and how it be a value-add for the business. Now you know Choosing a CMS platform can be a long-drawn-out and complicated process. However, it all boils down to asking the right questions. This will indeed make the procedure simpler and help you in choosing a smart CMS platform to meet all your business goals.

How Do You Know If Your Content Marketing Strategy Is Compelling

The talk of the town is how consumers and content have come to rule the market. For a business that wants to break through the clutter and attract the right kind of prospects, it’s essential to develop intriguing, actionable content. That’s easier said than done. Content is often confused with elaborate sales messages. If that’s how you define content, you seem to have missed the point. Content involves a whole gamut of activities, whose purpose is creating a network of engaged prospects—who can turn into revenue generators. At a time when consumers are resorting to methods to block unnecessary advertisements, businesses have to come up with innovative ways to put their message across to their consumers. “How do I promote my brand?” This one question seems to baffle even the most seasoned marketer. The answer: Provide the right information in the right format at the right time, and of course, to the right people. Creating a compelling content marketing strategy: it’s time to ask the right questions! 1. What gives you the edge? To begin with, companies should chart out their strategies, list down their goals, and analyze everything that makes them different from their competition. That way, it becomes easy to build a content marketing strategy that trumps competition, aligns with business objectives, and is customer centric. 2. What kind of content do my company and consumers need? Understand your target audience and evaluate all that makes them tick. At the same time, analyze the kind of content that can best define your brand and all that it stands for. It lets companies strike the perfect balance between identifying the correct cluster of consumers and offering them engaging content that adds value to both, the company and the consumers. 3. Have I made the right investments? Zero in on the right mix of in-house and external content. Invest in a content management agency that provides niche content marketing services. By involving an expert to handle all content requirements, the business can gain that added advantage and at the same time, allow focus to remain on its core competencies.  4. What about analytics? Big data and analytics play major roles in building brands. Companies already feel the need for intelligent systems that measure and map important metrics. With millions of data points, companies can gather crucial information on prospects, campaigns, impressions, and ROI. The business can thus take timely and relevant decisions. To summarize Building a successful brand requires businesses to offer enticing, value-adding material. The days of traditional advertising are numbered. Brands will stand out only with effective, coherent, personalized messaging. Now is the time for companies to make an effort and devote time to content strategies that can push audiences to react, act, and convert.

How CMS Capabilities Can Power Your Business

Eons ago, a website was a static page containing some tidbits. It was an online brochure of sorts with nothing but plain, mostly dated messages, a couple of pixelated images, and a contact address. That was it. Today, the website has evolved from being a mere online pamphlet to an effective business tool. In the digital age, when brands need to invest in innovative content creation to make their presence felt, businesses are extensively using their websites as a prime engine to create brand awareness, drive conversation, and generate revenue. Today, more and more companies are using content management system (CMS) as a solution to build and maintain their websites. A CMS makes content creation smarter and more powerful. It helps publish and modify content from a central interface. Websites thus stay fresh and also SEO friendly. Investing in an apt Content Management System can take your business a notch higher. Here are my top six reasons for investing in CMS: 1. A centralized repository: Without a CMS tool in place, content is prone to duplication. A CMS solution stores all data in a central database, facilitating content sharing, reducing redundancy, and enabling tracking. 2. Secure content: A major advantage is CMS solutions have firewalls installed and only authorized users can access back-end material. 3. Modifications and publications made easy: Approved users can easily add, delete, edit, and publish content. An integrated workflow facilitates better content management. 4. No complex coding: Gone are the days of hiring developers for minor website changes. With its easy interface, a CMS lets even non-techies tweak content as and when necessary. 5. Mobile ready: A CMS can automatically scale the website to be compatible for tablets and smartphones. 6. Search-engine friendly: CMSs can optimize websites for browsers to easily find your information through internet search engines. One of the biggest advantages that CMS solutions offer is control. Today, content developers need not worry about complex coding, laborious manual updating, and security. In a couple of clicks, they can update the website without hassle. CMS is a groundbreaking tool that combines features like automation, easy interface, and mobility. With the right CMS, developing, tracking and monetizing content is just a piece of cake!

Of Ad Blocking and Online Marketing: How to Counter the Threat

Today’s consumers are on the lookout for experiences that add value to their lifestyles. They are tech-savvy, demanding, and wary of marketers. Just pithy sales messages and cheesy one-liners are not appreciated. Annoying advertisements are immediately shut out. It’s certainly no big deal for customers, but a huge loss for the brand. Chances are the numbers can be huge. The reason being that consumers simply do not like marketers encroaching their personal space with irrelevant messages. The threat of ad blockers is looming and marketers need to act, fast. Who’s blocking ads—and why According to research conducted by Adobe and PageFair, people within the age groups of 18-29 are most likely to use . Gaming sites have the highest ad-blocking rates followed by social networking. There can be several reasons as to why people block ads. Users can find pop-ups to be an irritant especially when they are irrelevant. Ads also affect device speed. Other reasons why ad blocking software is rampantly used are prevention of cookie tracking and sometimes, to improve bandwidth or battery life. Dealing with ad-blocking: marketer’s perspective 1. Advertisements, with their sounds and animation, can be a distraction for users. Spraying ads that hold no relevance to viewers can also lead to ad blocking. To counter this, digital marketing firms should focus on creating individualized ads and sending them to targeted clusters only. Programmatic solutions are gaining ground. This will not only stop ad blocking, but also lead to higher impressions. Increasing the level of relevance and personalization will make it harder for customers to completely ignore advertising. 2. Native advertising and transactional advertising work great to deter ad blocking. Native advertising allows ads to appear in the same format as the page they are on.  Transactional advertising serve as single touch points for making purchases. 3. Marketers, in order to not encroach upon consumer space, now have taken to permission marketing. They gather approval from consumers to allow sending promotional material. This way they are able to reach out to their consumers only with their consent.   I feel it’s time for advertisers to educate consumers that all ads are not entirely noise. To achieve this, content developers should focus on delivering creative, value-adding content. This will act as a double-edged sword—not only deterring ad blocking, but also leading to more engagement.

Using Business Intelligence in AdTech

At a time when the world has gone programmatic, it simply doesn’t make sense for companies to stick to traditional ways of carrying out ad requirements. There is no point in spending valuable resources to carry out repetitive, inane tasks associated with today’s chaotic ad world. Leading brands realize that shooting in the dark, in hopes of getting new customers onboard, will get them nowhere. It’s, in fact, time to invest in technologies that provide exhaustive and intelligent inputs, which can basically help companies make relevant business decisions. Business intelligence is all about identifying complex operational, marketing, and analytical problems and, offering practical, scalable solutions that can take businesses to the next level. The concept of business intelligence or machine learning isn’t exactly new to advertisers; it has been in play in for some time now. Leading companies that understand the significance of business intelligence are quick to leverage it to reach out to the right consumers at the right time in an effective fashion. With brands proliferating exponentially and giving rise to a hugely competitive landscape, there is a strong need to continually think out of the box. Engineers and data scientists are striving to reinvent, innovate and bring to the table newer intelligent tools that can comprehend, predict and offer methodologies and solutions that can change the way businesses are run. This is a huge step in the right direction for advertising firms. Very soon, armed with such technologies, ad firms would have the capability to tap into not only upfront and latent demand but also create and foresee new needs and trends. Disrupting the AdTech space Here are a few important points that reflect how business intelligence can disrupt the adtech space: Advanced business intelligence software can help companies gather, interpret and even react to bouts of real-time data. The software can enable companies to analyze consumers, take instantaneous decisions based on market forces, predict campaign success and even point out alterations. With the software, advertisers can automate and seamlessly connect disparate sets of activities in the ad publishing cycle. By connecting silos, companies can create more effective campaigns and marketing strategies. Business intelligence software can bring together media buying and audience targeting on a single centralized platform, which can enable personalized and customized adverts to clustered groups, thus leading to impactful advertising The Future of Business Intelligence The programmatic landscape, in order to address the expanding needs of the cluttered market, has to be integrated with powerful business intelligent technologies. The day is not far when advertisers will have access to advanced intelligent tools that will combine statistical estimation of big data with human knowledge and expertise. Such tools will collect and analyze live data coming in from a variety of channels to instinctively guide programmatic advertising. The robust platform will enable companies to better segment, profile, and target consumers, by offering individualized content; thus leading to better profits and higher return on investments.

Data Management Platforms: Enabling Better Business

Data is at the core of all marketing and advertising operations today. Without it, companies would have no direction, let alone any edge over competition. They all know it, but only a few understand. For others, there are data management platforms (DMP) to help marketers and publishers make sense of it all. A data management platform is like a data warehouse software that houses and manages information (for example, cookie IDs) to assist in tasks such as generating audience segments and targeting clusters. Advertisers today communicate to a number pf publishers and buy media across a huge range of sites. DMPs collate information on all those activities in a centralized location and use them to optimize future media buys and ad requirements. Essentially, using a DMP is all about better segmenting, profiling and targeting customers. An enterprise DMP can scale millions of data points and offer marketers insights on a host of market and campaign variables. What do DMPs offer? The benefits: Prospecting: DMPs seamlessly integrate with third-party customer data. This way, they help in achieving higher accuracy and scaling better with targeted campaigns. Re-targeting: Businesses can analyze buying records, browsing behavior and customer profile, among others, using DMPs. Using online and offline variables, they help in implementing customized re-targeting campaigns. Audience segmentation: DMPs allow marketers to create several granular as well as broad segments. This way, marketers are able to reach out to audiences with the right message at different stages of the purchase cycle. Optimized site content: By using third-party data, DMPs gauge customer profiles and offer personalized content to users when they visit the brand’s site. Analytics: With DMPs, companies need not maintain cumbersome excel sheets. DMPs have inbuilt dashboards that provide measures and compare campaign performance across channels, give insight into audience interaction, etc. These reports help marketers optimize and channelize marketing efforts in the best possible way.   Of brands, customers and ROIs New brands are growing by the day, and making the marketplace even more cluttered. So much so that consumers are now developing a ‘blind-spot’ for advertisements. Hence, it is of supreme importance that marketers channelize their efforts in a way that the right message reaches the right people; people who have a high possibility to earn value by investing in the brand. DMPs help marketers effectively analyze all their disparate audience and campaign data, allowing them to make better media buying decisions, target prospects, and offer personalized content leading to higher brand awareness and conversion. With DMP capabilities, brands can judge the effectiveness of marketing efforts and optimally alter and implement them. Such platforms also help brands to better connect with consumers by setting in place a platform to reach out for quick customer service. All in all, it is a win- win situation for marketers; with access to valuable business insights, they can reduce wastage of resources, scale operations and ultimate earn a higher ROI.

Importance of Platform Specific UI Design for Mobile Applications

I have often heard people saying “Let’s keep the UI same for all the mobile platforms.”, “Is this app already designed for Android? Ok, let’s replicate it on iOS as well.”, “Client is okay compromising on the beautification of the app. It should just be functional.”. In this series of blogs, I will explain as to why – as a customer, a marketing evangelist, and a developer; it is important to follow platform specific guidelines while designing mobile apps and why we need to keep our mobile application designs differentiated for respective platforms. There will be only 8-10% scenarios where we can keep the UI similar (not same still!). I recently downloaded an application on my iPhone which was designed to run on both iOS and Android. Yes, the application is functional. It lets the user navigate, perform kinds of actions on different screens etc. But being an iOS user for the past 5 years, it was neither easy nor pleasing for me to understand the controls in the app. Few screens looked web-like, others looked Android-like. It took me a while to understand the overall flow of the application. The color scheme didn’t match to what my eyes are used to seeing in all other apps built for iPhone. I am assuming that the product team failed to explain to the clients about how important it is to follow UI design paradigms of the specific platform for any mobile app. I am sure they would have loved the approach if they were informed about the importance of design. The overall look and feel, branding could have been kept same following platform-specific guidelines for navigation, animation, actions, colors, shadows, hues etc. Result – I uninstalled the app. In my experience, creating similar looking UI design confuses and isolates the users. It is good to stick to native experience as the application would be predictable and easy to use. End users always expect an experience which is aligned with their platform. Uninstallation & Bad Reviews – In a recent survey it is shown that 60-70% of the people uninstall the app within the first day of installation. This is the last thing a company would want for its product. If the users don’t find an app easy to use in the first go, there is no second thought before they uninstall the app and write a bad review. In such scenarios, users tend to switch to the desktop. This is also valid for an enterprise user. I have seen enterprise users taking the desktop route when they fail to relate to the application. And this voids the very purpose of an enterprise having invested in the app. Here is a survey which shows that around 42% of the uninstallations happen due to bad UI/UX. Having said that, I would like to add that the decision to go with a common design approach for multiple platforms completely depends on the complexity of the app and its requirement. Around 8-10% of the apps are mostly form filling apps or single page apps with ‘read only’ data. In such scenarios, going with a common design approach is considered to be a better solution in terms of ease of development and maintenance. Below is a simple example which showcases this scenario – The app below is a simple form which captures users’ response and communicates them to the server. (A simple form filling application sharing the same design on Android and iOS)  But when we talk about other apps which has more user interactions involved with a bigger set of data to deal with, it is better to stick with the platform specific design convention. Whenever we begin to design our apps for any platform, it is very important to know and understand the design principles of that platform. I will explain the differences between iOS and Android screen layout and how minor changes in the design effect user experience and adaptability in my next blog. References – https://www.prlog.org/12254771-top-7-reasons-why-mobile-users-uninstall-apps.html http://iosdesign.ivomynttinen.com/ http://webdesign.tutsplus.com/articles/a-tale-of-two-platforms-designing-for-both-android-and-ios–cms-23616

Innovative Digital Advertising for Higher Revenue through Media Planning

With smart, tech-savvy millennials preferring minimal encroachment by ads, drawing attention becomes a challenge for marketers, but technology is bringing new ways to stay connected. Marketers need to unlearn and re-learn technology, consumer preferences, competitor actions and a host of other variables. Only then can they stay relevant in the dynamic marketplace. It is vital for brands to look out for innovative advertising solutions that entice next-gen consumers. Marketers need to rethink their advertising strategies, decipher the right mix of channels, and implement the optimum amount of investment to survive, sustain, and grow. Innovation is the key 1. Personalized advertising: Programmatic technologies allow marketers to effectively decipher customer profiles and shoot across personalized, targeted messages to prospects. At a time when it is crucial for brands to be succinct and informative at the same time, targeted advertising works wonders in attracting potential revenue generators. 2. Transactional ads: They allow users to purchase just by clicking on digital ads, without even visiting the advertiser’s website. Transactional ads are in vogue and disruptive technology allows the use of a single touchpoint for interaction and purchase. By upselling and cross-selling via these ads, companies can achieve higher ROI from their advertising spend while reducing the time to purchase. 3. In-app advertising: With smartphones emerging as the major chunk of the advertising pie, in-app advertising is gaining its share of eyeballs. In-app advertising works great for sending highly targeted messages to a segmented audience. An ad network pays the ad developer to include its code into the application so that when the app is running, the ad network serves targeted advertisements through the software. In-app advertising is reckoned to be the next big thing. 4. Native ads: As a response to users being somewhat blind to traditional banners, native advertising has been a solution to rely on. Advertisements are cohesively delivered and integrated into the page design. This way, the user feels that the ad is actually a part of the content, at least at first glimpse. And native advertising is proving to be a great way to create brand awareness. The future is here Innovative technologies do bring challenges and marketers need to think creatively to overcome technological and operational barriers. The future of digital advertising is all about programmatic technology because it is designed for targeting the right people with what they might need. Identifying that correctly is impossible by manual methods, and that is why programmatic can set brands apart and help them sustain profits.

Creating Opportunities: Mortgage Loan Originators Morph into Sales Personnel

The loan origination phase is a confluence of many opportunities. But bank personnel are found limiting themselves to application checks for compliance. The high number of regulations swamps too many people with paperwork and processing, and leaves few free for marketing operations, which are essential to productive loan origination. Prospecting and referrals entail marketing campaigns, attending industry events, running a social profile, and making sales calls. Post-sales services are also necessary to maintain bank reputation. Personnel who specialized in marketing have to concentrate on processing applications and checking for compliance in the face of new regulations. Although software can automate loan origination, some banks have virtually no workforce left to deal with marketing. They are feeling the need for specialized marketing teams, especially with the advent of social media. The focus has shifted to centralized digital marketing. Referral-partner phone calls, follow-up meetings, coordination with underwriters, and settlements with real-estate agents also require a special expertise. Digital specialists and data analysts have been able to mine through huge databases and send targeted messages to customers. It works better than generic email blasts and frees mortgage loan originators to do the necessary mortgage activities. Compliance with the new TILA-RESPA Integrated Disclosures Act (TRID) is a major necessity. Every message from the lender should be consistent and controlled across multiple channels. Centralized marketing campaigns help to deliver targeted messages to customers, at different stages of buying, while complying with current credit policies of US. Digital marketing campaigns are of high value for originators, as many are undertaken on behalf of the MLO (mortgage loan originator). The module uses the mortgage loan originators’ sales distribution lists and segments the business completely. Social media helps utilize the expertise of a marketing team and compliant messages with consistent brand information can be posted at regular intervals to reach out to customers. Modern technology has customers finding social interactive pages more trustworthy compared to a website. Banks cannot depend just on originators to run coordinated digital marketing campaigns. The institutions need to support them with point-of-sale and retail marketing by integrating them with loan origination systems. It gives mortgage lenders better tools to multiply sales.

From Good to Great: Top 6 Innovative Advertising Strategies

Consumers have a host of devices to stay connected: mobiles, tablets, laptops, desktops, you name it and they have it. With the power of technology, it takes only a few clicks to know what is happening on the other side of the world. From checking out social feeds on smartphones to downloading the latest editions of newsletters, consumers make the most of all the devices at their disposal. Being connected 24/7 is something that has emerged as a trend in the digital era. The changing times pose a huge challenge for marketers engaging customers across multiple locations and devices. So the industry is using programmatic solutions that don’t just create engaging, personalized content, but track customers across multiple devices, study their behavior, and target the right ones for the right objectives. That is what converts prospects into customers and fetches higher ROI. Programmatic advertising is the holy grail of digital marketing and the latest ad platforms have been truly revolutionary. They automate the ad buying process and help in effective targeting, segmentation, profiling, and tracking. The platforms also help analyze the results they bring. People recognize a good ad when they see one. So finding the right bucket of prospects at the right time and presenting them with that perfect combination of copy and visual is a mission that advertisers must consider. Innovative advertising should lead to deeper engagement and more conversions through campaign management solutions. Here are 6 strategies to advertise innovatively at minimal cost: Ensure a sizeable proportion of budget for mobile advertising Invest in cross-device tracking and targeting Use native marketing for today’s information-hungry audience Prioritize behavioral data such as past purchasing and online browsing patterns Think hyper-personalization, as ads can never be ‘too personalized’ Focus on exciting creatives to entice people and discourage them from blocking ads   It is of utmost importance for brands to understand that consumers today want to drive conversations. Just promoting doesn’t work anymore, content does. So if your brand hasn’t started talking programmatic solutions or donned the creative hat, it’s time.

How to Plug GSA (Google Search Appliance) with AEM or Any CMS

This article discusses the best way to leverage enterprise search platform Google Search Appliance (GSA) in Adobe Experience Manager (AEM) and any other CMS. AEM is a comprehensive content management platform for building websites, mobile apps, and forms. It helps deliver content consistently across devices and provides responsive, relevant, and social experiences, placing customers at the center of every interaction. GSA helps employees and customers garner accurate, relevant information to make smart decisions and stay productive. It can read more than 220 file types and find information from databases, file systems, and common repositories like SharePoint, Livelink, Lotus Notes, Filenet, and Documentum. Why is GSA required? GSA provides an extensive level of search features to an individual entity, with specific enterprise enhancements that make searching easier, intuitive, and customizable. GSA is required when the customer wants to implement features like: Scalable enterprise search solution Federated search across domain Partial search, exact search, advanced search, autocomplete, synonyms, autosuggestion and much more Reduced search result execution time Improved throughput performance Faceted search (can be in sync with AEM tags) Search in entire content pages and all types of digital assets Implementation approach Install and configure GSA appliance Create custom connector: Custom connector can help listen to calls from AEM and pass information to GSA. GSA API is based on Java. So other CMSs can leverage GSA Java API for creating custom connector. But another CMS has to write its own event handler. Sample code:   AEM Event Handler for GSA Read data (thrown by AEM) at GSA in the form of feed   Sample GSA Feed Start indexing and crawling The automated index process will start once the feed is pushed to the GSA. After completion of indexing, the admin can see the status of crawled data. GSA will deliver the best search results Search results will be served by GSA based on keyword or filtering criteria. GSA can deliver the search content in the form of JSON, XML, or XSLT. Below is the sample JSON output.   Such integration of AEM with GSA yields the following benefits: Scalable enterprise search solution Minimum development and maintenance activities Improved accuracy of search results Reports on search activities like most searchable keyword, promoted content view, etc. Tavant has successfully implemented AEM-GSA integration for a leading digital media organization and a leading online examination service.

Attention Mortgage Servicers: Are You Aware of The New TCPA Rules?

Following TRID, the Telephone Consumer Protection Act (TCPA) continues to caution mortgage CxOs. Process, finance, and technology heads felt they already did their bit by investing in loan origination technology to comply with TRID, but TCPA rules are mandating expensive CRM operations. While the penalty for an unsolicited debt-collection call can be as much as $1,500, process and technology changes can save the lenders from new debt-collection rules. Litigation should be avoided as far as possible. According to the new Act, debt-collection calls require prior written or oral consent before consumers receive them on their wireless devices. The calls can be made only to landlines or telephones if the borrower prefers it that way. It severely restricts the ability of debt collection agencies. The Federal Communications Commission (FCC) has expanded the definition of “Autodialer” and it includes any phone that automatically dials random or sequential numbers from the lender’s end. Every smartphone at the collection agency’s end will be considered an autodialer and require expressed consent from the consumers. More needs to be implemented upon the arrival of TCPA rules: Pre-recorded telemarketing messages must include an automated interactive opt-out mechanism, and it should be available throughout the duration of the call. Thus, consumers should have the option of dropping the call at any stage of the conversation. If consumers invoke the option to unsubscribe, then they should be delegated to the do-not-call list and not called henceforth. The recorded telemarketing messages should include a toll-free number to opt out of marketing calls. The burden of proof of consent falls squarely on debt collectors with the new FCC rules. Mortgage servicers should amend their policies and technologies to mitigate debt-collection calls. There are several means of ensuring consent, like written consent on the 1003 at the loan origination stage or web-based consent when uploading borrower information. Mortgage servicers should be careful in future, especially about how the new TCPA rules apply. Noncompliance can result in litigation and cause a loss of millions, as autodialers might end up reaching thousands of borrowers without consent.

How Being Social Helps Brands In Spite of the Congestion

Today, it’s not merely about providing quality products. It’s more about innovative marketing. Companies are diving deep into creative ways to promote their brands and reaching out to prospects. With 3 million people on the internet and 2.1 billion active on social media, this particular channel is indeed creating quite a buzz. The impact of social media on business performance cannot be ignored. In today’s dynamic environment, for businesses to sustain, relationship building is of paramount importance. Social media provides an incredible opportunity for businesses to build, sustain, and engage with customers 24*7. How do social media help businesses become profitable? By effectively using a mix of social platforms, brands can send across the right messages at the right time to the right group of people, thus creating brand awareness. By building relationships and communities, social media helps in forming loyal customers. Its presence gives brands a human touch. Here, brands behave as humans do and that gives them an opportunity to engage better with customers, that too on a real-time basis, and mostly without manual action. Here we look at some strategies to get the best out of your social media: What is the objective? Companies need to set the goals of social media campaigns. They must identify the variables they seek to achieve—be it advertising, creating brand awareness, brand loyalty, higher conversions, etc. Declare your presence After setting the objective, companies need to start creating awareness. By creating social pages, posting blogs, starting communities, forums and webinars, companies can tap into potential leads, help disseminate information regarding their products, share their thought leadership, and build a network. All these go a long way in creating and maintaining brand awareness. Choosing the best social medium platform Companies need to devote time and effort to researching the best social platforms suited for them. For example, Facebook can be used for reaching out to a large number of people and building a community presence. LinkedIn, with its focus on jobs and industries, becomes useful for business deals. Similarly, Twitter works great for driving conversations. Let’s talk metrics Organizations need to invest in social media analytics to measure metrics such as likes, followers, mentions, traffic, CTR, etc. Today, sentiment analysis is also gaining ground. With the help of analytical tools, organizations can filter out useless data and use important metrics to take business-relevant decisions. The focus should be on creating business value. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter.

Critics Proved Wrong! Automation Has Made Underwriting More Customer-Friendly and Transparent

Automated underwriting systems have brought in significant cost savings and streamlined mortgage business processes. The elimination of inefficiencies has worked well for lenders and borrowers. Automated underwriting is thus being increasingly adapted to make the loan origination process better, simpler, and faster. A recent study by Washington University has found that 60-70% of residential mortgage origination has been facilitated by automated underwriting and the numbers are steadily moving north. Loan origination is rife with documentation and every application requires a lot of supporting data to minimize risk for the lender. Lenders submit applications to underwriters who review borrowers’ financial viability, the veracity of supporting documents and check for compliance. Personal and financial data like credit score, loan-to-value ratios, property values, borrower-debt ratios and credit histories are taken into account to consider your loan application. Statistical models based on credit and mortgage data have allowed artificial intelligence, which reduces risk to the lender. Automated valuation models, scorecards, and review tools are excellent sources of data, but many companies are transitioning into new tools which can be integrated with analytics. This would help in minimizing the risks by co-relating data information with the external environment. The new RESPA-TILA guidelines have shortened the timelines considerably and therefore it is important that appraisers or underwriters efficiently complete their tasks to make loan origination faster. Critics have always upheld the view that automated underwriting would become extremely insensitive by keeping out minorities and credit-challenged applications. The human touch cannot be completely removed but the use of technology has become imperative. Automation coupled with professional expertise would be the way forward. Mortgage software has been enabling lenders and organizations to stay ahead of the compliance curve. With new guidelines and shortened timelines, speedy processing and efficient communication can be enabled only with automation. Automation has made underwriting faster, accurate and better. Lenders and borrowers have been immensely benefitted through: Reduction in documentation wherein automated underwriting only asks for recent pay stubs compared to earlier submissions of the previous 2 years of W-2s. Loan origination risks are minimized as the software red-flags problem areas for the appraiser before the reports are submitted to the lender. Frequent returns to the appraiser for modifications are thus averted. Much faster processes as reports are generated within minutes. Consumers save on their closing costs The loan application can be submitted before the property is identified and the customers have an upper hand while bargaining with the seller.   Technology has become an integral part of the mortgage process with loan origination reduced as per the new compliance guidelines. It has immensely benefitted underwriters by delivering tools that have streamlined and expedited the appraisal process without compromising on the quality. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. FAQs – Tavant Solutions How has Tavant demonstrated that automation makes underwriting more customer-friendly?Tavant has proven automation benefits through faster decision times, consistent evaluation criteria, transparent decision explanations, and improved customer communication. Their automated underwriting systems provide clear reasoning for decisions, eliminate human bias, and offer borrowers real-time updates throughout the process. What evidence does Tavant provide that automated underwriting improves transparency?Tavant provides detailed decision audit trails, explainable AI models, standardized evaluation criteria, and comprehensive borrower communication systems. Their platforms generate clear explanations for loan decisions, provide consistent feedback, and maintain complete documentation of the underwriting process for borrower review. How has automated underwriting improved customer experience?Automated underwriting has improved customer experience through faster processing times, consistent decisions, reduced errors, 24/7 availability, transparent criteria, and clear communication. Borrowers receive quicker feedback, understand decision factors, and experience more predictable outcomes. Is automated underwriting more transparent than manual underwriting?Yes, automated underwriting is typically more transparent because it uses consistent, documented criteria, provides detailed decision explanations, maintains complete audit trails, and eliminates subjective human judgment variations. Borrowers can better understand how decisions are made. What were the main criticisms of automated underwriting?Main criticisms included concerns about algorithmic bias, loss of human judgment for complex cases, lack of flexibility for unique situations, potential for discriminatory outcomes, and reduced personal relationships between lenders and borrowers. However, proper implementation has addressed many of these concerns.

Measuring Only Metrics? Think Twice Sentiments Matter!

Consumers today have innumerable channels to express themselves. Social media has empowered them with powerful platforms, where they can extol brands that serve them well and thrash the ones that fail to meet expectations. This is not necessarily bad for brands. An analysis of consumers’ opinion of brands and their standing in the marketplace  — in other words, sentiment analysis, a much bandied about term making the rounds in the digital marketing space — can help brands in better implementing their marketing campaigns to address issues. Sentiment analysis is the process of determining the emotional tone behind a series of words, to gain an understanding of the emotions, opinions and attitudes expressed online by consumers of goods and services or brands. Quality matters Playing with numbers such as shares, like, tweets, re-tweets is a good way to start, but is it good enough? Numbers can’t determine whether or not consumers are on the same page as the brands would want them to be. With sentiment analysis, marketers can have a holistic view of customer engagement by measuring qualitative aspects such as opinions, feelings and satisfaction ratings among others. Benefits Sentiment analysis is extremely useful in digital marketing as it allows companies to gain an understanding of the wider public opinion behind campaigns. It can uncover attitudes that consumers hold with respect to brands. A powerful marketing tool, sentiment analysis provides deep insight into consumer perception and, additionally helps in driving strategies for brand improvement. The applications of such analysis are broad and powerful. Consumers rely on peers when making their purchasing decisions. They take decisions based on comments and opinions expressed on social media channels. Even one negative comment can spell doom for brands. That’s why and how sentiment analysis comes into the picture. Brands can track all that’s being felt and said about them and thereby take steps to redeem themselves. A word of caution Inbuilt algorithms recognize and track a gamut of words, and categorize them as ‘negatives’ and ‘positives’. However, teaching machines to analyze the complexities of human language is not possible. For example, a sarcastic statement containing a positive word may be taken at face value and be categorized as ‘positive’. The way ahead Sentiment analysis is surely not a perfect science. It needs to move beyond the one-dimensional positive-negative scale. Today, companies can choose from a variety of tools for sentiment or opinion mining. With the right software and an expert, companies can gather, analyze and manage conversations about their brands. Remember, both quality and quantity are important for brands looking to maximize their business. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter.

CRM Essentials for Compliance and Efficiency

In the pre-Dodd Frank days, loan origination and marketing were prerogatives of mortgage loan officers. Banks and lenders encouraged self-developed marketing techniques to canvass borrowers and referrals. But with TRID regulations implemented recently, lenders have become extremely cautious about their messages reaching the markets. Compliance with Real Estate Settlement Procedures and Truth in Lending acts (RESPA and TILA) mandate strict control over marketing communications. There is zero tolerance for misguided communication. Controlling messages can get increasingly complex with mobile and social media sites becoming prominent vehicles of customer reach. Marketing needs to be meticulous and well managed. If not, it will be easy to circumvent the RESPA-TILA rules. Such difficulties can be curtailed by integrating customer relationship management (CRM) tools with loan origination software, point of sales systems, databases, and product & pricing systems. The integrated technology, along with sales automation, makes it possible to control and co-ordinate messages so that they are posted on social media and other channels with perfect compliance. A well-balanced marketing strategy helps reach out to customers and improve referral management. Loan origination is a tough task with regulations driving up costs. Lenders require their loan officers to close more loans at low costs and enable marketing campaigns with professional ethics and effectiveness. This is possible with a completely integrated CRM system, which ensures seamless communication with customers and effective presentations. Mobile functionality also needs to be integrated with loan origination systems. A large majority of lenders do make their websites mobile-friendly and ensure real-time customer reach. But to roll out a complete mobile-compliant marketing campaign is different from a simple online chat. A coordinated campaign across multiple channels will be a good way to test the CRM systems. It should be able to reach out to customers effectively. It is important to note that customized integration of CRM and loan origination will help meet specific objectives smoothly. That way, it will help loan officers choose the required marketing campaigns and deploy them immediately for specific borrowers and widen their customer reach smoothly. FAQs – Tavant Solutions How does Tavant integrate CRM functionality with compliance and efficiency requirements?Tavant provides CRM systems with built-in compliance tracking, automated audit trails, regulatory reporting capabilities, and workflow optimization tools. Their integrated approach ensures customer relationship management activities maintain compliance while improving operational efficiency and customer service quality. What compliance features does Tavant include in their CRM systems? Tavant includes automated compliance monitoring, regulatory reporting, audit trail generation, data privacy controls, consent management, and risk assessment integration within their CRM platforms. These features ensure customer interactions meet regulatory requirements while maintaining detailed documentation. Why is compliance important in CRM for financial services?Compliance in CRM for financial services is essential to meet regulatory requirements, protect customer data, maintain audit trails, avoid penalties, and ensure fair treatment of customers. Financial services CRM systems must comply with regulations like GDPR, CCPA, and various banking regulations. How can CRM systems improve operational efficiency?CRM systems improve operational efficiency through automated workflows, centralized customer data, streamlined communication, task management, performance analytics, and integration with other business systems. This reduces manual work, eliminates duplicate efforts, and improves customer service speed. What are essential CRM features for financial services?Essential CRM features for financial services include contact management, interaction tracking, automated workflows, compliance monitoring, document management, reporting and analytics, integration capabilities, mobile access, and security features that meet industry standards.

Accept the E-Mortgage Solutions Trend or Lose Customers: The Choice is Yours

Federal Housing Administration (FHA) has announced that it will soon accept e-signatures on most loan documents. This will help the mortgage process become faster and simpler for both borrowers and lenders. Moreover, the Consumer Financial Protection Bureau (CFPB) has released its disclosure documents, which many e-mortgage consultants predict will enhance the adoption. The most interesting observation is that all lenders, small and big, are using the online banking process. It helps customers to complete almost the entire mortgage process online. Customers now don’t need to run from one office to another. They can send their applications through electronic devices. These devices can be used for comparing different rates, submitting applications and other documents, and also for contacting loan officers online. However, even online platforms can get cumbersome. The use of a mortgage software solution to automate the processes is made exactly for that reason, and it has benefited many bankers. An end-to-end e-mortgage software is thoroughly involved in the acquisition, loan origination, underwriting, and managing portfolios after settling a deal. The entire process of applying for a mortgage becomes tedious because of the long pre-settlement process. Moreover, the entire journey of pre-settlement has to be seamless for the consumer. Using compatible software, you can: Provide customers with reports that help make decisions regarding property purchase Give lending agencies and brokers proper estimates of property values to help them pre-qualify their leads and work smart according to consumer expectations Provide access to plenty of comparable sales to acquire fast customer approval Accomplish the process of property valuation management between lenders and loan evaluators The best part of automation software is that after the successful execution of the pre-settlement process and follow-through with client onboarding, it continues its value addition for capital marketers and lenders. Here is what the software does: Gives regular updates on property valuations so that lenders can evaluate the current loan-to-value ratios to manage risks effectively Helps manage the underwriting procedures and shares well-researched inputs on ongoing property trends Helps in customer retention by keeping them alert on properties being sold Understands property data with respect to conventional home loan procedures and provides portfolio insights The most important aspect of end-to-end e-mortgage software solution is that it will equip all lenders with automation and speed. This shift is not just to meet the mandatory compliances and better accuracy, diligence, and transparency requirements, but also higher customer satisfaction: an absolute parameter in the present market-facing fierce competition. FAQs – Tavant Solutions How does Tavant help lenders implement e-mortgage solutions to retain customers?Tavant provides comprehensive e-mortgage platforms with digital application processes, electronic document management, automated underwriting, and digital closing capabilities. Their solutions help lenders meet customer expectations for digital experiences while maintaining compliance and reducing processing time from weeks to days. What competitive advantages do Tavant e-mortgage offerings provide?Tavant e-mortgage platforms offer faster processing times, reduced operational costs, improved customer satisfaction, enhanced accuracy through automation, and better compliance management. These advantages help lenders compete effectively against digital-first mortgage companies and retain market share. What are e-mortgage solutions?E-mortgage solutions are digital platforms that enable electronic mortgage application, processing, underwriting, and closing processes. They replace paper-based workflows with digital alternatives, providing faster processing, better customer experience, and improved operational efficiency for mortgage lenders. Why are customers demanding e-mortgage solutions?Customers demand e-mortgage solutions for convenience, speed, transparency, and control over the mortgage process. Digital-native consumers expect online applications, real-time status updates, electronic document submission, and mobile accessibility similar to other digital financial services. How do e-mortgage solutions reduce processing time?E-mortgage solutions reduce processing time through automated document verification, digital data extraction, electronic communications, parallel processing workflows, and elimination of manual paper handling. These efficiencies can reduce mortgage processing from 45-60 days to 15-20 days.

How Business Stays a Step Ahead by Relying on Big Data

Big data and analytics are vital to understand, target, and convert prospects into revenue generators. As data grows, so does technological capabilities. The tools are disruptive, but help to understand what makes customers tick. Technology now processes thousands of data sets and for real-time dashboards for real-time advantages. With innumerable brands competing for mind and shelf space, it is no puzzle that the only point of difference between a superior and a sub-par brand is customer experience. Brands that take due care to provide exceptional customer service and products are the ones that prove to have the ‘x-factor’ sometimes. Big data enables companies to make sense of zillions of bytes, know what to do, and ensure successful products and marketing campaigns. Helping businesses stay one step ahead Analytics is important for companies to gauge preferences and offer just the right options. Businesses, by analyzing data on customer purchase, browsing history, and customer profiles can understand the kind of products their prospects are willing to buy. With the information, companies can introduce new products, revamp old ones, and offer only what customers want. This means more targeted offerings and waning of unwanted product lines. Social media presence is for businesses to interact and engage with their customers in real time. Big data gives access to real-time data about what a consumer is buying, clicking on, and commenting about. Companies may have continuous conversations with people through customized landing pages, apps, and advertisements. Three basic questions any big data tool should answer What is the profile of an ideal customer? What are the top 3 products any given customer is likely to buy? What is the best channel and time to connect with customers?   Listening to customer interactions across digital channels in real time has become the most fundamental need in many industries. It is for businesses to learn about their customers’ behavior by building holistic customer profiles and running personalized campaigns through cloud and on-premise platforms. Most of that can be done through automation. Analytics-driven programmatic solutions let marketers develop and execute marketing campaigns based on a complete understanding of customer preferences. This helps unleash the full potential of media planning software and leads to improved customer satisfaction, better acquisition rate, and higher conversions—especially as automation gets more accurate and data gets bigger. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter.

Mortgage Industry Should Look Beyond Millennials for Opportunities

It is true millennials are by far the largest demographic group ready for buying homes. Born between 1982 and 2004, this generation is around 83 million in the US. However, two elder generations are as closely populated as the millennials: Generation X, around 65 million and Baby Boomers, around 76 million. Gen X and Baby Boomers as prospects Baby Boomers are investing more in mortgage today, compared to what they did a decade earlier. As about 2.1 million Baby Boomers live with their families (grandchildren in their 20s), the younger generation is found more dependent on elders for mortgage-related decisions. Moreover, Gen X has long finished paying their education loans. They can be targeted for home loans; they will go for it more readily. Why Baby Boomers? Around 23% of the total US population is Baby Boomers, a section looking towards retirement. The new loan borrowing table (reverse mortgage) for Baby Boomers now allows spouses to keep the house, as long as they pay the insurance, taxes, and association costs. Also, the Federal Housing Administration (FHA) through their Home Equity Conversion Mortgage (HECM) purchase allowed eligible seniors to use a reverse mortgage to relocate or downsize home purchasing. What makes Gen X qualify? Another generation showing promises in mortgage lending is Gen X, says a report of National Association of Realtors (NAR). The average Gen X buyer is 41-years-old and earns a little less than $105,000. One of their major focuses is on buying larger homes so that they can accommodate the entire family. With about 75% of Gen X in the U.S. (approx. 50 million people) using the internet, information on consumer lending can be disseminated to them really fast. In the middle of all financial crises, this generation is coming up as one extremely powerful and stable group. Of course, one of the main reasons why mortgage lenders target millennials is their immense proximity to technology. About two-thirds of them regularly use the internet on their mobiles. That proportion is only slightly lower for Gen X, and Baby Boomers are at just below 50%. These proportions have been measured from their entire populations in the US. Gen X and Baby Boomers cannot be mistaken to be less compatible with data-driven advertising, online property browsing, and mobile banking. Hence, Lenders can use mortgage software technology and achieve much more comprehensive business returns by targeting not just the millennials, but Gen X and Baby Boomers as well.

Ensuring Loyalty With Personalization

It is no secret that consumers appreciate that personal touch. It is in building brand loyalty, and digital marketers do understand that. Personalization, today, has reached a stage beyond simple log-in and log-out messages. Big data has enabled companies to read the minds and understand behaviors of consumers. Brands now have access to consumer profile, purchase history, internet browsing behavior and several other significant aspects. This makes it easy for marketers to aim and shoot across their message. Tailored-content is the talk of the advertising town. It’s all about breaking from the clutter and making yourself heard. With the emergence of sophisticated content and programmatic technologies, personalization tools can now analyze user behavior in real-time and instantly deliver targeted content across multiple user channels. Several industries are actively focusing on personalized content to attract users. One of the verticals fast setting the benchmark is the retail space. It has been observed that retailers who use personalization as an integral part of their strategy have seen an increase in brand growth, metrics and bottom line. How retailers are effectively incorporating personalization to build sustainable brand loyalty: Personalized product recommendations: Based on buying records, retailers today, send out product recommendations and information on new launches via emails, texts or communicate through social media to customers. Consumers are grouped according to their browsing history and online behavior. Basis this data, retailers shoot targeted content to consumers in order to retarget, remind and create an inclination for purchase. It’s a great opportunity for cross-selling and upselling. Email marketing: It is a terrific way to generate leads and convert more prospects. Retailers actively send promotional mailers about products and discounts to a select group of people to induce them to engage and buy. Personalized discounts: Such offers take sales to an altogether different level. A great example of this can be seen in how e-commerce websites segment their customers based on their product purchase history and the kind of money they usually spend on buying. The companies then offer discounts or freebies to lure these customers into buying more.   Personalization in the digital world is all about getting to know your customers and enticing them with the right message at the right time. Real-time technology combined with powerful content makes for an invincible formula that ensures better conversions and brand loyalty.

WSO2 Integration with AEM – Part 2

After WSO2 Identity Server (IS) Installation & Configuration in Part 1, we arrive at the stage when we can perform AEM (Adobe Experience Manager) Side Configuration. Steps for AEM Configuration: Put the below entry into your AEM project’s pom.xml file: <dependencies> <dependency> <groupId>org.wso2.carbon</groupId> <artifactId>org.wso2.carbon.identity.sso.agent</artifactId> <version>1.2.0</version> </dependency> </dependencies> <repositories> <repository> <id>wso2-nexus</id> <name>WSO2 internal Repository</name>   <url> http://maven.wso2.org/nexus/content/groups/wso2-public/ </url> <releases> <enabled>true</enabled> <updatePolicy>daily</updatePolicy> <checksumPolicy>ignore</checksumPolicy> </releases> </repository> </repositories> The above dependency will download the jar file, which will be used to generate the SAML request for Login and Logout. Download the sample project from the link provided below. It uses WSO2 Identity Server for Login and Logout, and configures the travelocity.properties file (in your AEM project) required to communicate with WSO2IS by referring to the downloaded project. Additionally, refer this sample project to generate the SAML request for Login and Logout. Two servlets were created in our case, wherein the first one was used to generate SAML request for Login and Logout, and the other was used to handle the response from WSO2 Identity Server. The Login page to be served by WSO2IS will be similar to the one below: Credentials need to be entered, and authentication will be handled by WSO2. If the authentication fails, it will show an error message on the same page, else, it will redirect to the website page-as per the code you write.

Private Investors Revive with Mortgage Process as a Service

The disaster endured by the U.S. housing industry was due to subprime mortgages. Enormous price rises of property finance ensued from a significant increase in Residential Mortgage Backed Securities (RMBS) and home prices. It forced most private loan originators and investors to give up on the property financing industry, helping Government-Sponsored Enterprises (GSEs) to acquire a much better market position than before. Instability in the private market followed, as GSEs became dominant. In recent years, private markets have revitalized by using Mortgage Process as a Service (MPaaS). It promises a better market for private players in the mortgage industry and here are the reasons: Guaranteed quality loans and data transparency ensure risk reduction and more private investments. The data on loan origination will be collected, verified, and presented in a standardized way to help originators make better decisions on credit underwriting, and hence provide investors better due diligence reports. Better quality service, and meaningful and accurate loan information become available while processing loan origination. That helps in reduced loan default rates and repurchases.   MPaaS helps remove the cost of ownership from applications, people, technology infrastructure, and platforms. The pay-per-use model is used for banks to save large amounts of money. Here is why the U.S. consumer lending market is ready for MPaaS: Both, industrial and economic parameters show U.S. mortgage is reviving slowly. There are proposals of closing the GSEs down, which is a big positive for revival of private investors. The trust of RMBS investors can be won with better risk management skills. This will also help decrease the repurchase risk.   With MPaaS entering the market, lenders have access to technology, process, and people, as well as the scope to transfer ownership risks and a few other responsibilities to mortgage software providers. However, such a shift is possible only if customers use the pay-per-use model. It helps shun capital expenses (Capex) and adopt operating expenses (Opex). Business process as a service (BPaaS) not only measures the extent to which a lender’s process is executed, but also reduces compliance and repurchase risks. BPaaS/MPaaS is required to act as an independent information mediator by providing better quality data for banks that require better risk management. As MPaaS provides the lender a platform to manage business processes, banks have enough scope to address the challenges arising from this new system more effectively.

2Ts to Attract Borrowers: Technology, Transparency

Much of modern-day shopping happens online, mostly through mobile devices. New-age shopping equips buyers with: Abundant options A quick, easy, and reliable buying process Tools for easy comparison and better purchases It is natural that people used to the fingertip experience expect a similarly smooth-sailing mortgage experience too. And the number of customers in that category is shooting up. A Home Buyer and Seller Generational Trends Report by National Association of Realtors says that 68% of first-time home buyers and 32% of all home buyers belong to Gen Y (the millennials). CEB Global estimates this generation to have a population of 75.7 million and a purchasing power of $1.68 billion. In the wide range of age from 15 to 35, Gen Y does not fit into a general behavior pattern. However, data shows that they are heavily used to multiple gadgets for communication, entertainment, information, business, and shopping. Smartphones and tablets are used for most online activities today. Laptops and desktops constitute a much smaller fraction. Consider how that affects customers seeking mortgage. Potential borrowers do a great volume of housing research online on their mobile devices. That is where to catch them. Take advantage of their love for technology. People are discussing companies and products on social media, discussion forums, and review sites. If no one is discussing you, it means you just don’t exist for them. While looking for mortgages, customers want communication to be quick and responsive. They need ample channels to get back to you for clarifications. Your web and mobile interfaces should have communication tools and styles that suit borrowers of different age groups and their online habits. People love dialogue. Dead-end one-way communications benumb them. Make way for greater interaction. Have a comprehensive mobile strategy. Communication tools integrated with web and mobile-based origination can help provide personalized and transparent experiences to mortgage clients. It is all for natural and faster conversions. It means higher productivity and lower cost per loan. Your origination system should get integrated with the best of modern IT to ensure speed, ease, and transparency. Customers should be able to auto-fill your forms and submit them online. Photographs and documents like paystubs can be uploaded with a few clicks. Such technologies improve processing efficiency and shorten the loan cycle from application through automated validations, verifications, approvals, and loan disbursals. The cloud-based Software-as-a-Service (SaaS) model helps mitigate your IT costs and ensures that you get the latest technology updates immediately as they reach the market. With no extra investment for hardware or software licenses, you can start using them. Moreover, you shall be relieved of the rigors of security testing. Technology that adds speed and transparency enables loan officers and executives to meet people in style, and get things done quickly. In addition to process efficiency and cost saving, technology will give your brand a chic image among customers and your employees. FAQs – Tavant Solutions How does Tavant implement technology and transparency to attract borrowers?Tavant combines cutting-edge technology with transparent lending practices through real-time application tracking, clear fee disclosure, explainable AI decisions, and open communication channels. Their platform provides borrowers with complete visibility into the lending process while delivering fast, efficient, and user-friendly digital experiences. What transparency features does Tavant offer in their lending technology?Tavant provides detailed decision explanations, real-time status updates, comprehensive fee breakdowns, clear terms and conditions, audit trails for all interactions, and educational resources about lending processes. Their platform ensures borrowers understand every aspect of their loan application and decision. Why are technology and transparency important for attracting borrowers?Technology and transparency attract borrowers by building trust, providing convenience, reducing uncertainty, enabling informed decisions, and creating superior customer experiences. Modern borrowers expect digital efficiency combined with clear, honest communication about lending terms and processes. How does transparency improve borrower trust in lending?Transparency improves borrower trust by providing clear explanations of lending decisions, disclosing all fees upfront, explaining how data is used, offering real-time process updates, and maintaining open communication. This reduces anxiety and builds confidence in the lending relationship. What technology features do borrowers value most in lending?Borrowers value mobile-first applications, instant pre-approvals, real-time status updates, digital document upload, automated verification processes, live chat support, and intuitive user interfaces. They also appreciate AI-powered features that simplify complex lending processes.

Show, Don’t Just Tell! Videos Can Help Awesome Mortgage Origination

In what ways do you present mortgage options to a prospective customer? By meeting in person and explaining the details with supporting documents? That seems convincing, but that is something you usually do in the last phase. It is also the least scalable option. What channels can you use to generate public interest, build repute as a thought leader, and gather leads? To name a few, those channels are your websites, blogs, social media, email campaigns, and public events. Studies show that most of such communication relies heavily on textual content. Two very obvious, but often unnoticed facts: People trust you more if you have a face and not just a voice. People do business with you more readily when they are familiar with you.   But how does that affect your loan origination processes? How can you take advantage of these simple facts to boost your business? They say a picture is worth a thousand words. Logically, a video should be worth a hundred thousand, with moving pictures and sounds, and the intimacy of human talk. High bandwidth internet and ubiquitous screen-cast applications make it very easy to create, edit, and upload videos these days. Have you considered putting up your message as a series of short videos? Screen casting (aka video screen capture) is recording your computer screen as video, usually with voiceover recorded by using a microphone. That is an excellent way to demonstrate your mortgage origination software and explain processes. Videos created can also be used for awesome presentations. Putting them online in front of the right people lets you explain things just like you would in person. Broadcast your message over a wide range of video-hosting platforms and social media in addition to your official websites and blogs. Many screen-cast software are available for free and are very easy to learn and use. Many are even available as mobile apps. Open Broadcaster Software, commonly known as OBS, is free and available for Windows, OSX (Mac), and Linux platforms. Some other popular free screen-casting software are AviScreen, Screenr, CamStudio, Copernicus, JingProject, Screencast-O-Matic, and Wink. For those who are particular about professional quality and full control, there is paid software (this does not mean that free versions are inferior in quality or features). Examples of commercial software for screen-cast include Camtasia, Adobe Captivate, ScreenFlow, AllCapture, HyperCam, iShowU, ScreenMimic, and ScreenRecord. These tools are incredibly simple and fascinating. Download and install a free version and start creating your videos. On the go, you will learn great ways of presenting your mortgage products and loan servicing software. They are designed to improve your image as an expert. It’s time to let people trust you like they want to trust you.

WSO2 Integration with AEM – Part 1

WSO2 Identity Server (IS) is a good choice to Integrate WSO2 Identity Server with AEM for Single Sign On, as WSO2IS is open source and supports SAML 2.0, OpenID, OpenID Connect, OAuth 2.0, SCIM, XACML, and Passive Federation. The server also has in-built LDAP, in which we can set up users and their roles. In this part, we address WSO2IS Installation & Configuration. In the next part (Part 2), we will look at AEM (Adobe Experience Manager) Side Configuration. Steps for WSO2 Identity Server Installation & Configuration: Download the server from http://wso2.com/products/identity-server/ and install. Login to WSO2IS Login using the default credentials (username:admin and password:admin). Go to Main > Users and Roles > Add. Create a new user. This user will be used to login to the website and become authenticated by WSO2IS. Create a new Service Provider in order to let AEM use WSO2IS as an IDP. Go to Identity > Service Providers > Add. We need to specify a Service Provider name, and then configure the SAML Web SSO. For that, we must access Inbound Authentication Configuration > SAML2 Web SSO Configuration, and click Configure. Next, we need to provide the configuration for SAML SSO like in the image below: Configuration for SAML SSO – Issuer: aem This is the entity ID for SAML2 service provider. This value should be the same as the SAML. IssuerID value will be specified inside the travelocity.com/WEB-INF/classes/travelocity.properties file. This is the Assertion Consumer Service (ACS) URL of the service provider. The identity provider redirects the SAML2 response to this ACS URL and this value should be the same as the SAML. The ConsumerUrl value will be mentioned inside the travelocity.com/WEB-INF/classes/travelocity.properties file. NameID format: urn:oasis:names:tc:SAML:1.1:nameid-format:EmailAddress The service provider and identity provider usually reciprocate with each other regarding a specific subject. That subject should be detected through a Name-Identifier (NameID), which should be in a format that simplifies identification by the other party. There are some formats that are defined by SAML2 specification. Enter the format’s default value here (i.e., urn:oasis:names:tc:SAML:1.1:NameID-format:EmailAddress). “Use fully qualified username in the NameID” option should be checked. “Enable Response Signing” option should be checked. Set this as true by selecting the checkbox. This is used to sign the SAML2 Responses returned post authentication. “Enable Assertion Signing” option should be checked. “Enable Single Logout” option should be checked. Set this as true by selecting the checkbox. Do this to sanguinely terminate all sessions once the user signs out from one server. “Enable Attribute Profile” option should be checked. “Include Attributes in the Response Always” option should be checked. Configure outbound authentication type as Default. This specifies that the identity provider authenticates the users by validating with the identity provider’s user store. Save all the configurations. Read the second part of the blog on AEM Side Configuration

The Future is with Customizable E-Mortgage

The mortgage process is usually a bitter experience for consumers because of heavy paperwork and multiple officials to consult before the transaction. Technology has made the process more customer-friendly and e-mortgage has emerged as a promising new option. Conventional loan documentation today involves 2,000 pages and the production cost per loan stands at $6,769, making it almost prohibitive for lenders and borrowers. Digitized, automated, cloud-based loan mortgage servicing is gaining traction across lending firms. The concept of e-mortgage has been around since the early 2000s. The Uniform Electronic Transactions Act and the legal acceptance of electronic signatures created impetus for the technology. But the real push has come after the financial recession of 2008, when the government introduced a slew of compliance standards for consumer protection. Compliance standards like the Qualified Mortgage/Ability to Repay (QM/ATR), along with the Know Before You Owe standards, which must be adapted, has increased documentation costs. But automation today encompasses the whole gamut of prequalification, application, disclosure management, underwriting, processing, secondary market management, and closing. This has made electronic mortgage an attractive option. E-mortgage ecosystem E-mortgage involves technology solutions that seamlessly include all mortgage touch points.  This includes electronic signatures, documentation, e-vaults, e-notaries, e-disclosures, electronic registration systems, e-registry and other e-commerce solutions. Implementation of industry standards for e-sign and UETA has accelerated the adoption of e-mortgage. E-mortgage involves collaboration between internal and external participants including lenders, borrowers, closing agents, service providers, and investors. A customized mortgage servicing process should be able to share and access data through a single web-hosted electronic interface. Today, loan origination “software as a service” integrates multiple customer touch points including a web portal, CRM interface, documentation, and mobile apps facilitating customer interaction. Smarter lenders with e-mortgage The number-one reason for lenders to adopt e-mortgage is compliance and regulatory requirements, which have pushed up loan origination costs. The automated loan servicing option has increased operational efficiencies, reduced the costs incurred, and boosted productivity. Lenders have been freed of heavy paperwork and can focus on delivering better customer experiences. Lenders are also better prepared for market shifts and can use big data analytics to gain insights into customer behavior, competitors, and market requirements. Automation can reduce back-office data entry and errors by about 25%. There is better quality control and lenders can achieve faster cycle times in loan servicing. Better borrower experience Tech-savvy Millennials prefer online experiences. Software with a customer-friendly digital interface can enable easy access, transparency, and instant gratification for buyers. Consumers go online to shop for better rates and services. They want to submit applications online, upload electronically signed documents on secure platforms, and get real-time access to their loan statuses. Lenders who provide personalized recommendations, online customer support, and realtor recommendations are appreciated by consumers. E-mortgage solutions provide customized digital tools that deliver a complete customer experience. Single stack driving borrower experience Today, in spite of automation, specialist providers may handle individual processes. One company does the documentation, the other loan origination, and another loan pricing and so on. A single-stack concept, where there is vertical consolidation of the services, will be the driver for e-mortgages. A single-stack cloud-based digital platform can meet all the requirements in mortgage servicing and deliver a customized borrower experience. Final thoughts E-mortgage enables lenders to use digital technology to customize a customer-driven process. It uses data analytics to identify customer behaviors and emotional drivers to generate personalized recommendations for borrowers. It brings in different players under a single umbrella and makes mortgage servicing an efficient end-to-end process in spite of full compliance with regulations. FAQs – Tavant Solutions How is Tavant shaping the future of customizable e-mortgage solutions?Tavant is developing highly configurable e-mortgage platforms with modular architecture, API-first design, and flexible workflow engines. Their future-ready solutions enable lenders to customize every aspect of the mortgage process, from application interfaces to underwriting criteria, while maintaining compliance and operational efficiency. What customization capabilities will Tavant offer for future e-mortgage platforms?Tavant will provide drag-and-drop interface builders, configurable business rules engines, personalized borrower experiences, custom integration capabilities, and flexible reporting tools. Their platforms will enable lenders to adapt quickly to market changes, regulatory updates, and customer preferences without extensive development work. What is a customizable e-mortgage platform?A customizable e-mortgage platform is a flexible digital mortgage system that allows lenders to modify workflows, interfaces, business rules, and processes to match their specific requirements, brand identity, and customer preferences while maintaining core mortgage processing functionality. How will e-mortgages evolve in the future?Future e-mortgages will feature AI-powered personalization, blockchain verification, instant approvals, mobile-first experiences, predictive analytics, automated compliance, and seamless integration with IoT devices and financial ecosystems for comprehensive borrower assessment. What benefits do customizable mortgage solutions provide?Customizable mortgage solutions provide competitive differentiation, improved customer satisfaction, operational flexibility, faster adaptation to regulatory changes, brand consistency, and the ability to serve diverse market segments with tailored experiences while maintaining operational efficiency.

Package Utility Using Query Builder

Package utility facilitates the building of a user interface (UI) to execute XPath queries for information-related pages, assets, etc. The user gets an option: either view the result of the query on the same UI or build a CQ package of the result obtained from the query. This approach has two key benefits: It can be used while syncing the environments Example: Suppose we have 100 pages in QA environment, 100 pages in production till date, and after a week, around 50 pages get created in QA. Now the environments are not in sync. We can fire a query in QA environment to fetch information about the pages created since last week, make a package of that result, and install in production. That will sync both environments. The UI allows authors to execute queries without having xpath knowledge. Steps for implementation 1) Create an html form to input the parameters regarding query. 2) To handle the request, create a servlet, in which the input fields will be retrieved from a form and query description will be created as a map. 3) Use the Query Builder API to create the query and get results from it. 4) Use Package Manager API to create the package of the CQ query and download it from AEM package manager console. User interface Using the Query Builder tool Select the Xtype from dropdown to search for like assets or pages. Select the name of the property from the dropdown. You can also provide a property which is not present in the dropdown by selecting ‘Other’. A text box will appear to enter the property. You can provide the value of the property and the date-range value for properties specific to date range. You can click on the ‘Show Results’ button to view the results of the query and for building a package click on Build Package button for package creation of the results generated.

Integrating Kaltura with AEM for a Seamless Video Experience

Adobe Experience Manager (AEM) enables users to create, edit, manage and optimize websites across different digital channels such as web, mobile, and social. Integrating Kaltura with AEM helps save all the videos in Kaltura and reduces the burden on AEM. Kaltura is a secure audio/video streaming service that allows uploading and embedding high-quality media. Some of the key advantages of AEM-Kaltura integration are: ˃    Helps in uploading individual media files from desktop to Kaltura ˃    Facilitates multiple media file upload from desktop to Kaltura in one shot ˃    Helps to record just-in-time audio/video messages for assignments and/or feedback Steps for Integration Create an account on Kaltura’s website using the link below: http://corp.kaltura.com/Products/Kaltura-API Create a job in AEM project which is scheduled to run at regular intervals. Its purpose will be to fetch all the metadata and thumbnail images of all the videos from your Kaltura account and save them in Digital Asset Management (DAM). Below is a screenshot of the Job created in AEM. This code fetches all the metadata from Kaltura. The properties shown in the above screenshot are of the account created on Kaltura’s website which would be used to connect to Kaltura API to fetch the videos. The video player can be dynamically embedded on the web page to run videos. Below is the code to do this.

Future of Mortgage Originations: Here’s Why Today’s Mortgage Originators Will Lose 35% Market Share by 2020

Who originates a mortgage loan? Mostly mortgage brokers, be it as firms or individuals. In some cases, the lending organization itself acts as the originator. A large volume of mortgages are originated by only a few giant firms. However, an equally large volume is originated by thousands of individuals and small firms. Is the industry going to stay that way, or will it change sometime soon? Studies and statistics say that the share of proportions is changing fast. However, how fast, is anyone’s guess! A study by Accenture says that by 2020, today’s mortgage originators will have collectively lost 35% of their market share to new entrants and small lenders, who are adopting new operating models. Online and independent lenders, who emerged after the great credit crisis, have already stolen the market share of midsize banks. Origination comprises marketing mortgages to consumers, assessing their credit-worthiness, verifying the documents and legal papers, identifying the right products for borrowers, processing the mortgages, capturing data, and storing it productively. Steps involved in originating a loan differ based on factors like loan type, loan risks, regulations, and lender policies. The disruption The traditional pen-and-paper mode of lending is gradually being replaced by IT intervention, leading to online implementation of almost all origination processes. All that is required is a web portal or a mobile app. Recent trends and projections for the coming years highlight some good news and some bad news for firms. The good news is that loan processes will be quicker and less dreary. The bad news is firms that are failing to gear up will gradually lose business—just like Kodak faded away from the photography industry, or the way tape recorders disappeared when digital disks and iTunes took over. The traditional model Sales agents assist customers to understand how terms of payment and interest rates matter before selecting the right product. Agents also help select suitable add-on products like insurance protection for loans. The assistance is available for filling application forms, and the required documents like proof of income, identity, address, assets and liabilities, right up to when the application is submitted. Back-office functions of loan origination continue from that point. The new self-service model Web technologies and smartphone apps are revolutionizing mortgage lending. Online-only originators like GuaranteedRate.com, QuickenLoans.com, and Sindeo.com have come up with websites and mobile apps capable of doing everything that sales agents and mortgage brokers did. With uncluttered user interfaces and intuitive algorithms, they can guide even the not-so-tech-savvy customer smoothly through the entire process. The smart systems help customers calculate loan durations and identify suitable repayment structures. The systems auto-fill customer forms with the right data and provide tips and suggestions depending on the needs of particular customers. All documents, photographs, and signatures can be submitted online, and the systems can verify immediately if the applicant qualifies for the loan. If everything is fine, the whole process will be completed and the amount disbursed in less than 15 days—without any in-person interaction. Quick and cost-effective, such a system also offers customers a seamless experience. In addition, smart systems provide personal financial management tools and access to other multiple accounts of the user. At any point of time, the customer will have access to all relevant data pertaining to the mortgage. New firms benefit from big data, analytics, cloud, and virtualization, and are able to come up with market-smart mortgage products. They can analyze customer profiles in no time and offer risk-adjusted products offsetting risk costs. It helps the firms with greater volume of business than one can fetch by sticking to the traditional risk-avoidance model. Moreover, new firms stay embedded in social media, constantly grabbing attention of customers and building rapport. In short, the coming years will present increasingly complex and highly dynamic environments. Besides immediate implementation, agility in IT development and constant innovation shall be the key drivers of lending businesses. FAQs – Tavant Solutions How is Tavant preparing for the future of mortgage originations?Tavant is investing in next-generation technologies including AI-driven automation, blockchain verification, predictive analytics, and cloud-native architectures. Their forward-looking approach ensures mortgage origination systems can adapt to emerging technologies, regulatory changes, and evolving customer expectations. What innovative features will Tavant include in future mortgage origination platforms?Tavant will incorporate voice-activated applications, biometric verification, real-time collaboration tools, predictive document generation, and intelligent risk assessment capabilities. Their platforms will offer seamless integration with emerging technologies while maintaining security and compliance standards. How will mortgage origination change in the next decade?Mortgage origination will become increasingly automated, with AI handling most routine decisions, blockchain providing secure verification, and predictive analytics enabling proactive customer service. The process will be faster, more transparent, and highly personalized for each borrower. What technologies will transform mortgage origination?Technologies transforming mortgage origination include artificial intelligence, blockchain, machine learning, cloud computing, mobile platforms, biometric authentication, IoT integration, and advanced data analytics that enable more accurate, efficient, and secure mortgage processing. What will the mortgage application process look like in the future?Future mortgage applications will be conversational, using AI assistants to guide borrowers through personalized application flows. The process will be completed primarily on mobile devices with automatic data verification, instant pre-approvals, and real-time status updates throughout the origination process.

Big Data Analytics Will Drive Mortgages and Property Valuation

The mortgage industry has become information-centric and highly competitive. Firms that understand mortgagor behavior and industry trends are managing to survive better than the others. Sophisticated big data analytics is central to this trend. ‘Big Data’ involves large and complex data sets, which yield surprisingly detailed insights. But the immensity of data necessitates special technology to process it and draw meaning out of it. Big data includes customer data captured from various sources, data bought from third parties like credit-rating agencies, and data from web, mobile, and social sites. To comply with regulations, there is a need to maintain account-holder information in the system for seven years. But for better reporting at the loan level and borrower level, data needs to be maintained for longer periods. Thus, data at all levels—origination, underwriting, fulfillment, servicing, modifications, bankruptcy, and foreclosures—keep swelling into terabytes. Then there are numerous variables that influence property value. Data keeps growing massively at micro and macro levels. Big data helps appraisers, lenders, and investors to estimate the present and future values of any real estate. It helps to better understand where markets are headed and make smarter decisions. With legacy systems, 80% of an appraiser’s time is spent on data entry. The process is tedious and prone to errors. The support of big data in an appraiser’s software helps make accurate valuations many times faster than it is possible with legacy systems. That will transform the nature and productivity of the appraiser’s job. Let’s see how big data helps the valuation process: It provides better insights on market environments that enable appraisers to comprehend growing and sinking markets in depth. It helps appraisers to address inconsistencies. Currently, the dependence on too many data sources creates misalignment between appraisers, lenders, and investors. Big data technology can collate all the different data sets, and create better objectivity and transparency. It helps them create detailed and compelling graphs and illustrations that make information more digestible.   So what happens if an appraiser chooses to ignore big data? There are huge implications related to market risk. Big data can provide immensely insightful predictive reports that highlight dangerous or favorable trends like high debt-to-income ratios, unusual spikes in value and more. However, big data intelligence is not a substitute to human intelligence. Rather, it is a highly powerful supplement to it. Machine intelligence can do huge volumes of complex calculations at astounding speed and deliver reports. But to understand the implications of those reports and to take wise decisions, human intelligence and practice are important for appraisers. Big data helps them do their job better with respect to speed, efficiency, and accuracy. Efficiently performed big-data analysis through SaaS (Software as a Service, also known as cloud-based software) can be used by anyone with internet connectivity. This facilitates data entry from the verification site, and it can be continued seamlessly outside the office setup or during travel. Many data fields can be auto-filled or imported from databases. This eliminates redundant data entry and manual errors. Overall, it reduces manual labor, processing time, and rate of errors, thus reducing the need for review appraisals. That means huge leaps in efficiency in spite of difficult market conditions.

When the right technology is out there, who needs a mortgage broker?

The traditional lending process required a middleman who could handhold consumers through reams of paperwork. 30 percent of loan originations in 2006 were through brokers. In 2014, it has come down to 10 percent. Where are all the brokers going? After the financial crash, many left business. The Consumer Financial Protection Bureau (CFPB) put in rules, which still prevent brokers from increasing their incomes by pushing clueless consumers into expensive mortgages. The CFPB rules also disallow brokers to extract commissions from borrowers and lenders simultaneously. Such developments have made brokering a less luring field in the US. In addition, there is the reverberation of advanced technology. These days, technology helps consumers avail mortgages through personal computers and mobile devices. Online-only mortgage firms like QuickenLoans, Lenda, GuaranteedRate, and Sindeo are beginning to dominate the lending industry. Even big traditional banks are losing market to them and mortgage brokers are finding it increasingly difficult to hold relevance. Mortgage software: the champion of multi-tasking Mortgage software plays the broker, the loan officer, the assessor, the reviewer, and the gatekeeper, and processes become swifter. The costs of processing shrink remarkably. Noticing the lightning pace at which online-only lenders are growing, traditional lenders are building their online lending models. Intelligent rule-based mortgage software can provide a simple online form to submit applications. Applicants can submit their income proofs, identity proofs, photographs, and signatures online. The software retrieves the applicant’s credit scores from credit-rating agencies (third party) and check for eligibility. Such a system can also parse credit behavior of applicants and offer risk-adjusted rates. Speed, cost efficiency, and convenience on the cloud Taking your consumer lending system to the cloud makes the entire processing and loan disbursal complete in about 15 days. With legacy systems, the steps might take more than a month. Legacy banking systems involve volumes of pages to be filled by customers and call for help from a knowledgeable person, who in this case, happens to be the broker. With software as a service deployed over the cloud, only relevant forms and fields, based on the kind of loan applied, show up. Many of those fields get auto-filled from the database related to the applicant due to access to big data. This saves a considerable amount of time, and ensures accuracy by evading manual entries. The information can be updated in the central database and reported to credit rating agencies. These steps occur quickly and seamlessly, avoiding many layers of redundant data entry and wasted manpower. The gain goes to the lenders and the borrowers. The pain goes to brokers and other intermediary officers. However, this model is here to stay. More and more banking institutions are turning to automation with robust software to improve business and customer satisfaction.

Five Ways in Which Big Data Helps You Grow as a Lender

Everyone is after big data these days, thinking more data means more success. On the go, they realize it is not data alone that matters, but also the tools to manage and analyze them. A recent survey by Gartner Inc. found that in 2013, 37.8% of North American organizations had invested heavily in big data. The proportion rose to 47% in 2014, and is expected to be 74% by 2018. Customer information is particularly important to the lending sector. As regulations increase, finding the best customers should not involve overheads that don’t translate into profit. That is why big data is a vital aspect in technology that supports lenders. What lending institutions can do with big data –  Security and fraud detection Big data technology identifies patterns buried in data and gives a holistic view to customers. It makes predictive analytics an important part of banking software. By clustering information, technology can help to distinguish suspicious activities from others. This pre-emptive intelligence helps you eliminate fraudulent transactions. Risk Management An integrated finance and risk management data platform can quickly address new requirements. This will facilitate new regulations for better internal management. Offering the right mortgage is necessary because it minimizes the risk of defaults and revenue loss for lenders. Offer personalized products Integrated processes help understand customers’ spending habits, and identify online channels they use and who the key influencers are. This helps take the right mortgage plans to the right people. For some borrowers, you already have their basic data and financial profiling. If they have enquired somewhere else about refinancing, or listed their house for sale, or advertised to buy a new house, you will get an alert through big data technology. That is the most opportune time to offer them new products matching their new needs. Group borrowers for better targeting Big data intelligence helps to classify potential customers based on their buying behavior, interests, age, purchasing power and more. This can boost the response rate to sales, promotions, and marketing campaigns. Compliance and regulatory reporting The Dodd-Frank Act requires lending firms to document everything that goes into the deal through a deal monitoring system. New generation data technology can ensure thorough documentation and automated compliance with regulations. Big-data based subscription to software services will allow your system to stay updated on new regulations as well. Big data is all the data possible to be related to the daily life of your potential borrower. It comprises customer data recorded at your organization, data that the systems harvest from mobile, social media, and ecommerce sites, and data that you can buy from data vendors. You can also avail credit ratings from relevant agencies to maintain in-depth knowledge about your market. FAQs – Tavant Solutions How does Tavant leverage big data to help lenders achieve growth?Tavant uses big data analytics to identify new market opportunities, optimize pricing strategies, improve risk assessment accuracy, personalize customer experiences, and predict market trends. Their platform processes millions of data points to drive strategic growth decisions. What big data capabilities does Tavant provide for lender expansion?Tavant offers customer segmentation analytics, market penetration analysis, portfolio optimization tools, predictive modeling for loan performance, and competitive intelligence dashboards that guide strategic growth initiatives. How does big data improve lending decisions?Big data improves lending by analyzing alternative data sources, identifying patterns in borrower behavior, predicting default risk more accurately, enabling dynamic pricing, and providing insights into market opportunities. What types of data do lenders use for growth strategies?Lenders use demographic data, transaction histories, social media signals, economic indicators, geographic data, competitor analysis, customer feedback, and behavioral patterns to identify growth opportunities. How can small lenders compete using big data?Small lenders can leverage cloud-based analytics platforms, focus on niche markets, use alternative data for underserved segments, implement automated decision-making, and partner with data providers to level the playing field.

Mobile Application Security

I have spent 12 years working in Mobility with many mobile platforms one could think of like BREW, Windows, QT, Symbian, J2ME, OEM proprietary mobile platforms, SHP, Android, iOS. It has been an amazing journey. Started with devices with few KB of RAM to devices now running 4 GB RAM and above. All these years and for all mobile platforms there has always been a significant common ground. It was valid then, valid today and will be valid forever – Mobile Security. Many businesses today are adopting mobility to streamline process, increase employee productivity, aiming at rapid growth. Gartner report shows that companies which are adopting mobility have simply increasing their reach by 18%. Mobility is playing such an important role, what starts bothering the enterprises is the security. Gartner reports suggest that about 75% of Mobile Applications are prone to security breaches due to wrong security practices adopted while developing a mobile application. This prediction is alarming and makes it compulsory for the developers to be well aware of the threats and methods to mitigate the risks. Below I am listing some common pitfalls and how I went about alleviating them. 1)     Weaker server side API: While writing one of the web-service using Express and NodeJS recently, I forgot to build security for my API’s exposed for the Mobile Client. Anyone who is aware of the API could exploit it. It made my server immediately prone to a variety of DoS attacks 1 Here I provided the hackers with a plated opportunity for Man-in-the-Middle (MITM) attacks. I immediately patched this up using secure coding practices, limiting API access to authenticated users only. 2)     Susceptible data on the move: While developing the web service I made one more error. I used HTTP exposing my server once again to (MITM) attacks. Most developers believe that just by using HTTPS this problem will be solved but they are wrong. This problem should be solved by using certificates signed by a valid CA, Certificate pinning or HTTP Strict Transport Security. In a typical SSL usage scenario, a server should be configured with a certificate containing a public key as well as a matching private key. I have seen many application developers when using HTTPS will accept all certificates as shown below: SSLContext sc = SSLContext.getInstance(“TLS”);   sc.init(null, trustAllCerts, new java.security.SecureRandom());   HttpsURLConnection.setDefaultSSLSocketFactory(sc.getSocketFactory()); Do not trust all certificates and don’t use self-signed certificates. For a good understanding, you can refer documentation2. Payment, banking, and enterprise application developers should make sure that they rely on above mentioned methods for rendering their data safe when in transit. Weak Authentication: Use Digest authentication over Basic authentication. Digest authentication communicates credentials in an encrypted form by applying a hash function to the username, the password, a server supplied nonce value, the HTTP method, and the requested URL. Whereas, Basic authentication uses unencrypted base64 encoding. Basic authentication is generally used where transport layer security is provided such as https. Try to safeguard your API with authentication such as token-based authentications.  Make sure the incoming HTTP method is valid for the session token/API key and associated resource collection, action, and record. For example, if you have a RESTful API for a library, it’s not okay to allow anonymous users to DELETE book catalog entries, but it’s fine for them to GET a book catalog entry. On the other hand, for the librarian, both of these are valid uses. 3)     Susceptible data at Rest: Programmers often believe that no one can have access to their application database. Like in Android, usually application database resides in the /data folder which is not visible to a normal user. Rooted devices easily provide free access to this database. Hackers can also get hold of this data using platform vulnerabilities. Developers can use various encryptions to safeguard data at rest. 4)     Susceptible visible data: What I mean here by visible data is the text, image, any mime type rendered on a screen which a user can see. Sometimes enterprises don’t want their data like emails to be copy pasted, forwarded, cached, etc. Programmers should be sympathetic with such needs and utilize Mobile device management features. iOS, Android and Windows platforms boast of a robust set of MDM features. 5)     In-app vulnerabilities: Native applications on Android and iOS are sandboxed normally giving them a comfortable secure environment. I was once working for a big security company and learnt running a cron application validator or a data validator a good option to safeguard an application against malicious intents. Programmers need to be extra careful when developing on hybrid or cross platforms. These platforms can also inject security vulnerabilities of their own. For eg: hackers increasingly aim for cross-platform vulnerabilities. When using Webview we need to be well aware of this3. 6)     Not using Proguard: Prevent your mobile application from reverse engineering and malicious injection. Proguard should be enabled for all your mobile applications in production. This blog is just an introduction on above discussed points. My aim was to keep it simple and easy to remember. It will soon follow-up with detailed write-ups on each topic. Please feel free to inbox me. References: 1)      https://en.wikipedia.org/wiki/Denial-of-service_attack 2)     https://www.owasp.org/index.php/Certificate_and_Public_Key_Pinning 3)     https://securityledger.com/2015/08/the-challenge-of-securing-rest-apis/ 4)     https://www.google.co.in/work/android/ 5)     https://www.apple.com/support/business-education/mdm/ 6)     https://tools.ietf.org/html/rfc2617

Architecture of a Massively Scalable Distributed ETL System

An Extract, Transform and Load (ETL) tool needs to be robust, scalable, high throughput and fault tolerant. Very much like an e-Commerce transaction system. Designing such a system on a distributed computing backbone can be extremely rewarding, given that mid-size to large organizations might be collecting data from multiple sources and bringing it all together into an integrated warehouse—resulting in thousands of batch and real-time jobs running during the course of a day. For example, retailers collect inventory, sales, finance, marketing, clickstream, and competitor data multiple times a day. But aggregating this data by running ETL jobs, only once daily, can slow down decision-support systems and rules engines, which must feed essential decisions (like dynamic prices) back to the system to control demand. For many e-commerce analytics and data-mining solutions, a slow ETL tool might prove to be a huge bottleneck. While commercial and open source tools help implement such workflows, it is often better to consider a homegrown ETL tool based on good design and distributed-computing principles.   Learn how to build your homegrown ETL solution and use a task queue to scale the tool horizontally. Download the whitepaper to read more: http://lf1.me/Ncc/  

Video Ads are a Huge Hit with Millennials

In a study identifying major celebrities popular among the youngsters (aged between 18 and 30) YouTube superstars topped the list. Millennials have been found flocking mostly to videos recently. These video sites are reaching more than any other networking media across people within the age group 18-35 (Source: Sprinklr.com). Every year the time spent on watching videos is growing by 60%, average video watching on mobile being higher than 40 minutes. This provides a huge opportunity for advertisers to specifically design ads for videos targeting millennials. The advertisers on video channels like YouTube have grown higher than 40% and the big companies are trying to capture millennials on other video sites as well (Defy-sponsored survey in 2014). It is the time spent by millennials on YouTube and Google Video that has resulted on top brands (rankings by Interbrand) spending almost 60% more on these channels than they did a decade ago (around 2005). Although site owners are not explicitly coming out with revenue figures, the available data indicates there is an explosion of demand for video advertisements. Advertisers have found millennials spending substantial time on these sites. Also, Google has said there is a huge growth in revenue from YouTube recently. The influx of advertisements has made many video sites turn their platforms more appealing for advertisers. They created a computing systems and dashboards where marketers can compare effectiveness of video ads with television advertising. A recent survey on millennial women found the main influencing factors for their shopping decisions to be websites, social media and word of mouth. See figure below. Source: AdWeek Guided by the huge popularity of video networks, many renowned brands on television and the internet are coming up with new video channels. They are trying to cover topics that interest the age group of 18-49 mainly, some specifically catering to the audience looking for answers to questions continuously. For brands trying to capture this vibrant and media-proactive group of millennials, video advertisements offer immense potential. Using a comprehensive campaign management solution, you can design your content and run it across the video networks to find maximum conversions happening within a short time. Make sure your products and your ads match the intellectual and emotional worlds of these millennials, and your job is almost accomplished.

Automated Mortgages: Just a Click Away

  Mortgage lending processes are becoming quicker and user friendly. Thanks to web and mobile technologies, loan applications and procurements are entirely online and do not involve any human interaction. Online-only originators like GuaranteedRate.com, QuickenLoans.com, and Sindeo.com have websites and mobile apps capable of doing everything that the sales agents and mortgage brokers traditionally did. The uncluttered user interfaces and intuitive algorithms can guide all applicants smoothly through the entire process. Looking at the times ahead, every mortgage firm is trying to offer its services online. Many firms are struggling with implementation of their digital versions, and yet, they are wondering why customers are not embracing them widely enough. It’s all very simple: if, at all, you choose to go online, do it right. The last thing you want is embarrassment in a new venture. Here’s the right way to go online with mortgage lending: Wondering what the smart online way is? Self-service online models have intelligent features, and they are designed to keep absorbing more intelligence day by day. Agile development of your online services should make it increasingly user friendly, so that no applicant feels like abandoning the process half way. Let’s see the key differentiators of a good mortgage-lending website: Assistance in planning: Built-in calculators should help mortgage customers try various loan plans, durations, and repayment options, and finally select what best suits their situations. Data collection: a) The system should be able to collect all necessary data, and show up only the necessary data for an applicant. What data is needed depends on the type of loan selected. Difficult or confusing fields should be supplemented with tips and suggestions. b) Photographs, scanned images of signatures, identity cards and other relevant records should be submitted online. All that should be archived with relevant meta tags that help in identifying and retrieving them later. It is important to find the latest credit reports of applicants. Web widgets should be connected to credit-rating agencies and they can fetch the report of the applicants even as the applications are being filled out. You should be able to provide instant responses to applicants as to whether they qualify or not. Long verification processes only discourage mortgage customers. Predictive analytics about the probability of defaults, delayed payments, or non-payments should enable you to offer risk-adjusted rates and nullify the potential risks. Normally, the entire process should be completed and the amount disbursed within 15 days. All the actions need foolproof security and privacy around them. Besides that, personal financial management tools are a great value addition, as they can help customers make wise decisions in a convenient way. The right online implementation of your mortgage banking system can save you from high payroll expenses. Cost and time-efficiency is of utmost importance, and going online should give the client a happy experience. To thrive in a complex and constantly evolving business environment, firms need to improve their systems constantly and maintain the habit of innovation. FAQs – Tavant Solutions How does Tavant make automated mortgages accessible with simple click-based processes?Tavant provides intuitive mortgage automation platforms with one-click applications, automated data verification, instant pre-approvals, and streamlined digital workflows. Their user-friendly interfaces enable borrowers to complete mortgage applications with minimal clicks while sophisticated AI handles complex processing in the background. What automation capabilities does Tavant offer for mortgage lending?Tavant offers automated income verification, property valuation, credit analysis, compliance checking, document processing, and decision-making capabilities. Their platform can process up to 90% of mortgage applications automatically, requiring human intervention only for exceptional cases or complex scenarios. How automated can mortgage processing become?Mortgage processing can be highly automated, with modern systems handling application intake, document verification, credit analysis, property valuation, compliance checking, and initial underwriting decisions. However, complex cases, regulatory requirements, and quality control still require human oversight. What is one-click mortgage approval?One-click mortgage approval refers to streamlined digital processes where borrowers can receive instant pre-approval or preliminary decisions with minimal input, leveraging automated data verification and AI-powered risk assessment to provide immediate feedback on loan eligibility and terms. Are automated mortgages safe and accurate?Automated mortgages use advanced AI, machine learning, and data verification systems that often provide more consistent and accurate decisions than manual processes. They include robust fraud detection, compliance checking, and audit trails while maintaining human oversight for quality assurance and complex cases.

The Era of Customized Mortgages

Customized mortgages have always been available from small mortgage firms, but today their popularity is growing in a world full of big belly banks. Organizations have realized that every service is now made customer-centric, and mortgaging is no exception. The needs and objectives of customers vary and so should mortgage plans. When customers opt for a loan or pursue a refinance, lenders can approach them with tailored mortgages that sync with their needs and capacities. Since the market crash last decade, customers are very skeptical about availing loans. Consumers have started preferring short-tenure mortgages to be able to close loans quicker, and enjoy smaller interest rates at the same time. Although the borrowers have to pay bigger installments, shorter tenures help customers save money on the whole loan. They can invest it somewhere lucrative. But there is also a section of consumers who don’t want mortgage liability to be a burden on their day-to-day lives. They settle with longer tenures and slightly higher interest rates. Why should lenders offer customized mortgages? Intense competition and stringent regulatory criteria are stifling the lending market. To beat competition and attract customers, it is vital for organizations to understand and cater to specific needs of prospective borrowers. Besides that, in this fast-paced era, individuals are looking for customized services to suit their convenience. Individualized or tailored mortgages are mutually beneficial. They drive mortgaging customers to choose lenders who vouch for convenience, rather than the ones who offer rigid mortgages. And not only does customizing increase revenues; it mitigates the probability defaults as well. Lenders are open to customers’ demands, allowing them to choose their mortgage tenures and associated interest rates, and a suitable payoff date. The initiative has encouraged customers to stick to their mortgage schedules. Often, customers are not sure about the mortgage or refinance plan that is suitable for them. As a result, they simply call their visits off! However, lenders should take the first step and approach consumers by offering them customized mortgage solutions. Besides easing their plight, the right approach will make your offer difficult to resist. Customized mortgages open up a whole world of opportunities for lenders. That said, they need to be equipped with appropriate technology and analytics that project customer needs, interests, behaviors, and emotional drivers. That will help create tailored mortgages and mortgage services. Lenders should customize mortgages based on customer segments. The segmenting largely depends on mortgage objectives (home loans, housing refinance, personal credit and more) and financial capability (credit scores and gross incomes). Lenders can provide individualized solutions like joint loans (for couples, etc.) and low-interest mortgages or bridge loans. They help consumers make payments without any glitches. As long as lenders and financial institutions remain flexible and treat consumers and their requirements differently, the mortgage ecosystem will be friendly and favorable to both sides.

What’s So Cool about OOH Advertising?

It consists of public display ads, both digital and print, but the plus side is you can choose your OOH ads to be displayed at particular locations according to how demographics travel run errands. Digital displays can be easily controlled in real-time, according to the movements of people with specific tastes and needs. OOH has evolved, thanks to big data! Big data allows the discovery of detailed information on specific groups of people. Based on that, OOH campaigns can be displayed to prospects from particular cultures, age groups, and professions at specific locations, and thousands of them can be targeted once you are aware of their movements. A greater-than-life ad is something no one misses. Outdoor media platforms may include posters, billboards, public vehicles, kiosks, etc. OOH is cost-effective OOH advertising can be cost-effective to promote a brand or service. An OOH advertisement is a one-time investment. You can ideally keep an ad running for both, short and long terms, depending on context and objective. The price you pay for OOH ad spaces largely depends on popularity, footfall and other related factors. However, once you mine data, a location relevant to your demographics may be available at a surprisingly affordable cost. And that happens quite often. OOH has better reach It is true that personalized ads have a conversion rate of 80% on average. However, viewability and fraud have been major challenges in the recent past. Although many OOH “impressions” may be irrelevant, a whacky ad gets people’s attention. That gets the word around. Moreover, with big data, the right prospects can be reached at strategic locations quite easily these days. Outdoor ads reach a wider range of viewers. Compelling viewership is one of the highest advantages of OOH advertising. Programmatic advertising has been a remarkable development in the last few years, but affordability, scope of creativity, non-stop exposure to audience, and potential reach to large audiences are some undeniable advantages of OOH.

US Housing Finance Recovery: What’s the magic behind it?

The trillion-dollar US housing finance industry was apprehending a low growth rate for many years. With recession having hit in 2008, the industry was facing a dull future market and sluggish growth at best. However, with Mortgage Bankers Association’s (MBA) Chief Economist Michael Fratantoni recently revealing optimism, the housing finance industry is expecting a staggering 13% growth in home sales in 2016. This follows a reported 3.4% rise in home sales in 2014-15. The grueling years Unlike the usual statistical low expected to precede a bull market, 2008 was weighed down by apprehensions and a deluge of emotional lows. Moreover, low public confidence in the housing finance industry was expected to keep things dull for a while. As a result, the consumer lending industry for housing was left with very little to be optimistic about. Market opinions It’s interesting to note how a little change in optimism can trigger market indices to behave favorably. When it comes to opinions taking effect, credible industry experts voicing their opinions play a significant role in the market. One might argue that they are the only factors, especially in the age of television and online news. Fratantoni blamed the low number of first-time homebuyers primarily on the lingering credit crunch. Besides that, his arguments for a steady future of the overall industry seemed strongly grounded in recently observed upturns. Complex buyer behavior Buyer behavior changes constantly. It is for no one to predict what people on the fine line are going to do exactly. Even more difficult is predicting how many of them will do what they’ll do! It is true that housing indicated an upturn in 2010-11, but it was at the mercy of moneyed investors. Capital expenditure was the only source of hope in a market riddled into stagnation. However, unlike first-time home buyers, big borrowers are actually causing an upturn in the housing-finance stocks. Besides that, the 3.4% increase in housing sales last year has been a cause for interest. Analytics of interest It’s time to ask what allows an expert economist like Fratantoni to opine with confidence. What changes things for real? One should never make the mistake of thinking that mortgage bankers use some math wizardry to come up with believable figures on television news. It’s not just to make the markets work in their favor. Besides, it doesn’t work for long. Mortgage software systems can use metrics and do as programmed. Quite plausibly, mortgage bankers used in-built customizations in their software to understand what particular segments of the market were going to do in the near future. Predictive analytics software can use minute details of the market and interpret facts from an overwhelming gamut of behavioral and other data. While even detailed market information seemed to suggest negative or sluggish growth rates for the housing finance industry, what changed opinions was cutting edge analysis. It picked out microscopic details existing in reality. Predictive analytics has given many housing finance companies the confidence that is shared with stakeholders and other investors. While the stocks look good, the industry seems to have gathered momentum with fantastic confidence. Unemployment in the sector is likely to dip below 5% by the middle of 2016. FAQs – Tavant Solutions How does Tavant support the US housing finance recovery through technology innovation?Tavant supports housing finance recovery by providing advanced mortgage technology that improves lending efficiency, reduces processing costs, and expands access to credit. Their platforms enable lenders to serve more borrowers effectively, process loans faster, and maintain competitive advantages that contribute to overall market recovery and growth. What role does Tavant play in strengthening US housing finance infrastructure?Tavant strengthens housing finance infrastructure through reliable, scalable lending platforms, comprehensive risk management tools, and innovative technology solutions that support sustainable lending practices. Their systems help lenders maintain operational resilience, regulatory compliance, and market competitiveness essential for long-term recovery. What factors are driving US housing finance recovery?Key factors include government support programs, improved economic conditions, technological innovations in lending, increased consumer confidence, demographic demand from millennials, and policy initiatives that support homeownership. These elements combine to create positive momentum in housing finance markets. How has technology contributed to housing finance recovery?Technology has contributed through automated underwriting, digital processing systems, improved risk assessment, enhanced customer experiences, and operational efficiencies that reduce lending costs. These innovations make lending more accessible, efficient, and sustainable for long-term market health. What challenges remain in US housing finance recovery?Remaining challenges include affordability concerns, inventory shortages, regulatory compliance requirements, interest rate volatility, and ensuring equitable access to credit. Addressing these challenges requires continued innovation, policy support, and technological advancement in housing finance.

Steps to Successful Media Buying Strategy

There is quite a lot of serious business you need to tackle in media buying. The product has to match the right platform, go to the right prospect, at the right time to expand your viewership horizon. Achieving a high target at relatively optimized costs is one challenge all ad networks face. It may appear a seamless process in the end. However to blend in all the different verticals into one whole unit, a marketer has to follow certain steps like: Creating market strategy You need to understand what the appropriate strategy for optimized reach will be. This requires not just understanding what potential different media types have but also its messages. Which platform you will choose to place your campaign will provide the bridge that achieves the campaign objectives. Research Two extremely important parameters in media buying are cost effectiveness and reach. You need to identify your target viewers and use specifically those options in media buying which your viewers use frequently. Proper research of how different businesses are advertising on that platform, looking into different reports and case studies helps weed out platforms with limited help. Negotiate Working on the deal requires all the labor and research so that you can buy the best inventories and deliver your messages. Whether you use a) Direct media buys, b) self-serve, or c) large network buys in media, you need exceptional research and tactful negotiations between the publisher and ad serving agencies, to make the best choice from a spectrum of market options available. Delivery Platform In today’s competitive market, it’s not simply enough designing creative products, though it does work as the foundation for a good campaign. You also need to support it by distributing across different platforms, keeping in mind location and timing as critical measures. It is your location and proper timing that brings out the lead generating potential of your message. How you will build these steps and why at all, will all depend on your understanding of the industry. You need to understand that media planning is a fine-balanced activity, where each step has to be executed with due diligence. Of course, to make your task fast and smooth there are these media planning software that actually translate with more ease and less effort now.

Which Media Should You Be Buying in 2016?

Media world has gone extremely swift and agile, while taking utmost care of its viewer’s personal preferences. To cater to the personal and timely requirements of your viewers, you need to have a resource that can meet the interests of your prospect personally and fast. What’s the buzzword? There’s been a lot of buzz around programmatic, predicting it will account for half of the ad sales in digital media by 2018. If you haven’t used it for your latest ad campaigns, maybe 2016 is when you should venture in it, as programmatic promises being the future of the ad world. Advertising, more so the digital ones, are no longer the generic hoity-toity creatives you throw at some popular show or event to capture audience attention. Today, you need perfect knowledge of your audience much before you post one for him to consider buying. At the digital arena, all the task of picking the perfect advertisement specifically for that precise customer, happens within less than 200 milliseconds. Why programmatic? With programmatic, the entire process of identifying the prospect, understanding his choice, asking for the right ad, asking the ad agencies to bid and then selecting the right bid to post on his monitor happens within the time you bat your eyelids. Manual sorting is impossible within this time, where there are millions of ads to choose from. So, you need an automated decision-making process with artificial intelligence (AI), teamed up with real-time bidding (RTB). This process can be used in online display portals, video, mobile, social media, and television as well. Age of selective advertising We have come to an era, where the audience cannot be lured by the millions. Businesses will have to learn to customize their products and their display for personal level of satisfaction. Today, the advertising industry has gone highly fragmented. The best mode of resolve is by programmatic to maximize ROIs for your ad budgets. Of course, a marketer’s intuitive role will always be there, that being the root to guess a prospect’s interests and preferences. However, blending this marketing strategy with machine-learning techniques and technologies of programmatic advertising, you will not miss hitting your right target at the right hour and drive home better-quality leads and higher ROI.

Customized Mortgages and its Benefits

Every homebuyer in the US has unique borrowing needs. No two mortgages remain the same after their lifecycles are complete. Mortgage companies are offering flexible loan options, but have to comply with regulations. For lenders, mortgage customization offers a chance to transform the borrower-lender relationship and make profits in a transparent manner. Trends nowadays Borrowers are seeking options to refinance their mortgages and many lenders are offering customized plans, which let homeowners choose suitable tenures of repayment and enable them to refinance loans at a lower interest rate if required. Shared appreciation and reverse annuity mortgages People with high appreciation value for their properties can go for shared appreciation mortgage (SAM). In that case, the lender receives a share or all of the appreciation value of the mortgaged property. Elderly people have the option of reverse annuity mortgage (RAM), in which lenders can receive the repayments of a long-term loan, and wait for the property to be sold for full repayment. Banks need to identify when what works and for whom. Customers want to time their repayments based on life events like retirement and children’s education. Lenders using big data analytics are definitely displaying greater flexibility in that context. Technology for low-risk profit from customized mortgages Any mortgage is a big decision for the borrower. Flexibility on your part will definitely stand out and increase your popularity. Customers only expect to find affordable interest rates, suitable tenures, and zero controversy related to back-end fees. By using analytics and other software technology, lenders can figure out a vast array of customized mortgages, each suitable for a set of borrowers. The use of third-party data in predictive analytics has emerged to help lenders identify low-risk customizations which can be made to loans during the repayment phase. Bankers can also modify mortgage terms without risking loss by analyzing big data through a sufficient technology. Options to refinance the loan When borrowers are paying high interests, they can choose to refinance the mortgage at a lower rate and pay it off sooner. Refinancing is a preferred option when markets are volatile, but identifying such circumstances for the near future will require meticulous analysis of customer data. Technology should be able to indicate risk incidences through the tenure, by depending on third-party data integration. Closing the mortgage quicker When borrowers are paying mortgage payments for a longer duration, they can modify the plan and decide to reduce the term. This will help them to close the mortgage much earlier and enjoy peace of mind knowing they are debt free. Such an option was not available earlier. Comfort level depending on budget One of the best ways out for borrowers is to decide on the monthly payment as per their repayment capacity. Being able to choose this parameter was hardly possible. The new flexibility creates freedom to set the loan terms based on the borrower’s capacity to repay, keeping all other advantages intact. End note… With the right planning and proper homework, lenders can offer the rightly customized mortgage, depending on borrowers’ budgets. The availability of new options will have a positive impression on customers.

7 Reasons to Invest Wisely in Agile Predictive Analytics Tools

“Here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!” Thus says the Red Queen in Alice in Wonderland. This is very true of the modern business world. We live in times when business advantages are short-lived. Analyzing historical data to plan tomorrows is a kind of sluggish way of doing business. That is why predictive analytics is important. Image credit: commons.wikimedia.org Predictive analytics helps to: Earn low-risk customers Know about developments impeding repayments Reduce service cost and increase profit Provide more individually customized services Run better-targeted marketing campaigns Identify risk events affecting borrowers Improve the maturity of your very analytics These will lead your business to become more agile, competent, and profitable. You acquire some customers. Some of them end up unable to pay back, some turn out frauds, some repay only because of good market conditions, and the rest repay as per the agreement. You analyze this data and assess your overall risk profile, and based on it, you make your future decisions. Now, what about the damage already suffered? What if your analyses and risk profiling were more accurate before acquiring customers and through their repayment period? That is what predictive analytics is all about Predictive analytics software has become an inevitable tool for enterprise risk management in many industries, including banking, insurance, mortgage, healthcare, medicine, travel, and retail. Remarkably, in the parlance of analytics, risk has become almost synonymous with credit risk. The key function performed by a risk-analysis product is transforming uncertainties about the future into probabilities that can be used in business decision-making. Various techniques are used for predictive analytics. Software products rely on multiple techniques, but also on third-party data about customers so that lenders are able to identify risk levels around repayment. Credit scoring and rules-based decision making are important for risk management in financial service organizations. They need actionable and predictive rules that can bring about continuous business growth. By studying the borrowing, spending, and repayment behavior patterns of applicants (individuals and institutions), they can create scorecards. By forecasting the amount to be recovered, schedules for recovery, cost of collection, and methods of recovery, they can strategize the lending and the inventory. Thus, predictive analytics makes businesses agile and competitive. FAQs – Tavant Solutions How does Tavant provide agile predictive analytics tools for lending? Tavant is advancing embedded lending solutions, API-first architectures, real-time decision engines, and predictive analytics for market trends. They’re building platforms that enable instant lending integration across various digital channels and ecosystems. How does Tavant prepare lenders for future fintech disruption?Agile predictive analytics tools are flexible, cloud-based platforms that enable rapid model development, testing, and deployment. They support iterative development processes, real-time data integration, and quick adaptation to changing business requirements without lengthy implementation cycles. Why should financial institutions invest in predictive analytics?Financial institutions should invest in predictive analytics to improve risk management, enhance customer targeting, optimize pricing strategies, reduce operational costs, increase competitive advantage, and comply with regulatory requirements through better data-driven decision making. How do predictive analytics tools improve lending decisions?Predictive analytics tools improve lending decisions by analyzing historical data patterns, identifying risk factors, predicting loan performance, optimizing approval criteria, and providing real-time insights that enable more accurate and consistent lending decisions across all loan types.

Millennials & Gen-Z Look to the Future with Hopes of Borrowing

The first twenty-odd years this millennium will have seen two distinct generations step in to the world of money. Although the first ten years were riddled with terror attacks and a major financial crisis, gigantic recovery efforts followed and some positives have ensued under the current US administration. However, confidence in the US finance market depends on how swiftly lenders can handle the new regulations around mortgage and financing. The majority of Millennials (born 1980-1994) were already in jobs in the first decade. However, Gen-Z (born in or after 1995) is yet to play their independent role in the economy. Two distinct categories of people have emerged, one having faced the brunt of instability, and the other hoping to enter a mended world. Challenges for banks With the US job market showing signs of recovery since 2011, the lending industry should have been in better shape. However, the regulatory laws favored borrowers rather heavily. Experts predict tangible improvements in finance by the end of this decade, as hiccups with compliance fade out. Some optimistic estimates say that the job market will reach pre-recession standards by July 2016. Both generations, Gen-Z and Millennials will have enough jobs, and will be seeking loans for housing and other objectives by 2020. Understanding their requirements and offering them low-risk mortgage plans will require business intelligence. While Millennails will have entered their mid-thirties and forties by 2020, Gen-Z participants in the economy will have only started working. Low-risk financing for Gen-Z can be a challenge for banks because of inadequate data due to the age factor. With technology, it is now possible to use predictive analytics, which can notify banks using third-party data during customers’ repayment periods. That possibility seemed bleak a few years back. But owing to the abundance of information now, competitive strategies in finance are expected to keep emerging throughout the next 5 years and beyond. The role of software as a service Software as a service will prove instrumental to banks through the coming years, as they must identify their target customers efficiently. There are already well-established differences in between the two generations, Gen-Z and Millennials. As job markets and individual assets improve in the US, banks will need to develop suitable mortgaging options while complying with evolving regulations. Such intense change calls for subscription-based software development. Increased complexity, coupled with opportunity – that’s the future. Reaching prospects in such an environment will require banks to target not just who desire loans, but those who can afford it, and still make profits. While customers will be fewer than during the Bush years, the need for customer-centric banking cannot be overstated. Stringent risk mitigation will keep the US banking sector in need of rich insightful data to satisfy different types of customers. A reliable lending system in the US can lead to market recovery, but interest rates as low as the 2001-06 rates can be dangerous. How far banks can reduce interest rates, and for which customers, only sophisticated analytics, covering an increasing sphere of data, can tell with a high degree of reliability.

Here’s Why Mortgage Tech Innovation Must Extend Beyond Point of Sale

On a bright sunny Sunday in El Pueblo de Nuestra Señora la Reina de los Ángeles, in the beautiful LA LIVE entertainment complex at the MBA’s National Technology in Mortgage Banking Conference, there was a unique confluence of CIO and industry leaders.  The first annual CIO Summit sponsored by Tavant brought together leaders from within the mortgage industry to discuss challenges they face as individuals, as companies and as an industry, and to exchange ideas on how to meet these challenges and help rebuild the mortgage industry. “We wanted to create a forum where technology leaders with varying backgrounds, all vested in the lending industry, can foster relationships and collaborate to drive innovation in the industry,” said Hassan Rashid, EVP of sales and marketing at Tavant. The invitees included CIOs, CEOs, COOs and technology leaders from banks, independent mortgage companies, service providers and technologists.The events began with a “fireside chat” with Barry Libenson, global CIO for Experian, hosted by Sarvesh Mahesh, CEO of Tavant. Libenson provided the audience with a unique perspective of managing a global data center managing over 1.5 petrabytes of data. Libenson and Mahesh fielded questions from the audience and discussed topics from the challenges of managing the highest levels of data security to the increasing role of alternative credit models in the lending industry. Libenson’s unique perspective on the regulatory changes and technology innovation happening in the industry gave the leadership present a view of the changing landscape and how Experian as a service provider in the industry is adapting to meet the evolving needs. Another panel discussion at the summit covered whether and how mortgages can be originated 100% online. The panel included Jeff Javits, CIO of Fremont Bank, Stan Pachura, former CIO of National MI, and Diego Guayan CTO/COO of Employee Loan Solutions. The panel tried to define what a digital mortgage is and what it means to be paperless. They concluded that there are many challenges in fully funding a loan but the need of a consumer to get a clear, binding decision as early in the process as possible is of definite value to the consumer. “The expectation of rapid mortgage funding has already been raised with our customers, and all of us in the mortgage business need to move towards mobile interfaces, process automation, and faster overall processing time, especially if we want to capture the next generation of homebuyers,” Javits said. The panel went on to discuss how innovation in the industry, which has the ability to integrate asset, income and employment information rapidly into its processes, was now more readily available. Integrating these technologies in our consumer’s online experience is the cornerstone of providing them a new type of lending experience. The panel discussed how these innovations can and should also be clearly leveraged within the fulfillment process as well. Pachura cautioned, “As lenders focus more and more attention on providing an engaging customer experience for all borrower segments and continue the pursuit of an electronic and paperless mortgage, they must ensure that they don’t frustrate the customer in the process. “Providing transparency to an existing process that is inefficient and inconsistent after an well-organized and rapid initial approval experience will upset the borrower and likely result in negative comments and complaints.” Collectively, the group felt that leveraging technology innovation throughout the lending process was a key to how we move the industry forward.  The innovation is not just at point of sale but must be woven throughout the fabric of the origination process. Recalling the recent CFPB pilot on eClosing, Javits recounted, “An evolution in notarization might mean videoconferencing with a notary and holding your ID up to the camera. But we may be on the verge of a revolution, where biometrics combined with Blockchain authentication may eliminate the human from this process entirely, resulting in a much faster and more convenient closing without sacrificing verification of identity.” Finally, the panel concluded with a discussion on whether the industry should pursue these types of changes. Should we pursue more rapid, streamlined verification of income, assets and employment? Should we move towards a faster origination process? The active participation and exchange with the audience was a resounding “yes.” The audience overwhelmingly felt that these new approaches were in the best interest of the consumer when combined with education of the borrower. When we as bankers take the initiative to ensure the consumer understands the innovations and recognizes how it benefits them, it improves the process and results in better decisions. These innovations allow us as an industry to originate more quality loans. The audience felt it was not a matter of if, it was just a matter of when their organization would adopt these tools. The keynote event during the forum was an address from Gary Clark, COO of Sierra Pacific Mortgage. Clark focused on New Age Technology for the Digital Mortgage. Having served previously in an IT leadership role at IndyMac Bank, he was able to provide the audience with a unique perspective on how technology has changed our industry and as an executive at Sierra Pacific Mortgage he is finding new technology solutions that can be leveraged at SPM to drive innovation and growth. Clark spoke of market “disrupters” who are continuing to change the online experience. Focusing on usability, decisioning and communications, he helped the audience correlate customer satisfaction data with adoption and utilization of technology in the lending process. Clark discussed the future of the lending market and how as lenders we strive to balance the cost of originating loans by leveraging innovative tools and enhancing our processes. He concluded with sound advice on how SPM is leveraging partners in the industry, building a strong network of strategic partners who are all collaborating to drive results. The summit also witnessed the launch of Tavant FinConnect, a modern mortgage data and services hub that connects the internal and external systems of the mortgage ecosystem to enable a digital mortgage experience. Ben Sizemore, CIO at First Guaranty Mortgage Corp. said, “It was great to be a part of the inaugural Tavant CIO Summit. It provided us an opportunity to network,

Data Management Platform (DMP) – A Better Way to Audience Management

A Data Management Platform (DMP) allows you to prepare your list of target viewers on the basis of a deeply analyzed first-party and third-party audience data. It helps to target campaigns accurately to the right audience in third-party ad exchanges and ad networks. It also accurately measures which campaigns made best performance across different channels and helps to prepare more focused media buying over time. DMPs offer a holistic solution to audience management. They can universally connect all data relevant to a digital marketer. DMPs are software-driven data warehouses that receive, sort and store information, and distribute them to benefit publishers, marketers, and different digital businesses. Fig 1: How DMPs integrate and service data But, do you as marketers need DMP? If you want to do any three or more of the following activities, you need a DMP. Buy third-party data and media placements Bid on ad exchanges regularly Be fully in-control over your data assets and monitor what’s in use by the partners Prevent data leakage and maximize channelization Increase the potential of messaging, niche targeting and scalability of your retargeting Target campaigns better to enhance brand recognition, response rates and conversion Manage multiple campaigns online, on various kinds of ad exchanges, publishers and networks Manage costs in advertising and improve revenue generation With successful implementation of DMP, brands can easily retarget their campaigns based on specific audience behaviors. You can easily integrate with source of the third-party data to collect anonymous data and prepare precisely targeted campaigns. You may use data to understand what customized content will suit the target customer. You can compare and contrast your visiting audience against the acquired data sources and understand audience behaviors specifically for increased conversion rates. And, you can use centralized analytics in media performance to understand which audience took action and where you should try again. For better audience management and audience targeting, marketers, agencies, and publishers, all have used the DMPs. All platforms use the DMP technology to create a huge pool of data and better understand audience information for effective value extraction.

Multi-Channel Frequency Capping: Challenges and Solutions

Frequency capping refers to the maximum number of times an ad is scheduled to be shown to a user over a specific period of time. This will be variously set depending on media channels, platforms, and operating systems. How many times should an ad be displayed to a user? What is the optimum frequency that brings the best results? There can’t be a universal answer. The answer depends on many variables. Some amount of testing, measuring and strategizing are needed to find the optimum for each case. There are people who believe that too much of bombarding with an ad will create distaste and immunity in the audience. Keeping away from too much intrusiveness is advisable as users hate aggressive stalking. This calls for stricter frequency caps. There are others who believe that brand recall will be great when the ad is served more frequently. They argue for liberal frequency capping. One simple consensus could be that ads for brand building can be served many times more (liberal frequency capping), and ads that expect user response should be served modestly (stricter frequency capping). The type of ad, a variety of ad creatives available, and the buying model are three major factors that impact ad-serving frequency. Type: Traditional display ads like banners are not normally too intrusive. They sit somewhere, and it is easy for the user to ignore them. But innovative ad types like interstitials, pop-ups and pop-unders (screen-takeover ads) disrupt smooth user experience. Therefore, liberal capping is okay for traditional ad types, but capping should be stricter for the more intrusive types. Variety: If an advertiser has only a few creatives for display, the same ad will pop up frequently and repel the viewer. If there is a good number of interesting creatives, they keep rotating. Stricter capping is advised if creatives are fewer and liberal capping if there is sufficient variety. Buying model: Ad impressions may be bought for CPM, CPC or CPA. In the CPM mode (cost per mille—cost per thousand impressions), if the ad is displayed to the same user too many times, the allowed quota will soon be over; but the message will reach only a few people. In CPC (cost per click) and CPA (cost per action) modes, billing happens only when the user performs an action on the ad. Moral: Stricter capping is needed for CPM mode; the other modes take care of themselves. Frequency capping has always been debatable. With earnest efforts, things can still go wrong in many cases. For example, when an ad is being served through multiple ad networks, since they do not share the cookie IDs of the users, there are chances of frequency capping violations. A breaking development in modern-day campaign management solutions is the emergence of real-time bidding (RTB). RTB is a conclusive solution to the challenges of frequency capping. It provides advertisers the agility to serve the right impression to the right user at the right time—the three key variables that make frequency capping necessary. RTB gives control over the fourth important variable—the price. Thus, it creates a win-win situation for advertisers, ad platforms and users. RTB is here to stay, and frequency capping woes will go away for good. We’ll discuss more on RTB in the coming weeks.

Mobile Healthcare: Transforming the healthcare delivery

Gone are the days where a doctor digs into the pile of a patient’s case history files, reads through it and writes the prescription on a piece of paper and hangs it on the patient’s bed. Most of the paperwork in the healthcare industry today is replaced by handheld devices or computers. The advent of mobile devices and multiple apps that facilitate easy operations and reduces the workload on healthcare professionals has brought in a transformational change in the healthcare industry. Healthcare is an industry that has been impacted largely by the introduction of new mobile medical devices. June 2012 Manhattan Research/Physician Channel Adoption Study found that doctors’ ownership and use of mobile devices is pervasive, with 87% using a smartphone or tablet device in their workplace, compared to 99% who use a computer1.Surveys have shown that around 80% of physicians use an iPhone while most of the remainder opt for Android smartphones. Major drivers for the quick adoption of mobile devices by healthcare professionals include: Voice, text and video communication capabilities. Easy storage, archival and updating of medical records. Quick availability of informational resources such as textbooks, notes, guides, research findings and videos. Software applications that aid diagnosis and treatments.   Health care professionals use mobile applications for multiple purposes. One such purpose is healthcare record maintenance. Every patient while in hospital generates huge amounts of data in various forms such as lab results, prescriptions, X- ray reports and scanning images. Storing the data in multiple formats, updating it regularly and easy retrieval is a challenge. Multiple apps are available today on apple and android platforms helping doctors take informed decisions faster. Also, some companies have developed specialized apps for remote viewing of image scans. Mobile devices and health apps assist in a broad way for remote patient health monitoring of individuals with chronic health disorders. A mobile app can help monitor the entire bed of the patient based on the vital sign indicators. The alarm is raised based on the severity of the condition. Mobile GPS systems are used to track the chronically ill, elderly patients and those with a mental disorder who show a tendency of forgetting the place they belong to. Beyond the functions mentioned above, mobile devices are contributing in educational and training fronts as well. Students and healthcare professionals are increasingly relying on mobile devices for textbooks, research articles, journals, and medical podcasts and training sessions. It’s also frequently used by professionals to double-check the processes and procedures involved in diagnosis and treatment. This reduces the time by cutting off the unnecessary test procedures and mitigates the risk of taking wrong diagnostic decisions. Healthcare professionals also use mobile devices to keep themselves regularly updated about the happenings in the healthcare industry and ensure learning happens on the go. Healthcare wearables also have evolved over the years. The Global Wearable Healthcare Market was worth $3.3 billion in 2015 and estimated to be growing at a 17.7% CAGR, to cross $7.8 billion by 20202. One such innovative wearable developed recently for knee pain relief comes with Bluetooth technology and compatible on iOS and Android operating system. The electrode placed inside the brace gives pain relief for over 40 hours after the battery is charged. The proliferation of mobile devices in the healthcare industry and its quick adoption by healthcare professionals has made mobility ubiquitous. Both practitioners and patients have gained their benefits. Despite having constraints such as internet connectivity and GPS reliability for seamless information exchange, mobility has come a long way in changing the dynamics of the healthcare industry and has contributed largely to its advancement. iPads and other drugs. Medical Marketing & Media: The Interactive Guide, 2013 Global wearable medical device market growth trends and forecasts 2015-2020, prnewswire.com, 2015

WoD (Warranty on Demand) Series – Configuring Payment Rules

WoD (Warranty on Demand)  – is a cloud based warranty management system developed by Tavant Technologies to automate and optimize warranty process of global organizations. WoD’s comprehensive list of functions can make it one of the best warranty management system to resolve the warranty related problems of different organizations The high configurability of the system allows users to easily build business rules to adapt to the corresponding OEM’s warranty processes. One such key process is Claim Payment and the logic toward arriving at the payment amount. The service network structure varies from organization to organization. Contracts between OEMs and their servicing dealers may also vary which will result in their labor being different. Regulations may vary from country to country resulting in added surcharges and taxes.  The complexity starts when OEMs want to calculate the claim payment using these small adjustments and their inability to adjust. WoD has the capability of resolving complex scenarios of different organizations efficiently. In WoD, users can create customized logic to calculate their claim payment using multiple rules. Here, the claim payment is divided into three segments so that configurations can be done for every scenario effectively. The three segments are: OEMs can create and configure all cost categories that is required at the time of calculating claim payment. These cost categories will determine the values that a dealer can enter while filing a claim.  Examples of cost categories are: labor cost, travel cost, parking cost and meals cost. OEMs may configure multiple cost categories in the system but every claim may not require all cost categories to calculate payment – so, an OEM can specify the cost categories that the system will account for calculating payments. Pre-defined cost categories are linked with policies and based on the rules created, system will calculate the claim payment by selecting a policy for a particular claim. Modifying the payment information is generally linked to the cost categories selected for calculation of claim payment. This is a set of rules that an OEM may create if they want to change the payout for a particular dealer and/or machine/part claim. These attributes make it possible to override a claim payment previously calculated with basic payment rules.   As WoD brings the agility of Salesforce.com with it, organizations will find it extremely easy to use as it can be accessed from anywhere due to its cloud functionality. If we consider warranty management in a global scale – the scope is changing rapidly and the main aim of building WoD is to focus on understanding the future of warranty management. WoD is already loaded up with all new releases provided by Salesforce till date and periodic feature upgrades from our experts make it a dynamic system for resolving warranty management issues.

User Identification in Programmatic Advertising and Other Challenges

Without a ‘proxy’ to accurately identify individuals, programmatic marketing is simply unmanageable. While cookies have been the proxy for identification, it’s time to ask about the future. Programmatic experts are yet to figure out a way, but if cookies are dying out, programmatic will surely face a threat. The cookie seems to have been doing a great job. Reports from Internet Advertising Bureau (IAB) and PricewaterhouseCoopers (PWC) found total digital revenue reaching $12.4 billion in the last quarter of 2014. Programmatic is basically a combination of different kinds of technologies that buy, place, and optimize advertising automatically, and hence enables highly profitable ad campaigns. As the job of programmatic is to identify the right viewer and make that viewer see the ad, the first thing required is a real viewer. Unfortunately, some rogues in the web world are capable of misleading even the smartest software programs. Challenges like viewability and fraud are constantly troubling the world of programmatic. Most advertisers shy away from programmatic buying because of the looming presence of reputation concerns and fraud. As of May 2015, the leading hurdles in using programmatic ad buying in UK and USA were statistically evaluated from a survey: Leading Challenges in Programmatic Buying, 2015 (UK and USA) – % of survey participants More than half the traffic on websites is essentially bot traffic. It has been estimated that more than $6.3 billion damage will be experienced because of bots. Hence, out of an estimated $43.8 billion, if more than $6 billion is fraudulent activity (fake clicks through a set of automated software programs), it is a huge loss for advertisers. Huge organizations with complete set ups operate with botnet malwares to extract millions of dollars. For folks in programmatic, ad impression viewability continues to remain a huge challenge around online display ads. In order for an ad to qualify as viewable, a minimum 50% of its pixels should appear on desktop screens for at least one second. For video ads, 50% pixels should be viewable for at least 2 seconds. These challenges may be confronted by micromanaging customized platforms. Advertisers need to use technology that maximizes outcomes. It’s not enough to seek impressions or aim at a single behavior pattern for producing the desired results. It is necessary to depend on technology that executes the numerous conditions correctly for ad-space selection. By using such a platform, advertisers with different needs can improve their ROIs in clear-cut ways.

Maximizing Benefit from Programmatic Creative

Programmatic advertising is based on artificial intelligence (AI) and algorithms. Hence, hindrance to creativity is a matter of concern. Having said that programmatic advertising presents an opportunity to effectively utilize creatives by structuring the creative as a template. It means one can configure the different sections of an asset based on different audience segments. However, to develop creatives, brands must keep them compatible with their ad-tech platforms. Coordinated effort from both media and creative agencies, can help maximize the profits programmatic offers. Fig: Primary goals for optimizing programmatic creative  (Source: http://www.slideshare.net/celtra/the-rise-of-creative-in-a-programmatic-world) It is not just direct response First thing that comes to mind when thinking about programmatic is direct response. But, programmatic is no longer limited to that. Initially, inventories were restricted to indicating how many prospect-to-consumer conversions happened. However, that is hardly close to the potential of programmatic. It can develop a brand’s relationship with customers over a certain length of time. In a way, programmatic influences the kind of creatives brands must develop going by the response trends. Publishers may demand a new product demonstration or a descriptive video, something absolutely different from what worked previously. Digital media has made interaction highly personalized across various devices. You need to be extremely careful about how you move your consumers into the buying process, but brainstorming for an ad when you are close to selling is unlikely to work. Create assets that are flexible to work with in automated platforms With programmatic, your scope to decide which ad to show your prospect stretches till the nick of time. However, you need a flexible and agile inventory for that. For creative agencies, the turnaround time (TAT) is very stringent and they have to be extremely agile. If your TAT says 72 hours, it should be exactly that. Lack of advance planning makes the entire journey bumpy. As an agency, you can settle with your clients on different creative options like format, copy, color, etc. Your programmatic creative team can then change the combinations regularly. Programmatic creative is trying to monitor every second of customer response when publishers show an ad. It gives a clearer picture of brand engagement for a product. This helps in transforming the ads into more effective assets, based on detailed study.

Visual Workflow: Record Create

Application development no longer means learning to write code. Application development has reached new standards as there is a growing demand for business administrators to play the IT administrators role. Salesforce.com has also acknowledged this change and shows with a lot of development methods without coding. There is a new feature called visual workflow that provides point a click configuration for multi-step business rules. Visual Workflow, built using Force.com’s Cloud Flow Designer, allows a user to create a flow for the complete business process without writing code. It can be a series of screens to enter the values for the process or an auto-launched one which runs or performs the functionality automatically. Salesforce has various options to create or update records –in both standard and customized way. We will see how to create a record using flow: When a Cloud Flow Designer is opened, we can find different tabs available. The Canvas and Explorer tab display the elements that exist in the flow. The Palette tab displays the available element types that you can add to the flow by dragging them onto the canvas. Every flow element has three settings in common: name, unique name, and description. These elements help in various functionalities From that we will understand the elements used to create a record in Salesforce. For every single piece of data captured or displayed, need a variable to be set up in the flow, and set the Input/Output type of the variable as “Input only” or “Input/Output” or private. This defines the accessibility of the values. It is always recommended to follow a naming convention to identify easily which is meant for what. Let’s say I have to create a case from account object. For this, we have to create a flow to perform the create functionality, then call the flow from the page and then finally a button to initiate the process. Below are the elements used in our case: Screen: This element is used as the UI element to show the fields and enter values for the same. Record Create: This element is used to create Salesforce record(s) using the field values that is individually set. For more details on rest of the elements click here Steps: Create >> Setup >> Create >> Workflow & Approvals >> Flows >> New Flow  Add screen element for input values for the case record to be created, in this scenario I have added two text fields to input the values for status and origin. 2.  Next step is to create the case record by assigning the values from the screen elements and the input variable     and map against the standard fields of the case in the ‘Records Create’ element. 3.  Make the screen as the start element and connect the two elements. 4.  Activate the flow Now the flow is ready for the record creation. All we want is a triggering point to invoke the record creation from the standard page of the account record. A custom page with standard controller as Account will be created with the below tag to connect the flow that we just created <flow: interview name=”FlowName”> Pass the current account ID to the flow to connect the case to be created with the current account. <apex:param name=”varAccountId” value=”{!Account.Id}“/> Now the page is ready to launch the flow. Let’s create a custom button for calling the page just created and place the button on the page layout of the account object.

Optimize Customer Satisfaction Using Analytics!

As per the Alteryx analytics, 69% organizations use customer analytics to support core sales and marketing. 63% use analytics toward enhancing customer satisfaction and 46% use it to increase customer loyalty. Today’s business success is mostly dependent on how customers are perceiving a product on offer along with the flexibility in making the product available to customers. It starts with products being viewed on display to how it met the customer’s expectations regarding its usability and services offered for it. Companies are working toward optimizing using ultra-cautious measures. A satisfied customer is the one who makes repeated purchases from a brand and also refers it to others, resulting in positive publicity. A satisfied customer is like an advocate who publicises the strong points of a brand to the world. As per Bath Empire survey 2014, 41% customers’ purchase products as they think the prices are great and 26% go for quality and choice. Analytics also show that 27% customers fall within the age of 45 to 54 years and 24% fall between 35 to 44 years. Earlier, when there were no tools or metrics used to measure the satisfaction, the companies were unable to get the ideal reports on what to produce or on how to optimize the operations and most importantly ‘What customers want?’ They could get the answers only when a customer switched brands. According to Tony Hsieh, “Customer service shouldn’t just be a department, it should be the entire company.” A lot of terms like customer retention, customer loyalty and customer delight has been coined, and reports are gaining more focus. Companies are investing heavily in getting analytics reports with insights on factors affecting end-customer satisfaction like the attentive / knowledgeable staff, respect for the customers, easily accessible, delivery service, post-sale service, return policy; pricing; as guidance to forecast future business requirements. These details, when expressed with the help of pictorial representation, provides meaningful insights into how an end-customer perceives a product and service of a company. The image below depicts the different types of satisfaction levels that can be identified using analytics to understand the customer’s behaviour toward an individual product or brand. E-commerce companies leverage analytics to track details of customers who add products to the cart but do not checkout. It also provides details like real-time view of the number of customers accessing a website, the type of customers and their geographic location. The focus of the analytical study is to gain insights like bounce rate, exit pages, In-page analytics, Site search; navigation summary. Now-a-days, with the increasing requirement for better and improved analytics report, lot of companies are coming up with different methods using which different analytical reports can be fetched to determine future course of actions. The methods of measurement are: As per the Qualtrics, below mentioned are some of the parameters used to measure customer satisfaction. (Source: Qualtrics Blog)

How to Leverage Programmatic Platforms for Better Marketing Results?

Data capabilities affect almost every field in the global economy these days. Data and insights are the inevitable elements driving competition. There’s a huge amount of transactional data being churned out, and trillions of bytes are available about customers, operations, and suppliers. Innumerable network sensors get attached to different physical devices like mobile phones, and other digital and mechanical equipment. It helps understand, create, and also communicate in the online world. Advertising companies get to serve their ads and establish digital interaction with millions of browsers. In the process, they generate huge amounts of data worth studying for optimizing campaigns resulting in higher user engagement. The data is also helpful to drive programmatic platforms and they deliver marketing results more accurately than the marketers. Different interactive platforms like social media websites have huge user base which is generating big data. With multimedia growing, content is found playing a major role in the rapid growth of data. In this highly digitized world, people are involved in browsing, communicating, searching, distributing, and thereby, creating huge data trails. Every customer today expects and demands direct, relevant, and authentic communication. Marketers may have their restrictions, but they now have the capacity of creating experiences based on user’s preferences. However, if the content served is fragmented, irrelevant or invasive, it might drive customers away, negatively impacting their engagement with the publisher or the brand. Improved accuracy in data analysis algorithms can help prevent that. Hence, most marketers are striving to improve their ad targeting through programmatic solutions which allows them to take decision in real time based on numerous data parameters. Surveys have revealed that nearly all marketers find data to be extremely vital in efforts of customer experience and advertising. According to one survey conducted by the Winterberry Group and GlobalDMA, these figures are worth observing. Fig 1: Areas where data is used (in %)   Fig 2: Top priorities in data marketing Fig 3: Popular channels used for consumer engagement With huge amount of data explosion worldwide in all companies in all sectors, no wonder the advertising industry is strongly addicted to data.

Streamline Your Supplier Recovery Process

Many industries have been able to overcome their challenges by using the recent developments in data technology. It has helped organizations by providing them access to real-time data and streamlining most of their communication channels. Warranty service is one of the significant areas to have improved due to such developments. Manufacturers now depend on the cloud, real-time data, extensive databases, and interactive technologies. Customers expect quick turnarounds and zero hassle when they need a vehicle part replaced through warranty. Manufacturers earlier felt unprepared to meet such expectations because recovering the cost without occasional misses was difficult. Missed opportunities in supplier recovery result from sluggish processes. Although protocols and contracts between suppliers and manufacturers exist, customer often feel neglected due to disputes. This difficulty can be resolved with an IT implementation that puts the manufacturers in an interactive mode with their suppliers. To support such a system, accurate verification and rules-based algorithms are necessary. Sourcing history is an important data set in warranty technology used by manufacturers. Machine and vehicle parts can be assigned unique codes and mapped to specific suppliers for accurate identification when the need arises. Although this process can be automated, the system can be complemented with an interactive dispute management system. It will help the two parties resolve exceptions in real time. As soon as a warranty claim is raised, the verification process can take into consideration if a supplier needs to be charged or penalized for low quality products according to the contract. This is possible if a complaint is recognized and mapped to its root cause analysis. With an automation system designed to perform checks, manufacturers can reduce their supplier recovery turnarounds to days, or even a single day, in spite of accurate and rigorous verifications. Even now, many manufactures are riddled with tedious methods of supplier identification and managing inventory risk. They can be the biggest hurdles to delivering prompt warranty service, especially with the kind of consistency that will help the brand value. Manufacturers need to avoid the rigmarole of supplier recovery and also avoid delays and disputes so that turnarounds are under control and superior warranty service results from streamlined supplier recovery management. Technology plays a key role in making a difference. Warranty management systems are required to facilitate manufacturers with adequate data so that they can identify failure and standards violation patterns and never incur a loss due to their suppliers. To avoid blurry interpretations of contracts, a rules-based, interactive cloud system can help resolve issues quickly when warranty claims are being processed.

Waiting for Complaints to Correct your Manufacturing Process? Foresee and Prevent Them Now

In recent years, software intelligence has lifted the burden off manufacturers by reducing the number of warranty claims they receive. Intelligent software also recovers costs from suppliers accurately and protects you from fraudulent claims. All the data stacked across your systems can be analyzed with advanced intelligence and you can get your departments to collaborate seamlessly. But legacy systems were inept at doing that. Data collation goes beyond warranty-claims data. A thorough visualization of the complete ecosystem of numerous suppliers, assembly systems, processes, sales and service chains, and their interrelationships is important. An advanced system can hunt down the root causes of claims and unravel the intricacies: What products or parts fail more often, if more claims come from any specific geographical area, are there seasonal patterns, is any particular parts supplier responsible for more failures, is the supplier-recovery process timely and efficient… Practically, every bit of data in the supply-production-quality-warranty chain needs to be analyzed. Every bit of data can be seen from all possible angles using software. This extends to a daunting variety of data sources—supply chain, bill of materials, product life cycle records, dealer-distributor-service networks, CRM records, call center records… the list can just go on. Predictive warranty intelligence systems analyze these data and help you do at least three key things with tremendous business impact: Forecast potential issues early enough to prevent them from causing heavy losses Prioritize issues according to magnitude and urgency for the system Identify factors that cause recurring failures so that you can focus on eliminating them   By nailing the problems, you will be able to reduce the financial burden coming from warranty claims, continuously improve product quality, and build up customer satisfaction. The correctly implemented warranty intelligence system should let you get real-time information on warranty KPIs (key performance indicators) in easily understandable graphs, charts and other visuals. All of that comes with a few clicks on a very intuitive graphical user interface. In such a system, dashboards can be integrated with complete qualitative and quantitative information on critical performance metrics with respect to suppliers, factory processes, product lines, models, dealer, service centers, customers, geographies, etc. This provides a robust and informative reporting framework that covers: Warranty expense Claims turnaround time Processing efficiency Parts return efficiency Supplier recovery rate Supplier quality Cost drivers analysis Reliability analysis Reserves   The money you lose on warranty is nothing compared to the goodwill and opportunity you lose by not using warranty intelligence solutions. Warranty intelligence can help you find and eliminate root causes of situations pertaining to warranty claims. That helps to improve manufacturing practices and unprofitable supply contracts by identifying repeat claims. Warranty costs and time spent on processing claims can be reduced in the long run. Powerful analytics can convert raw data into actionable insights and aid data segmentation. Early-warning systems are implemented to track error patterns, and thereby generate inputs to improve product quality and reliability. Proactive measures for improved product quality, higher reliability, and reduced downtime enhance customer satisfaction and retention. Data-driven IT implementation improves the accuracy of reserves forecasting with accurate historical warranty data and trends. This helps you plan the cash flow easily. All in all, the latest systems can transform your organization into a new profile with a higher level of customer esteem.

How to Sell More HVAC Units through Warranty Changes?

The primary goal of a manufacturer is to enable more revenue, through product sales, and whenever possible, through services and service contracts. With standard quality products, dependable service is always a winner in the manufacturing sector. Selling more products becomes easier when your company builds the reputation for reliable, cost-effective services for the products. This is true especially for HVAC products, which need servicing almost every year. Opportunities in the HVAC sector The HVAC sector has huge aftermarket opportunities. Service is required frequently. If you supply heating and ventilation products to offices and homes, you are part of a market growing at 3.8% annually since 2010. That is a trend expected to continue till 2020, during which the US GDP will be growing at 2.2%. Market experts unanimously say the demand for service from HVAC companies is likely to go up very noticeably. There is no doubt 2010-20 is an important time to elevate your brand image by providing efficient services besides quality products. Where do you stand now? Long-term service contracts, especially with businesses, might be fetching you service opportunities and product sales regularly. However, it is important that you improve your service and its efficiency to increase your reliability. It is not beneficial that some customers get repairs done through independent mechanics and choose another brand when they want a new HVAC system. Most domestic HVAC owners prefer to call neighborhood maintenance services, because they are quicker. The question that every business should ask itself is, “Are we positioned to address that gap?” If you want HVAC customer to use your warranty system, you have to position it locally and closer to customers. You then need to have an online system accessible to customers. That will surely improve revenues, especially with the support of localized marketing channels. Can you feasibly improve your warranty services? Improving your warranty service usually seems unfeasible. Estimates tend to discourage manufacturers from taking steps to improve service turnarounds and revenue opportunities. However, customer experience is the crux. It is essential to build your system around customers to meet all their needs. Overheads can be reduced with the help of a technology-driven solution, which verifies claims accurately and facilitates cloud-based mobile communication between your service teams and headquarters. This can mitigate the very cost required for quick and dependable aftermarket services. Solutions that technology can offer Reduction in service delays and overheads are important to achieve for HVAC manufacturers to ultimately sell better. The total warranty-service cost incurred can be reduced by technology through automation of claims-validation and cross-functional integration. Long-term savings can be achieved through continuous improvement with the use of system data. Parts replacement and repair turnarounds must shrink to allow better customer experience for HVAC users. Cloud-based warranty systems will help manufacturers as customers increase.  Cloud-based systems have many advantages: They are easy to scale up or down according to business fluctuations. You just need to pay according to the volume of use. You don’t have to invest heavily in infrastructure or tools. The vendor takes care of them. You will get access to the latest technology without having to invest heavily on R&D. Your vendor will take care to provide the newest technology.   Cost-effective service can be provided when you have local service units, seamless connectivity with them, real-time updates, lower travel overheads, and integration with sales, service, and management. This also helps in achieving better understanding and evaluation of performance. An example of how warranty technology can reduce costs In 1992, warranty costs at our company for replacement, IAQ, and accessories installation work were about 2.5% of installation revenue. Beginning in 1993, we implemented a comprehensive installation quality control process for our retrofit work and realized immediate and dramatic improvement in our retrofit installation warranty costs, as well as in all of the areas noted above. By the end of 1994, we had reduced our installation warranty cost to just over 0.5% of revenue and had added a full percentage point to the bottom-line profitability of our retrofit installation work. – Jackie Rainwater, writing for Peachtree Heating and Air Conditioning Cost-effective implementation of technology The warranty technology or system should have low implementation cost and quick deployment turnaround. Implementation of ‘surround-not-replace’ and agile development practices have made IT implementation affordable. The already-existing software systems with HVAC manufacturers need to be “surrounded” with features and resources that enable cost-effective and high-quality warranty improvements; they need not be replaced. That is the first step to making warranty improvements feasible. Once better service is easier to provide, brand value and sales will see quick improvements Note Register for our upcoming ‘Building a Best Practices Warranty Management Program for 2016 – And Beyond!’ webinar by Bill Pollock and Rohit Lohan to learn how to manage your warranty costs better.

Top 6 Retail Trends to Look Out For in 2016

The retail industry has been gradually expanding over the years and has reached around $24 trillion. This number is expected to reach $28 trillion by 20181. Brick-and-mortar retailers are expanding their sales channels and moving towards building an omni-channel retail solution. This has been made possible by the emergence of connected devices and the internet of things. All these connected devices produce a huge amount of data which can be used to make informed decisions about inventory management and stock replenishment. Mobile-based retailing and the rise of a new generation of tech-savvy shoppers has boosted the industry and pushed retailers to open new channels to provide a seamless customer experience. The major retail trends in 2016 are: Internet of Things (IoT): Connected Devices to Dominate: Sensors, digital signage, location-based beacons, and innovations such as smart price tags appeal to customers demanding a seamless experience. The IoT component in the retail market is expected to grow at 20% CAGR from $14.2Bn to $35.6Bn by 20202. A major challenge for the IoT industry will be the availability of internet security as well as privacy and data protection. Data Getting Bigger: Data from sales transactions and social media enable retailers to know what and where customers buy with a great level of certainty. According to McKinsey, a retailer using big data to the fullest could increase its operating margin by more than 60%3. These insights help retailers customize their services to satisfy the shoppers’ desires and forecast the stock levels with improved accuracy. Omni-channel Retailing to Boom: Despite the complexity, integrating online and offline channels will be key to retaining customers. A study by MasterCard found that 8 out of 10 consumers now use a computer, tablet, smartphone, or in-store technology while shopping. Forrester also predicts that cross-channel retail sales with reach $1.8 trillion in the U.S .by 20174. With omni-channel retail growing, fraud management also becomes a key challenge for retailers. Forrester Consulting conducted a study on how retailers are managing fraud across channels and found that 65% of retailers believe that they lack the tools to effectively manage omni-channel fraud; which will drive more investment in fraud management. The Rise of the Millennials: Tech-savvy millennials have enormous purchasing power, are vocal about their preferences on social media, and favor a multi-channel shopping experience.  By 2025, millennials are expected to make up 75% of the global workforce. 63% of millennials stay updated on brands through social networks, and 89%5 would prefer a store with advanced mobile capability. “Mobile” – Search & Shop on the Move: Mobile apps, expanding beyond basic research, purchase and payment, have made tablets and smartphones into “shopping assistants”. Forrester Research reported that commerce transacted on smartphones today comprises 10% of all ecommerce, up from 6% in 2013 and 3% in 20126. Flexible and Visible Supply Chain: With customers wanting to buy from different retail channels, retailers are investing in technologies such as RFID to build a system-wide visible and accurate supply chain. Studies suggest that retail companies invested 29%7 of their capital expenditure in omni-channel fulfilment, indicating its importance. Retail Sales Worldwide Will Top $22 Trillion This Year, emarketer, 2014. IoT in retail market to surge at 20% CAGR by 2020, imc, 2015 Big data: The next frontier for innovation, competition, and productivity, 2011 eCommerce Forecast, 2014 To 2019 (US), Forrester, 2015 Three online retail trends for 2016, smart insights, 2015 Forrester Research eCommerce Forecast, 2014 To 2019 (US) April 22, 2015 Making omni-channel fulfillment processes profitable is imperative for CEOs, says JDA report, logistics management, 2015

Strategy and Approach for Successful CMS Migration

Many big publishing houses that use custom, propriety or even open source implementation of Content Management System (CMS) admit that their CMS is not good enough to keep pace with their current needs. But most of them are not looking for an alternative as they doubt if a new CMS would make any difference. While this concern is genuine to a certain extent, upgrading or migrating to a new CMS is a step worth considering. Here are few facts to consider while you upgrade. 1.    Explore and Evaluate maximum possible options. Choosing an alternate CMS is the biggest hurdle, hence one has to be extra cautious. Always hire or involve a non-biased agency or vendor for CMS evaluation. Ensure that the comparisons are quantifiable and not subjective. Make sure that all stakeholders provide their expectations of the new system before the evaluation exercise. These expectations should form the basis of the evaluation matrix. 2.    Always do a short discovery before full-fledged development Do not rush for an implementation, CMS migration is slow and should be planned through. For a publisher, the new CMS requires new content strategy, new feeds, new syndication, new integration, etc. Discovery phase is a stage where all risks should be identified and proof of concept should be developed for feasibility. End of discovery phase should be a broad level release plan for different phases. 3.    Ensure CMS users and all stakeholders are engaged from the initial stage of development The users of CMS, who are most vociferous in raising concern about the existing system, usually turn out to be excited as well as skeptical during kick off. Change is natural and good, but People’s reaction to change is unpredictable and irrational. Managing change means managing people’s fear. The best way to address the change is to ensure that they are part of this journey. The image below depicts the classical psychological reactions to change. It has been observed that the projects where users are involved very early, have high propensity for acceptance. 4. Follow the agile methodology for CMS migration Though there could be another methodology for other type of system, for Content Management system migration project, Agile is a must. Migration to a new CMS is always an evolving process where new ideas, basic requirements, must have features & good to have features – all kinds of expectations need to be addressed and managed simultaneously.  Any project team which follows the principle of agile as a sprit is very unlikely to fail.

Super Bowl Ads 2016: No, it’s not Housing Apocalypse 2.0

Super Bowl Ads 2016: No, it’s not Housing Apocalypse 2.0 It’s difficult to say who walked away with the biggest limelight: Von Miller or #puppymonkeybaby or “Here’s what we were thinking”. No, the last one is not Housing Apocalypse 2.0. It is Rocket Mortgage’s sheer convenience, nothing less, nothing more.   Quicken Loans’ first Super Bowl Ad begins with a rhetoric: “Here’s what we were thinking: what if we did for mortgages what the internet did for buying music, and planes, tickets, and shoes? … “If it could be that easy, wouldn’t more people buy homes?”. It sparked an immediate backlash all across the country, and the Detroit-based company had to come out and clarify that the ad was meant more to showcase the convenience of applying for a mortgage than anything else. Indeed, that was the goal. But it got lost in what may now be construed, in retrospect, as a poor script and an ambitious attempt to save a housing economy through a funneling effect just because it’s now convenient to buy a home. Even CFPB could not help. CFPB’s tweet, though completely precise and pertinent, came in at a time that did little to alleviate the fears. At least, it did not mislead. You have a right to Know Before You Owe! So how fast is too fast a mortgage? To all those that found the advertisement a little unsettling, please be assured that you are not going to get your mortgage funded in 8 minutes, irrespective of who says what. It still takes way more than four weeks in most of the cases and maybe 3-4 weeks in some exceptional cases. And to all those lenders and other participants in the industry, here are five key things that we should remember: 1.    Let’s not confuse a potential home buyer: The borrower wants to buy a house and that alone is the end product. Mortgage is just the means. So it doesn’t matter whether it’s a “Digital Mortgage” like Guaranteed Rate’s or it has a “7 day processing” like Movement Mortgage’s or it can be “approved in less than 8 minutes” like Quicken’s, this kind of messaging may be great achievements for the mortgage industry or even greater marketing vehicles, but is not helping solve the confusion in the borrowers mind and we, as an industry, are leaving a door wide open for an alternative lending and disruptive company to come in and swipe the market. What matters to an end consumer is how soon and how conveniently you can close a loan end-to-end from the initial touch point. 2.    Digital Mortgage, nothing to brag about, it’s an expectation: A digital solution is a challenge to the mortgage industry. Neither do people outside the mortgage industry know why it’s a challenge nor do they care. It is an expectation that you provide as much of a digital solution as you potentially can. But in the end, what matters is how you have helped them through the process, doesn’t matter whether the help was provided through a purely digital channel or partly digital channel. Strike the right balance so as to create a great customer experience. 3.    Let’s not downplay the role of advisors in an otherwise intimidating process: The mortgage process is perceived to be cumbersome, arduous and intimidating. There are some people that know it all and are willing to take a mortgage process completely on their own through digital solutions. But most of us, know very little to make an informed decision. We need to talk to someone experienced to ensure that we are taking the right course, selecting the right products and leveraging our profile to ensure the best deal. The process is so documentation heavy that most of us do not want to go through those endless pages. Having someone to talk to during this process still plays and will continue to play a big role in such a lifetime investment. And that’s what CFPB was trying to do in their messaging of Know Before You Owe. 4.    Leverage the entire eco-system: What matters most is how you leverage all the possible touch-points in a rehearsed and well-orchestrated manner. It does not have to be completely digital if you cannot provide a good experience. The focus should be on how we are bringing together all our micro-channels to project one brand to the customer when she tries to reach the loan officer, realtor, call center or her mobile device. Companies that are able to tie all these dynamic pieces into one will be able to create compelling user experiences. The journey to a house still takes more than 30 days for all practical purposes. It’s a long process where communication and transparency is key. 5.    Focus on streamlining operational efficiencies: The behind-the-scenes operational aspects of a mortgage process require tremendous focus and attention to the details. The cost of producing a loan has been skyrocketing and we are still unable to put a real time countdown to how many days are truly remaining in a typical mortgage application-to-funding life cycle. It’s kind of unsettling that we haven’t gotten our arms around this as yet. Lenders probably need to invest a little more on leveraging business analytics to detect and eliminate those operational inefficiencies. This will be critical to getting a tight grip on the underlying processes, providing transparency and real time status update to the end borrower and thus help create a great customer experience. So, in short, lets demystify the mortgage process and make it as easy for the end borrower as possible, which was anyways the real intention of Quicken’s Ad. So let’s move on to the real Super Bowl. Even though the Super Bowl may not have been the most gripping, it was one of the classiest displays of defense. But if you still cannot unsee the #puppymonkeybaby, please have a gatorade while you are thinking deep, then kiss Papa John and then drink Budweiser till you crash. Yes, those were the 3 masterstrokes of

Predictive Modeling for Advanced Audience Targeting

Media advertising in this decade is superfast and all about accurate customer engagement. Smartphones, iPads, and the internet bring unprecedented access to information, and publishers are facilitating carefully customized content that caters to old-fashioned as well as new-age audiences. Content is developed with focus on customer needs and brand loyalty. Tailored product or service information is vital. Predictive analytics help publishers understand customers better across all services and brands. With predictive modeling, audience data is sorted for in-depth and actionable insights. That provides recommendations on how to target audiences and engage them whenever required. The objective is always more revenue and greater customer loyalty. Challenges that brands are facing without predictive modelling: Lack of incentive in sharing information across channels and brands Generic product-based information which doesn’t benefit like audience-centric information can Expensive rates for integrated internal database of multi-channel users   These cause inaccurate marketing and lead to failure in audience engagement. With cloud-based predictive modeling, brands can achieve what they need. They can target their audiences better and achieve higher ROIs. Here are the reasons: -Analytics reveals customer preferences to develop marketing engagements with exclusive data sorting. This results in tailored inbound and outbound interactions with the most relevant contextual data. -Data-driven mechanics can analyze those elements that drive customer loyalty and customer spend at the micro level. Publishers can invest in customers having the highest potential towards lifetime value. -Analytics can optimize decisions about customer service to improve measures of customer satisfaction and retention. Using historical data, the technology can sort information to identify those elements that churns and retains customers. It further provides the insight that can help in offering proactive service or offer required for customers moving out. – Predictive modelling comes with features like integrating survey information. A similar approach is used to deliver customer experience across all channels. This not only helps in capturing customer responses to enhance the models continuously, but also towards relevant and consistent audience journey. Audience engagement in the digital market has to be relevant, consistent, and personalized. Real-time data is no doubt expensive, but hardly useful without predictive modeling software. Developers can provide technology as well as real-time data cost-effectively, but data alone makes very little sense in terms of cost-to-benefit in the market.

Content Management Solution to Enable Easy Ad Inventory Access

Enterprise content management systems are now using the features of ad inventories and helping publishers generate high ROIs. You no longer need to spend much time responding to requests for proposal (RFPs). You now have an ad inventory that’s extremely user compatible and helps you identify the right package for your prospect. With an enterprise content management (ECM) system, you can reduce huge layers of spreadsheets and translations. You can focus solely on whatever you sell best and provide ready explanations to your customers. Understand and classify your inventory Software can be designed to understand your inventory and make smart automated decisions based on understanding. You no longer have to sit with queer acronyms and naming conventions. You can actually report, prioritize and push a campaign in real-time. Using predictive analytics and intuitive workflow, you now reduce human error using multi-selection fields that can be customized. With ECMs you can now classify your inventory with the same concepts used for selling it. The software can provide an innovative approach to describing inventories using natural language. You no longer need to use complicated conventions to name the ad servers. Moreover, with workflow and UI, less time is spent on operations and more time on generating revenue. Configuring complex pricing structures The ad industry has a complex pricing structure. The more you mix the media inventory involving different currencies, the more complex it gets. To price every advertisement accurately, especially within a robust inventory, a price management solution may be required. This solution has to be capable of catering to the complex demands from the big publishers that manage a wide range of advertisements. Even with the most motivated team in product management, it’s mostly difficult for managers to find out which product makes maximum sales and which one works best when collaborating with another product or service. Custom content is now using price configuration modes to easily create and update different rates. Content management solutions can also adapt to different rate cards where different rates and currencies can be assigned. This actually leads to much faster and specified content distribution, capable of targeting a niche audience. It is a good decision to look for software that uses comprehensive ECM solutions to collect invoice, inventory information, sales and customer relation management (CRM) in an integrated way. This helps analyzing the inventory’s value with easily made reports and properly direct your cost criteria.

RTB Integration for Better Savings and Enhanced Media Experience

Real-time bidding or RTB became possible with inventory access and big data. Now the strategy is to enable media buyers with access to audience data, and third-party data suppliers have had some success. Simultaneously, technological advances in digital marketing have made online inventories more accessible. The benefits of RTB have been around for media buyers, irrespective of their size and experience. When data and inventories merged earlier, they offered quite a few options among sites for running display ads. That is why selecting specific audiences from different sites was an incredible task. Even for selecting a small audience from a few sites, buyers needed to define their audiences for every site based on individual campaigns. Media buyers felt it necessary to ask every site to construct a universal set of criteria for targeting their audience, but that was obviously impossible. However, with RTB, audience data is already segmented. Using audience scaling, the objective is to display the ad to those audiences that have maximum probability of responding. Features that make RTB the most convenient option today: Almost 30% of all conversions in RTB happen with just one-time display. Influences 50% of conversion in paid search. Influence 40% of conversions in natural search. RTB’s influence has the highest impact on assisted conversions.   Cost saving with RTB For saving money and enhancing customer engagement, RTB creatives prove economical for pay per click (PPC) campaigns and they work even better. Media buyers are attempting to retarget their target audiences based on their past responses to RTB display ads. RTB is empowering media buyers to find lots of options that help in specific audience targeting. The audience specificity actually helps in huge cost savings and also enhances the end-user’s media experience. Additional information: Your software technology supporting media buying and brand marketing should be tested regularly for the best outcomes. If you want to improve convenience, resource utilization and savings from media operations, make sure you are offering a better user experience through technology and gaining an edge in predictive analytics regularly.

Operational Efficiency and Excellence with Media Planning Software

Media planning can be quite a Herculean task. Cross-channel communication using multiple mediums like social media, mobile, email, etc. is difficult. Additionally, understanding and meeting the media needs of a business is not easy. Therefore, for success, it is vital to plan the entire media campaign. To meet challenges, media planners take a consolidated approach towards managing a campaign. Software solutions help them in this effort of running sophisticated campaigns for brands globally. These Media Planning Software solutions use data from 1st party and 3rd party on premium inventory and cater to all formats, devices and channels. The real-time metrics in the software enables tracking of the economic impact, investments and performance of all the products during a campaign’s data-run optimization process. The entire software is consolidated keeping in mind the following benefits: Robust planning The media planning software delivers functionalities and tools that businesses need for programmatic buying. There are a host of partner segments created from the integrated data that can effectively target audience. These segmentations of digital format consider the key performance indicators (KPIs) according to the requirement of the given campaign. Buying design The software is designed to simultaneously process and execute the analytics and workflow of an inventory. It can instantly access open premium exchanges and/or import reserves from programmatic inventory in the software. This enables optimized measuring of the inventory streams from the same folder. Measurement metrics Measurement of different metrics may vary depending on the means used for creating them. Some can add together huge number of brand metrics available in a single folder. This is done to evaluate the audience, creative performance, inventory, and visibility in real-time. Optimization The purpose behind creating such advanced algorithms is to continuously monitor the performance of campaigns. This helps in optimized delivery of content in an effective manner to meet or go beyond the campaign’s expected goals. The tools are capable of increasing efficiencies and dynamically optimize these campaigns. There are wide ranges of choices available with web publishers to partner with ad media networks and generate revenue. Recent emergence of exchanges has provided a more optimized and effective channel to sell media content.  

Optimize Ad Server for Lower Costs and Higher Throughput

Ad servers need to be fast—so fast, that users do not notice any lag of external content being loaded. Typical ad servers select, serve, and track ad impressions in milliseconds. They offer seamless experience with lightning fast response times, even when handling thousands of ad requests and event-tracking operations every second. It is crucial that you optimize your ad server for excellent throughput (number of requests per second). Make sure your system architecture is well planned and agile. Use small components that gel together for fine performance. The advantage is, even if you find the system to be slower than expected, you can identify and replace the module that is not fast enough. That helps you remain efficient and quick, rather than having to rework or replace huge modules. Test it for performance by bombarding your application with massive traffic. When it stops being responsive or starts throwing exceptions, verify the logs, identify problem areas, and then fix them. Ad servers can be optimized by using highly concurrent event-driven technologies. Let us consider a few like Akka, ZeroMQ, and Spray. Using such technologies helps to keep the cost of ownership low. Akka (http://akka.io) is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications.   ZeroMQ (http://zeromq.org) is a high-performance asynchronous messaging library. It comprises high-speed asynchronous I/O engines in a tiny library and is backed by a large and active open-source community. Spray (http://spray.io) is an open-source toolkit for building REST/HTTP-based integration layers on top of Scala and Akka. Being asynchronous, actor-based, fast, lightweight, modular, and testable, it is a great way to connect your Scala applications to the world.   Scalability Scalability is another important aspect that affects performance and throughput. Scalability implies the system’s ability to avail new resources, so that the application runs smooth. Adding server machines for the application is horizontal scaling; and upgrading existing server machines is vertical scaling. Horizontal scaling may create problems for applications with server affinity; but vertical scaling works well independent of application design. Being concurrent in nature, Akka, ZeroMQ, and Spray enable horizontal scaling down of ad server solutions to achieve higher throughput with similar server configurations.

5 Reasons Why Tavant Fleet Solution is the Answer to Common Fleet Management Problems

Every company owning a large fleet has a challenging task of effectively keeping track of assets, utilizing assets, keeping the maintenance costs low by predicting pitfalls in business and taking corrective action. Tavant Fleet Management Solution (TFMS) provides ways to manage these fleet management problems effectively. Some key areas of challenge Tavant customers have had while using their legacy systems were: Fleet Maintenance Managing Service Contract System Integration issues and Scalability on resources Globalization Reports, uploads and dashboards   1.   Fleet Maintenance The bigger a company, the more assets it needs to maintain in top condition. To maintain huge fleets, you need relevant inputs such as engine diagnostic information and assets history. These are essential to plan periodic maintenance schedules, create and track work orders, record detailed maintenance histories and other relevant reports. TFMS helps pin-point assets that need frequent servicing thereby improving the performance of the asset and prevent damages. TFMS has customisable alerts which can help fleet managers’ make quicker and smarter decisions. Some of the advantages include: Proactive reminders for preventive maintenance activities such as tracking service records, maintenance performed, date of completion, the service technician etc. which thereby helps reduce repair costs. Engine diagnostic alerts for early signs of asset problems Progressive dashboard for data analysis to improve fleet health and lifecycle maintenance. Real-time troubleshooting: a step-by-step guide to asset users on how to fix issues. If the user cannot find a solution a Service Request can be manually created. TFMS self-diagnosis will then capture the fault code which helps fix the issue faster. Self–diagnosis can reduce the creation of excess service requests. Sample Maintenance Report 2.  Managing Service Contract Service contracts are the revenue generators for businesses. Most IT solutions will have an option either to create the contract from outside the system or within the system. The challenging part will be on maintenance and understanding what should be covered and what not to be covered under contract. TFMS has an option tocreate contract within the system and from outside the system where contract will be synced through integration and managed in TFMS. Along with contract we capture cost category level details which specify what should be and what should not be covered under contract. The Google API helps to calculate the travel distance. 3.  System Integration Issues and Scalability on resources Enterprise fleet managers work in companies that are large, handling thousands of assets, and that often experience rapid growth, particularly when they acquire new businesses. Managing the profit/loses are very difficult. For an enterprise that is growing at a fast pace scalability issues related to database, system integration and restriction on number of users are common. Tavant not only has the infrastructure to support the unique and challenging requirements of larger enterprise fleets, but supports them in every step of the way. Easy-to-use cloud-based solution allows reporting on huge amounts of data across their fleet. TFMS enables quick communication /integration between existing systems and fleet management software via Tavant APIs. Tavant APIs can be used to integrate with ERP systems or integrate driver performance for employee grading, allowing paperless timesheets and notifications. 4. Globalization A globally distributed fleet needs information on effective utilization of assets, fleet positions, routing plan, information on regulatory zones and other KPI reports for analysis. A good fleet management software should enable the frequent exchange of information with distributed stakeholders via multiple modes of communication. TFMS provides multi-language capabilities to support a global workforce and supports a web-based system to interact, communicate and track fleet-related KPIs efficiently. Notification capability to inform fleet managers with warning signals on vessel movements. Customizable reports and dashboards provide KPI-related information.   5. Reports, uploads and dashboards One common problem in fleet management is managing huge data and generating real-time reports or scheduled reports. Updating or taking action for huge data is one more challenge. Customer using manual processes to generate reports and tracking progress of a job is a tedious task. TFMS resolves this problem by providing upload facility for the huge data insert to the Fleet system. This feature offers claim filing or raising bulk service request in the system. TFMS provides customized report creation both real-time as well as periodic reports. TFMS provides download capability on search engines where customers can customize on their search and extract reports. TFMS provides each and every progress reports on resource tracking and task performed. Progressive dashboard information helps analyse comprehensive service reports to improve fleet health and lifecycle maintenance. Sample Fleet Management Dashboards: Dashboard -1 Dashboard-2

Achieve overarching customer satisfaction through Omnichannel approach implementation

“Whoever said money can’t buy happiness simply didn’t know where to go shopping,”- Bo Derek. The modern-day purchase decision and shopping experience is much more complex with the shopper having multiple options to choose from online store to teleshopping sites. To address this challenge and provide shoppers a seamless shopping experience across the channels, retailers deploy Omnichannel retailing solution. A distinctive feature of Omnichannel retailing is the integration of various sales channels including retail stores, mobile stores, online stores, mobile app and telephonic sales. This provides a unified customer experience starting before the sale and continuing even after the sale is complete. However, it’s essential to differentiate from a multi-channel experience. Essentially, this differentiator is the depth of integration of all sales channels. Most companies in today’s business invest in different engagement platforms such as Facebook, Twitter, website, etc. but still the customer lacks the seamless experience due to lack of integration. So, businesses investing in Omnichannel approach should focusing on aligning the objective, goals and messaging of all the different channels and deliver a seamless experience to the customer. Companies considering to implement an Omnichannel approach should involve all the stakeholders of the organization such as front end executives, marketing, IT, sales, etc. in strategizing to ensure that transition to the new model happens smoothly. Ultimately, this translates to providing a superior customer experience and better satisfaction. One good example of Omnichannel approach providing a great experience for customers is the entertainment giant Disney. The approach begins with a very well designed website having a good user interface. It has a real experience not only on a desktop but also on mobile devices which lacks in many other cases. After booking the trip, the user can use My Disney Experience tool that allows the user to have a complete view of the future experience. The customer can plan the entire trip starting from picking the pass to identifying the rides and deciding what to eat at which eatery. Disney’s unique customer experience offerings don’t stop here. The company provides Magic Bands or cards that can be used to do a gamut of activities including unlocking the door of resort hotel room, enter the water park, check in at fast pass entrances, connect Disney photo pass images to the account and also charge food and merchandise purchases to the Disney resort hotel room. Such a seamless integration of multiple channels to provide a comprehensive user experience is the key to the success of Omnichannel retailing. This exercise might seem out of reach to companies of small sizes. But, technology has come a long way over the past few years helping reduce the cost involved in customer engagement. So, it can be said that commitment from the company’s management, predefined strategy and working with different organizational stakeholders in tandem will surely help organizations achieve a successful Omnichannel approach implementation and thus provide a significant customer satisfaction. Implementing one such strategy, in turn, leads to customer retention and contributes to better revenue prospects.

Advanced Analytics: Solution to Traditional Media Buying Inefficiencies

Little more than a decade back, when big data arrived, it gave businesses something extra. However businesses either felt their data was inadequate or the inferences they could draw, were at best, vague. The world has come a long way from then and there has never been a better time to advertise online than now. However, it is not big data alone, or even the technologies that came immediately after its emergence. Smart analytics is relatively more recent, and it goes beyond providing detailed descriptions of your data. Advanced analytics is what data technology should ideally be. The blur before your marketing data disappears, and you can draw sanguine inferences such as what percentage of your ad spend generates what proportion of sales and from which channels. The main advantage of advanced analytics is the availability of accurate inferences in real-time. Besides automating your online advertising process, you will be able to quantify the outcome of your advertising efforts across channels. That helps you make media buying decisions with minimum risk and measure your performance without forced assumptions. You can cut costs incurred on expensive statisticians Unlike in the recent past, when big data was used by high-end statisticians to figure out the best locations, channels, and messaging for you, advanced analytics can do much better, and at a lower cost. With real-time data on multiple aspects related to your markets, advanced analytics software can deliver the inputs you require for adjusting your media buying budget. Efficient media buying involves a range of data processing techniques, but with approximations, you get nowhere in spite of large expenditures. Using predictive analytics, software is now able to provide you with accurate suggestions. Knowing and making the right cross-channel investments It is important that you realize your returns on investments that are distributed across channels. Earlier, with traditional media buying, finding it out accurately in relative terms was impossible. With advanced analytics and customized software deployment, you can assess your individual channel investments. It helps you take confident decisions on whether to expand your campaigns or stop them. Moreover, the deployment over cloud helps you with maximum efficiency in media buying, ad placing, and transacting. Measurements can be carried out every second, and the whole media buying process becomes free of uncertainties. Facing palpable shortage of data? That’s changing! Even when big data had first arrived, companies felt their in-house data was inadequate for drawing sufficient insights. Back then, many decision makers doubted if analytics would be useful for media buying. Software that was smart enough wasn’t around as yet, and even a flood of data seemed inadequate. However, with the emergence of advanced analytics, companies have realized that they can use even their in-house data—related to third parties, finances, channels, transactions and more—to generate the insights that now help them make media buying decisions with unprecedented accuracy. Software can now be deployed and used to implement advanced statistical functions—such as relative importance analysis, structural modelling, case-based reasoning and more. When deployed over cloud, such software can function in real-time to deliver insights on how to best optimize your media buying spend.

Five Trends That Will Shape the Mortgage Industry in 2016

As we embark on 2016, the mortgage industry is poised for another exciting year. Here is a look at trends and topics that will shape the new year. 1.    Interest rates are on the rise – There is potential for future rate increases from the Federal reserves. Rates are expected to rise by up to 1% in 2016. Increased interest rates may prevent many first-time buyers from entering the housing market. The millennials, already reeling under student debt could delay home purchases. 2.    The rise of Millennials – Growing up in a digital age, their set of priorities and buying behaviour is different to the previous generations. Born between 1980 and 2000, millennial purchasing power is at an all-time high. As the first digital natives, they naturally expect lenders to engage with them on digital platforms. Lenders are cranking up their tech muscle and developing digital platforms to capture this lucrative market. However, rising student loan debts may withhold them from immediate purchases. 3.    Marketplace lenders continue to transform the industry – Marketplaces are revolutionizing industries across business lines. Marketplace lending platforms match borrowers with investors who purchase securities backed by notes issued by these platforms. By adding critical functions in the middle, they are leveraging technology to unlock value, deliver scale and in the process take a significant market share. In a digital world, technology allows marketplace lenders to use advanced data analytics to make possible credit decisions, reduce risk and enhance customer experience. Marketplace lenders will continue to disrupt the market 4.    Automation is the way forward – Buying a home is a complex process.  It involves multi-layered, levels of approvals across a relatively long timeframe. Equipped with an array of options, the digital consumer expects speed across the loan application cycle. Lenders are looking to eliminate roadblocks and deliver superior customer experience. Lenders will leverage automation and adopt advanced technology platforms to automate credit assessment process, track customer sentiment, and detect fraud. 5.    Cheaper to buy than rent – Rental rates across the United States continue to rise. Rental vacancy rates are at a low for both apartments and houses. Growth in rental rates is higher than inflation, and buying is cheaper than renting in major urban markets. With an increasing need to be mobile and lower than average employee tenure, millennials may not want to commit to living in a single location. This could impact buying behaviour and keep rentals at a high. We’ll do a mid-year review to check how these trends are affecting the market. Watch this space for more.

HTML5 – Opening new horizons

The volatility of stock markets and the availability of diverse trading technologies have made the task of stock trading interesting and challenging at the same time. The advent of new platforms and applications has added a certain degree of complexity to the concept of online trading. Traders, for many years, have been relying on thick client applications for monitoring the market. Trading terminal features can be classified into ‘must-haves’ and ‘good to haves’. Must-have features include a constant connection with the exchange or clearinghouses, technical analysis support, security, and provision to create a dashboard to perform multiple actions.  Customization and multi-platform support are some of the good-to-have features. Since most of these features are resource-intensive, they did not work on browsers in the past. Nowadays, with the introduction of new technologies like Flash, Silverlight, etc., most of the features can be built for browsers. But the challenge is that these solutions continue to face bottlenecks since they are still not at par with existing thick clients. The improvements in Javascript engines and the introduction of HTML5 specifications have now made it easier to build an application on the browser, or a suitable workaround. Features Overview Things we feel excited about HTML5 introduces features that have a direct application in the Capital Markets space. By leveraging these applications, it is possible to bring the experience of a thick client based terminal to the browser (directly and indirectly) with very little compromises. Streaming A terminal requires constant feed for prices, order execution and alerts. This has been achieved using various techniques like long-polling, Flash sockets and HTTP streaming. With the introduction of WebSocket, a bi-directional communication channel, it is easier to build streaming channels with a lot more efficiency. Leading commercials and free products in this space provide support for WebSocket based channels. Cross-platform All modern browsers on desktops or mobile devices support most of the features, and hence HTML5-based applications give a true cross-platform experience to end-users. The same applications can be re-modeled just by using Javascript and CSS3. The new form factors of devices have encouraged developers to build applications––by using cross-platform HTML5 language, API and tools––and remain focused on developing features rather than infrastructural blocks. Despite the fragmentation, the growing community and third party libraries have helped address a lot of issues with ease. Analytics Analytics is an integral part of any application in the Capital Markets space. An application can have calculations as simple as percentage change to as complex as computation of pricing model for derivatives. Since most of the work is done on UI threads, performing such calculations made the application sluggish. By using Worker (Web Worker), thread calculations can be offloaded from UI threads, hence making the user experience better. Other features like Storage API helps in handling large volumes of data on the browser by offloading objects from memory to browser-based storage; it also helps in securing cross-domain communication. Canvas is a case in point––it helps in advanced graphics with improved performance on machines with GPU.

Insights into Online Advertising

In recent years, the online advertising space has grown at an astounding pace. More and more businesses are counting on the online medium, thanks to the reach and measurability. Whether it’s pay per click advertising, search engine optimization, email marketing or social media marketing, advertisers can easily reach out to their target audience more efficiently––no matter where they are located and what they do. Having started with a conventional model, online advertising has grown to a complex and innovative business model today. Be it the view, pricing, type and/or ad positioning, change has become the only constant. An ad that was once nothing more than a simple unoptimized image, has now become a dynamic and versatile communication asset that rotates, animates, speaks (audio) and plays (video). The what, where, why, when and how of ad display have become critical factors that determine the success of online advertising. As new functionalities/features/opportunities get unveiled every single day, today’s advertisers are game to increase their online spending. Let us first have a look at the nuts and bolts of online advertising, and then go through more complex concepts in this fast evolving domain. In this post, let us have a look at the role of advertisers, publishers and networks, and understand some of the critical terminologies commonly used in the domain. Key Players in Online Advertising There are multiple roles that come into play in the world of online advertising. Advertisers: The advertiser’s primary role is to provide the actual ads and campaign parameters. It is up to the advertisers to decide what ad they want to run, where they want to run, how long they want it to run, and how much they are want to spend. Apart from providing the actual ad, the advertisers may want to examine reports to see if advertising is meeting their desired goals. Publishers: Publishers run websites, and these websites have specific ad spots where advertisements can be placed. Generally, the publisher is also responsible for managing and running ads on their web site. An advertiser tells the publisher to run a campaign, but the publisher has to make sure that the campaign is set up properly and delivered as promised. Networks: Most advertisers do not have time to search for sites to run their campaigns, and many publishers do not have the time or resources to handle ad sales. An ad network will have extensive relationships with advertisers and publishers. An advertiser might go to that network and say they want to run a campaign across the whole network, or on specific category of sites. The advertiser gets the maximum number of audience, without having to deal with each individual site. The publisher gets the benefit of receiving a higher number of campaigns that they might not be able to attract on their own. Rates and Fees: Most widely asked questions are “What rate should I charge?” or “How much should I pay?” Unfortunately, there are no simple answers to these questions. The rate can be based on several factors like demographic and geographic targeting, total impressions served, etc. Few of them are listed below: CPM (Cost per Mile)- Is a flat fee for thousand-page impressions. CPC (Cost per Click) – Has become popular because of Google, where an advertiser only pays when a user clicks on the advertisement. Fixed Cost- Advertisers pay a fixed cost for delivery of ads online, usually over a specified time period, irrespective of the ad’s visibility or users’ response to it.   My next post will delve deeper into more advanced concepts in online advertising! Keep watching this space.

NoSQL – Break The Shackles

Recent growth of social media networks and smartphone users have led to a sudden spike in internet user population. In today’s world a good application can become viral in a matter of few hours. Gone are the days when user-base growth was a slow, linear and predictable process. Having a million+ users is the new ‘Norm’ for any internet application. Growth estimates have become unpredictable and now companies need to implement/create applications such that they can effectively support dynamic increase in the number of users as and when required. Today’s web applications also generate a lot of unstructured data in form of comments, feedback and access patterns. This data contains lot of useful insights about the users and usage behavior. Applications should be able to preserve and analyze this unstructured or semi structured data. Let the facts speak for themselves, 2+ billion  – Internet users 32 billion   – Hours spent online daily 1+ billion   – Smartphone users 566%          – Growth in number of internet users in last 12 years Scalability and Performance have become key success factors for any web app company. Normally, web applications follow a three tier design approach – Front-end (UI) layer, Model layer and Back-end (Database) layer. There are a variety of solutions and design strategies available for UI and model layer implementations, however database layer has been dominated by relational databases. Relational databases are rigid and difficult to scale. Application developers find it extremely challenging (impossible in most of the cases), to get the dynamic scalability without compromising performance. Relational databases have also failed to provide effective storage and retrieval of unstructured data. So, in nut-shell Relational databases are biggest bottleneck for rapidly growing web applications? Not anymore; NoSQL databases have emerged to their rescue. NoSQL is a completely new approach of thinking about a database. NoSQL databases generally do not adhere to the traditional RDBMS principals and fundamentals. A NoSQL DB may not support SQL and may not provide ACID (atomicity, consistency, isolation, durability) guarantees, but compared to relational implementations, a NoSQL DB is more flexible, scalable and cost efficient. Let me clear the ambiguity around the term ‘NoSQL’. It is a misnomer. NoSQL should be understood as ‘Not Only SQL’.NoSQL databases are – non-relational, schema-free, distributed, open-source, eventually consistent and horizontally scalable. NoSQL DB solutions available today can be classified into four major design categories, namely: key-value stores, document store, Columnar, and graph databases. Irrespective of the design and implementation, NoSQL databases share following characteristics: a) Auto-sharding: A NoSQL Database automatically distributes stored data across servers, without any need of explicit applications logic to do that. Servers can be added or removed from the cluster without any data loss or major application downtime. b) Implicit cache: To increase throughput, advanced NoSQL database technologies seamlessly cache data in memory. This is implicit and transparent to the application development team. c) Schema free: The storage structure is schema free and thus more flexible to efficiently store and retrieve unstructured data. NoSQL may not be a fit for all applications, specially the ones which involve online transactions. However, most of the today’s web applications can break performance shackles through NoSQL. NoSQL is supported by many technology companies and is gaining momentum. It is here to stay! Stay tuned to dive deeper into the NoSQL world in my further posts.

DISQUS: Elevating to the Next Level Commenting System

DISQUS has proven to be unique. I am able to record my observations about this distinctive comment management system in my blog. Firstly, I realized that what makes DISQUS a winner is that it removes hurdles for users across the web for managing multiple logins when all they want to do is just comment. The user can log in with Facebook, Twitter, OpenID, or a Yahoo! account and comment. This is the first win. Other significant advantages that make DISQUS stand out from other commenting platforms are: •Real-time comments – DISQUS launched a newer version of their system named “DISQUS 2012” which has improved features.  One major enhancement was that DISQUS now works in real-time which implies that you don’t have to refresh a page to check for new comments.  In fact, you can have a conversation within the DISQUS comment section as you would have on Facebook or MSN. •Email replies to commenters – If you leave a comment in DISQUS, you will get an email when anyone replies to your comment. This also helps in active commenting and replying. •Seamless integration with any website – DISQUS integrates seamlessly into any website or blog regardless of the platform of that website/blog. With a few quick and easy steps you can have your new commenting system up and running in no time. •Practically handles all spam – DISQUS uses its own anti-spam software to smartly combat comment spam. As it was designed to learn over time and becomes increasingly accurate with your moderation activity. •Shared profiles – As more and more websites are opting for DISQUS for their websites, profiles that commenters create are shared across blogs. This benefits bloggers in their communities. •User analytics – The DISQUS dashboard provides information on the users commenting on your site, including user reputation, history, post-approval rating, like from other users, and more. While there are certainly advantages to this system, there are also a handful of disadvantages in using DISQUS. The biggest problem which websites face is Search Engines Optimization (SEO). As search engines cannot crawl in a JavaScript comment widget and comments are fresh content to any website which search engines value highly. This poses a pretty big drawback. Another big disadvantage of using DISQUS or any other third party commenting system is that it lessens your control over it. Users can sometimes experience a slow page load or even no response if the DISQUS server is down. Though the blog details some of the pros and cons of using DISQUS, the system eds up being easy to use for both commenters and readers.

Ad Impression and Click Counting: Are You Billing Your Customer Correctly?

One of the key functionalities of an ad server is to determine how many ad impressions and clicks have been logged and confirmed. This is important because billing is dependent on that. A “confirmed” ad is one that we’ve verified as actually seen by a user. This means the ad was visible on the page and not blocked by an ad-block software or something else on the user’s end. Counting and confirming impressions In general, whenever ad server receives a request, it has all the user-related information from the cookie such as age, gender, etc. Depending on user profile, ad server returns ad to the user and logs it, which is counted as one impression. Key point to note here is that ad server has counted an impression, but it might not be confirmed because of several reasons. Therefore to confirm, ad server sends a blank GIF file (1×1) in the ad response with some unique ID, which is used by ad server to mark it as confirmed. Customer billing is done on the basis of confirmed impressions only. Similar is the case with clicks, in which ad server verifies the source is registered and valid and is not a robot. Delayed impressions There are some scenarios wherein ad server sends initial ad to the user but impression counting is not done till the ad server receives another request for the asset itself. An example is video ad, wherein you will see the video ad with “skip ad” options. If you skip ad, it will end displaying ad, else it will send request to ad server to continue the ad. Delayed impressions are used with: •    Prefetched ads •    Out-of-page ads •    Video ads •    Mobile ads •    Ad Exchange ads Counting clicks When an ad is displayed to the user and user clicks on it, a request is sent to the ad servers. Whenever ad server receives the request, click is counted and in parallel redirects the user to the landing page. Discarded impressions and clicks Sometimes there are impressions and clicks that are not generated by actual people browsing the web. Such impressions and clicks are neglected by ad servers. Invalid impressions and clicks can come from a variety of sources, including: •    Web crawlers and spiders •    Impressions and clicks from sources that are not registered and considered to be robots. Different ad servers can use different logic to discard impression and clicks. A Popular logic is to discard requests are coming from unknown sources such as robots. Robots IP are usually filtered in three ways. 1.    The first is based on known user-agents, which is straight forward. All entries in the log files where the browser is robot, are considered to be not confirmed. 2.    The second way of filtering robots is based on a known list of robot IPs and hostnames. The list is maintained in a configuration file and updated by the system administrators. 3.    The third way of robot filtering involves identifying robots based on behavior by analyzing a sample of the ad logs. Robots are identified by click activity. An IP/host which has clicked on more than THRESHOLD_TOTAL clicks that day, or has clicked on more than THRESHOLD_HOURLY clicks in any hour of that day, is considered to be a robot. THRESHOLD_TOTAL and THRESHOLD_HOURLY are configurable. This was an overview of ad impression and clicks counting. If anyone is interested to know more, feel free to send me an email. In another post, I will discuss technical details of impression and click confirmation and different scenarios related to ad impression and click counting. Stay tuned!

3 Step Program to Customer Targeting

Businesses, today, are turning to customer analytics to define and predict customer behavior. Most businesses represented by publishers and advertisers rely on demographic data to target consumers online. This is required to associate a product to a particular audience as defined by its demographics (age, gender, income) and interest data. Below is a three-step method  for advertisers and publishers to reach their target audience: Collect User Behavior  User behavioral data is usually collected through web browsers and video/audio players. Scripting languages such as JavaScript or Flash action script can be used to collect information related to browser, IP and content consumed by the user. This information can be categorized as strong or weak. For example, video player, tags or GPS-based location information can be categorized as strong and  IP address, browser type, or login patterns as weak. Audience Segmentation   The next step is the classification and identification of users by their interest or demographic characteristics. Users can be broadly classified by the following attributes. >Interest (media content) >Behavior (source, location, region) >Demographic (age, gender, income, company) This data can be used for machine learning model. Machine learning algorithm correlates user behavior to a specific interest. Users can then be targeted by using a combination of observed behavioral data. Interest and Behavior-Based Targeting Online behavior and the kind of media content consumed is required to predict user interest. The audience segmentation model identifies relationships between interest or content categories. The affinity rule increases the penetration of the ads campaign beyond the observed data. Interest-based advertisements, also sometimes known as personalized ads, are displayed based on information from online buying and browsing interacting patterns. Demographic-based Targeting The registration process can help to obtain demographic data such as age, gender, income or place of residence. The combination of behavior and demographics data is used as input to the Machine learning algorithm. By affinities rules regarding interest and demographics the machine learning algorithm increase number of users for ads targeting.   Deliver Ads in Real Time When all this information is collated, then the next step is to deliver advertisements in real time.  The trend in real time advertising is already visible and businesses can push dynamic content advertisements, across platforms and in a social environment. Thus, once the target audience has been defined and the ad content is formalized the power of the Net takes over.  By doing this, advertisers and publishers will move away from creating perfect messages to creating perfect brands.

Online Dispute Resolution (ODR) Process – Six Steps to Get Your Disputes Resolved Faster

Dispute Resolution is a process of settling a disagreement on a business transaction made between two or more parties. Every online website has certain protection policies for buyers, but there are instances where a protection policy is unable to stop a dispute from being raised. According to Emarketer, “China and United States are the world’s leading e-commerce markets combining 55% of the global internet retail in 2014.” As per the analysis, China will top the chart by exceeding $1 trillion in retail e-commerce sales by 2018 accounting for more than 40% of global e-commerce sales. With these huge number of transactions, it would be hard for anyone to keep disputes at bay. Without a robust dispute resolution mechanism in place, companies are finding it difficult to stay even competitive due to the inability of evaluating a dispute legitimately. With every on-going dispute settlement, companies will not only bleed out chunks of dollars for reaching a mutual settlement but also slowly lose the user tractions on their websites. According to the “Online Dispute Resolution for Business by Colin Rule,” for $7 trillion internet projections, the rule of thumb is that 1 to 3 percent of transactions end up in some dispute that will result in hundreds of billions of dollars tied up in disputes needing resolution. With the ODR process in place, a lot of disputes that were unmanageable earlier are now resolved promptly. Two renowned e-Retail giants have recently implemented an ODR system for efficient management of disputes. They even have strong policies of Buyer Protection, where if charges are proved against a merchant, then the whole amount will be refunded to customers with no questions asked. These companies are working continuously to make their ODR process more flexible than what it was in its earlier days to achieve a level of customer delight. There are some other companies like Modria, PeopleClaim, and 4PS, who only specialize in settling disputes between other companies and customers. When we talk about resolution, there are different ways of resolving a dispute that varies from company to company. There are certain basic methods that are pinpointed by most companies for settling disputes are mentioned below: Negotiation – Mediation – Conciliation – Arbitration After a dispute is raised, it is advised that the disputed parties come together for a discussion to reach a mutually agreed solution using ‘Negotiation’ process between them. In some cases, when negotiation is failed, ‘Mediation’ process is followed where a mediator tries to direct the discussion to reach a consensus but does not suggest any outcome. But there are times where disputed parties will not mediate, and then a third party known as conciliator tries to settle a dispute by providing multiple suggestions for reaching a common agreement. This process is known as ‘Conciliation.’ There are events where all processes mentioned above are unable to settle a dispute, then a single person or a group of people known as arbitrators hear the case presented by disputed parties along with all supporting pieces of evidence. This process is termed as ‘Arbitration.’ All parties involved in a dispute need to be bind by the decision put forth by arbitrators. Arbitration helps in resolving a dispute privately instead of going to court. Below are some of the steps of how an ODR process would work online. Do you have a problem with a transaction? Raise a dispute An online dispute can be raised either by a customer or a merchant for a particular transaction within a specified time span. Once a dispute is raised, parties involved in the dispute will be notified with the details of the dispute. Negotiate to reach a mutual consensus In the first instance, all disputed parties will communicate with each other and try to settle a dispute amicably by reaching a mutual agreement. The parties once satisfied will close the dispute. Still not satisfied? Escalate an existing dispute to a claim If any party is not satisfied with the terms negotiated for a dispute, may escalate the same to a claim within some specified time span. Solve a claim with expert advisors As soon as it gets escalated, a third party first understands the reason of the claim and then asks for some supporting documents. After all, documents are evaluated, the concerned person tries to direct the discussion towards a common agreement and may also provide some suggestions as part of the resolution process. Close a claim Once an agreement is reached, the claim is closed, and the third party freezes all documents to avoid any legal actions filed against them in the future. Want to arbitrate? Re-open a closed claim. In case a disputed party is not satisfied, he can re-open a claim within a particular time frame. Once re-opened, the process of arbitration may follow to reach a settlement.   Using these steps, a generic ODR system can be outlined. While having an ODR process in place, the companies can save their money from being drained out due to different disputes. This also gives a sense of protection to buyers which increases their loyalty for the websites. The Modria team already helped companies like eBay and PayPal to solve more than 400 million cases.   In eBay, there are approximately 60 million disputes among traders are resolved using their ODR process. Now – a – days, in the UK people are so much inspired by the ODR process of eBay that they are thinking of creating an ODR system to move the judicial system partially online. As per the report written by Prof. Richard Susskind, cases like financial claims worth less than £25,000 or various family disputes could be resolved over email and telephone conference calls. By 2017, the new and updated three-tier online dispute resolution system known as ‘Online Courts’ would be running live in the UK with an aim to resolve civil disputes on some pre-defined criteria using techniques like e-negotiation and e-mediation. In the coming days, Online Dispute Resolution (ODR) process will be a game-changer for companies to survive in this highly competitive world.

How to Get the Maximum Out of Your Ad Spends using Programmatic Ad Buying

Advertising is a sphere that changes as soon as a new technology arrives. From the early days of door-to-door advertising to personalized notifications on smartphones, consumers have only become more demanding. Each and every development has created new ways of meeting business needs and capitalizing on marketing and ad spends. The adoption of customer-centric technology has been large scale. Now, technology is trustworthy and increasing in popularity among marketers as they look to simplify their jobs to be able to do more. The advertising and digital arena is scaling up faster than ever. You have to keep pace with such changes in order to get the maximum out of your ad spend and emerge successfully. Programmatic ad buying The technology lets you interact with sellers directly and administer automatic and real-time bidding. Programmatic ad buying makes ad buying a transparent process where you are level with all competitors and can buy spaces that don’t cross your budget. Besides, the automated solution empowers marketers to buy the right ad spaces to reach the right audiences. Make the most out of data The more targeted your ad campaigns, the more you can capitalize on ad spending. And customer data is vital to guide marketers to strategize and create specific ads targeted to specific audiences. Big data has given the power to businesses to reach audiences that are ‘categorically’ relevant. To make most of the data, brands should be able to extract insightful readings from bare content. Integration is indispensable Consumers browse quickly and they enjoy the liberty to use multiple devices and varied platforms. Advertisers should accept the challenge and make ads visible and responsive for all screens. At the same time, try to optimize your ad spend by integrating the content seamlessly across channels. The scalability factor Buy ad spaces from advertisers who offer viewable metrics of ads and know if your strategies are working. Viewable impressions give you a fair idea about fraud clicks and the quality of online spaces. Viewability is the answer to measuring the effectiveness of ads more lucidly, and it helps advertisers choose better ad spaces each time a campaign is launched. These steps ensure that money spent on ads is used effectively. Programmatic advertising is a solution that takes care of all such tenets. The automated technology simplifies the tasks of marketers and advertisers, reaps the most out of ad spends in a hassle-free manner, and creates better ROIs.

The New World of Ad Ops

Advertising does well in environments that allow employees to show in-depth focus, creativity, and value addition, and leave the hard-nosed number crunching to a capable technology. Automation is the key in this new world, which is driven by years of data insights and machines in self-learning mode. The good new world The ad ops world has definitely become more hospitable after the internet. Ad-tech solutions have eliminated the need for advertisers to maintain supercilious media connections. The process of spend optimization has also become smooth-sailing and more professional. The labyrinth of advertising Organizations hiring ad agencies must realize that advertising is about proving to be valuable and useful. Ads should make customers feel they have someone to turn to. Interestingly, a simple line of text describing your offer can win you great ROI, but only if it reaches the right people. That said, your ad will work only when those right people are in need of your offer. Technology enables decision-makers to identify that too. A programmatic ad-tech solution will connect you with vast ad inventories, from which you can identify the ad spaces frequented by your prospects. Search histories reveal what their needs are, and you get to know all about it! Surety and anxiety Technology makes guess-work unnecessary and helps you know what kind of ads should be targeted at which people, and when. It leads to a more conclusive picture about the possible return on investment. That is the reason why doing ad ops the modern way eliminates the need for bickering arguments on whose market analysis is better. The needs you need to feel the need for Ad-tech software solutions can deliver value to your organization because it simplifies three things – budgeting, ad-buying, and testing. However, every industry has those processes differently structured. The objectives are also different. In a world where programmatic advertising itself is new, it is hard for decision-makers to imagine the necessity of customized programmatic advertising solutions. However, it is necessary. Without customized ad-tech, there is little meaning in automation. Experts in technology have realized that they can benefit organizations only by treating each organization differently. IT approaches change as per which legacy systems have been in use, the latest integration requirements, and market access capabilities. On the other hand, IT implementation needs to be flexible, and adopt algorithmic refinement patterns. It will help identify your target ad spaces through the quick-paced evolution of the digital world.

Four Reasons Why Programmatic Advertising is a Must

While programmatic advertising is stretching the realms of marketing today, there are many businesses yet to catch up with the technology. Here’s programmatic advertising in simple terms: “Programmatic” is synonymous with automated. That makes programmatic advertising a solution that automates advertising functions, from the initial task of ad buying to results tracking. The technology might be prevalent in digital but has equal benefits for traditional media as well. To know some interesting facts about programmatic advertising, read: “Go Mobile to Keep Up with the Fast-Changing World of Advertising” Technology that handles advertising – totally automated, scalable, and efficient eMarketer has found that 55% of all digital display ads will be bought programmatically in 2015 in the US. The activity will be worth nearly $15 billion! Automation alone can minimize efforts, eliminate errors, and mitigate risks in advertising. The efficiency aspect has witnessed remarkable improvement with the adoption of programmatic advertising. The technology is flexible to adapt to the needs of consumers. At the same time, it is equipped to scale at a high speed. Automated sales processes, target-identifying algorithms, campaign management, integration with third-party services, real-time analytics, and batch reporting have made programmatic advertising a highly efficient ad-ops technology. In a nutshell, programmatic advertising is a cost and time-effective solution. Real-time bidding (RTB) makes programmatic advertising & ad buying more competitive Adweek recently published that by 2016, programmatic direct will be worth $8.57 billion compared to $11.84 billion for real-time bidding. Programmatic ad buying has increased transparency in ad buying. Advertisers can view inventories and have the liberty to choose ad spaces as per their needs. Buyers take part in real-time auctions and get access to quality spaces irrespective of their sizes. Buyers can focus on targeted impressions while transparency in pricing is maintained throughout the process, unlike in traditional ad buying. Advertisers and sellers have direct interactions and RTB eliminates the scope of tightfisted negotiations. The solution provides greater power to advertisers in campaign performance, and at later stages, enables them to streamline their ad spending. Know your consumers and act – time to optimize big data the bigger way A recent Forbes report states that about 65% of marketers are using 40% of their ad spends on programmatic advertising with a high dependency on data and a quarter of the marketers are in fact allocating almost 80% of their ad spend on this . With the entry of programmatic advertising, the scope of targeted campaigns has broadened immensely. Advertisers can draw consumer-related and creative insights to create the best possible campaigns. Tracking the diverse attributes of consumers and real-time feeds helps increase clicks and conversions. Programmatic advertising enables automated and multiple tests to identify the best creative. It provides solutions to complex questions like, “Which creative is suitable for which segment?” Identifying the ideal prospect group and creating tailored ads become possible through automation technology. Programmatic advertising doesn’t just produce big data on a large scale, but puts it to optimum use . The solution has enhanced reach to audiences like never before. Handle any digital space and screen, anywhere, anytime! Programmatic Advertising enables you to manage digital ads across platforms. Reaching consumers at their preferred interfaces fuels higher returns. Be it customers browsing the web through PCs or laptops, using an app on smart-phones and tablets, or watching TV, you can track their buying journey and influence their decision-making extensively. At a time when cost, time, efficiency, and transparency are the looming concerns, programmatic advertising is paving inroads to better marketing. The integration of data and technology was a pleasing development for the digital world and with programmatic advertising; the next stage is already here!

Go Mobile to Keep Up with the Fast-Changing World of Advertising

People across the world started using mobile gadgets for online search only a few years ago. This trend caught on after smartphones became affordable and people found them useful for surfing. In the early stages, people used mobile phones to surf when not around their desktops. But today, they choose mobile over the desktop as it gives them access to high-speed internet and a range of functionalities on the go. They can find the information they need, and shop what they want. And they can do it anytime from anywhere. Already, there has been a steady decline in the number of searches made through desktops. The prediction that it will decrease by one-third between 2012 and 2018 is turning out to be true. Marketing budgets have increased in the mobile segment in almost every organization. Those investments are working, leading to a steady increase in mobile marketing budgets as a proportion of digital marketing spends. It is predicted that 75% of digital marketing budgets will be dedicated to mobile advertising by 2018. The need of the hour Dependence on data cannot be avoided. Investing in data is the need of the hour because it will help keep your mobile targeting strategies relevant. However, that is easier said than done. In order to capture the interest of your targets, advertising with the support of data is just the beginning. Click-worthy calls-to-action, quick data retrieval and audience connect, design optimization, and A/B testing are all required for you to be able to tap into digital opportunities successfully. It is also worthwhile to focus on native advertising strategies, as they fetch 50% more clicks on average. For better engagement, using videos may not be wise all the time, but they can be extremely effective if you are able to reach your audience while they travel or in their free time. Undeniably, mobile advertising is mostly about timing. Getting it right will solve many problems. And remember, nothing beats good app functionality. You can reach mobile users in two ways: Through your brand app, which the users have already downloaded Through platforms, which your targets audience to use in everyday life   the first case, you will have to make sure that your app delivers some value. There is no reason for someone to download it otherwise. Here’s what makes an app worth downloading for customers:  Seamless interactivity – for getting across the right messages and enabling convenient communication Easy functionality – for making the customers’ life easy and preserving their inclination to use your app Flexible technology – for changes or upgrades to your app, which should be easy and involve minimal or no downtime   The second way of reaching mobile users involves recurrent planning for the right programmatic advertising platform and the right publishers. For mobile ads, here are some “always remember” strategies: Just let your ad viewers call you directly if you are having trouble converting them. Although it sounds simpler than it really is, displaying your phone number works. If your targeting is strong enough and you’ve done all the hard work, maybe your audience thinks literally one click should be enough to reach you. When targeting audience segments, don’t forget about conversion. You can let the ad viewers call you on the phone directly, ask them to submit their contact info, follow you on social media, subscribe to emails and newsletters, or register for an event. Put the right conversion button for the right purpose for better conversion. Use Dayparting, which is a technique to program your ad reach differently for different parts of the day. Everyone likes to be notified about something relevant. For example, reaching parents in Miami during the afternoon with an ad for a children’s waterproof will definitely help them remember about it! You can also time your ads to match when they go for shopping. Consider shifting a part of your budget to desktop when mobile CPCs feel too expensive. This can be implemented by accessing the “Devices” tab under “Settings” in AdWords.   Programmatic advertising technology has proved to be the latest asset for experienced marketers, and the returns are proving to be worth the effort. Third-party advertising requires a team specializing in ad-ops. It should be able to strategize for profitable online advertising. That said, a sufficiently capable ad-tech software solution will help optimize your mobile ad expenses and assess your activities with advanced analytics on a real-time basis.

Five Ways to Raise Aftermarket Fleet Revenues with a Small Cloud Investment

Aftermarket revenues from fleet operators are not something manufacturers hope for. The usual feeling is that fleets handle damages, and it’s more cost-effective once the parts are availed. The challenge for techies was to provide the warranty business a technology that enables manufacturers to provide maintenance as conveniently as the in-house “service” fleet operators prefer. Enterprise software systems can help manufacturers achieve high proximity to fleet personnel at a low cost. Previously, systems were known to shrink profits, but, these days, the acceptance of cloud systems benefit manufacturers with great aftermarket revenues from fleet, especially if the right development strategies are used. So here we will give you five ways to judge a warranty management system, which can benefit fleet operators with unprecedented convenience; and you, with steady aftermarket revenues and customer satisfaction. #1 Cloud-based systems can collate data and provide insights for decisions on vehicle investments and cost optimization. Try to deploy a software service which integrates fleet services, manufacturers like you, and service units on the cloud. It will let you develop a database of performance, parts quality, and improve on them. #2 The cloud solution should also let you use GPS to deploy services at a win-win cost. An added benefit can sometimes be the RFID detection in cases of road emergencies. It proves when vehicle damages can be detected and signaled to manufacturers, who can send immediate help and medical assistance if the need is felt. #3 Since manufacturing companies have service stations across the globe, what remains is a technology that puts them and new parts at the fingertips of fleet operators. There is no need to outsource the repair otherwise. Manufacturers like you must deploy a user-friendly interface where fleet personnel can update you and get real-time feedback on complaints. #4 Rules-based warranty modules on the cloud will let operators access an interface and submit claims. On the same interface, the fleet operator can interact in real-time with the manufacturer on the issue. As a manufacturing company dealing with fleet personnel, you should be able to provide to-the-point explanations on claims processing. #5 Go for agile development software service. If a certain warranty claims management policy is proving to be unfavorable, be it for the OEM, supplier or vehicle user, changes in the rules will prove extremely satisfying. And that is where agility in the software will prove profitable. Fleet management operations, when supported with such cloud-based IT capability, can be implemented through smartphones. Mobile versions are becoming increasingly common these days. With software that is truly agile and flexible, fleet warranties can become cost efficient and simple to process. Understanding the needs of fleet operators is important, because a large potential of aftermarket revenues lies there. Vehicle utilization and equipment optimization are some decisions which have to be made extra carefully by fleet operators. Everyday logistics are also a challenge to achieve. Calling up manufacturers to do the repairs, and then claiming warranty, are low on priority. The whole process needs to be as if manufacturers are where the fleets are.  

Five Improvements Required for Warranty Revenue in Vehicle Manufacturing

Vehicle problems arise out of parts manufactured either by the automobile company or a parts supplier. The process of warranty inspection involves a validation process and communication protocols. The manufacturer should be able to validate, or turn down the warranty claim, and revert to the distributor with the right message and reasoning. And, at the same time, ensure customer satisfaction so that no complaints ensue. Transparency and turnarounds are the key factors behind customer satisfaction. Besides offering user-friendly interfaces for different players in the value chain, technology can save time and costs with real-time communication and a rules-based claims validation module. The latest systems also allow manufacturers to deal with supplier recoveries and shipment within short turnarounds in an inexpensive way. (Image credit: en.wikipedia.org) Simply improve these processes for better warranty revenue: Validation – When the customer routes a warranty claim (through a dealer and/or distributor) to the automobile manufacturer, it should determine the liability. In order for the automobile company to avoid a long turnaround, it needs an integrated cloud system to receive the claim details, identify the cause, notify the liable party, and revert to the customer, all within minutes. Service dispatch – Once the manufacturer is able to verify the claim and prepare a service team for repairs, the step involves shipment of parts and/or the vehicle. The service teams require mobile devices to access information, and do the required job. The service team should also be able update the manufacturer on the possibility of misuse, if any, and get further directions through the integrated mobile interface. Parts tracking – If required, the manufacturer should track the liable supplier for parts replacement, and inform the right supplier personnel on the issue. Technology should enable accurate identification with secure user IDs, as well as process and product IDs. Identifying the nature of the customer’s problem becomes easy with a technology which uses claims history to help identify fraudulent claims. Communication – The service team has to resolve the problem within quick turnarounds, and allow the KPI data to be appropriately filled. For this, the manufacturer should be able to measure customer satisfaction accurately. Issues like minor delays and dissatisfactory results should be taken into account through a real-time reporting system. Continuous improvement – Repair costs are difficult to control on the ground, and manufacturing improvements over the future are a must. Software technology can develop a claims history to let manufacturers identify areas of improvement in the manufacturing phase. This will enhance brand value and improve reliability over the long run. Vehicle manufacturers must not only check for technical issues related to the customer’s problem, but ensure that replacement and repair services are dispatched cost and time-effectively. That is when customers will stop thinking about going to the local mechanic to avoid the warranty rigmarole. Technology can make quick communication a completely transparent process. With globally distributed service stations of manufacturing companies, what’s required is a solution that uses GPS to identify cost-effective ways of dispatching services, and a transparent system that allows the customer to know everything from claim to billing. Technological support for such purposes requires integration over cloud: be it for the dealer, the special-parts supplier, the distributor, or the automobile warranty unit. As the need for improvement in such a vast value chain is bound to be felt, agile software development is a highly suitable practice for vehicle warranty management.

How to Ensure Customer Satisfaction with Technology in the Warranty Business

Software development efforts are usually directed at reducing overheads and increasing ROIs, but when technology is used to improve the value chain through better customer satisfaction, the approach required is essentially number driven. The need to improve customer satisfaction can be met if operational complexity was lower and measurability was easier for manufacturing personnel. Technology can make business performance measurable. The improvement brought about by such technology has to be persistently felt across the warranty chain. The need for measurability Experience improvement across the value chain occurs as the result of integration and transparency, which can be brought about by a user-friendly and integrated technology. However, without KPIs or measurability in it, personnel will remain in the dark about improvements. Error-free and time-effective processes involving dealers and special-parts suppliers are necessary as well. Manufacturers should detect inefficiencies and production defects, and consequently, make modifications that improve customer experiences. What are the software requirements? 1.OEMs first need an integrated system in order to obtain relevant data from the value chain. Access to data that lets managers use customer satisfaction as a KPI is only possible when a real-time system involving telematics, simple user interfaces, and the cloud can update the warranty system at the manufacturer’s end. 2.A capable technology must also use warranty data for internal calculations. Warranty intelligence helps identify patterns and leads to higher measurability, easing the process of operational control and performance scoring. When the data is real-time, pre-emptive corrections can lead to long-term profitability and brand value. 3.Warranty spends are contained within appropriate limits as the result of fraud detection. Data intelligence can help detect frauds by identifying customers’ behavior over time, and create transparency between OEMs, dealers, and suppliers on fraud issues. The need for agile software development Imitating the dynamism in any given business environment is understandably a difficult challenge in enterprise software development. IT suites for warranty management should feature user-friendly interfaces, which can be used by OEM personnel without technical expertise. While they should be able to set their warranty rules without the assistance of a software developer, algorithmic changes should be available from a software provider instantly. A capable provider should be able to deploy the changes within days or weeks by the virtue of Software as a Service (SaaS). Similar changes from legacy system providers can take months and overheads easily eat into profits. A vast scope of improvement lies in warranty intelligence. As your system gathers a massive source of cumulative data, algorithmic refinements can identify profitable distribution points, unproductive policies, scopes of fraud, supplier-recovery risks and let you score them regularly and accurately. Remember, refinement in algorithms helps organizations to improve continuously. In a nutshell Your warranty management system must drive your organization towards being better informed, so that you can avoid misunderstandings, take the right decisions, and deploy the right investments after quantifying performances. Moreover, a good software system is one that enables clarity and predictability in the value chain with ease.  

Four Steps to Have an Efficient Reverse Logistics Process

According to studies, an average of 4% to 6% of all retail purchases is returned, costing the industry about $40 billion per year. Every industry is facing a challenge today in managing goods return and use this as a key differentiator for their business. Without having a proper reverse logistics process in place, it is difficult for an enterprise to stay competitive in the Ecommerce industry as today is all about providing quality service along with quality products. A renowned Ecommerce company having an amazing reverse logistics process built on the lines of flexibility and responsiveness; will first provide a new product to the customer and then take the defective / damaged product within 3 to 4 working days – from the date of return request. Without a reverse logistics process in place, organizations are not able to manage returns and channelize goods. This situation makes it difficult to understand whether the returned goods need to: go to the warehouse for resale or to the manufacturing unit for re-manufacturing or be recycled. The customer satisfaction levels continuously decrease as they find it cumbersome to return the goods. Organizations are losing a lot of money to get these returned goods back to the warehouse. According to Harris Interactive, “85% of customers WILL NOT shop with you again; if the return process is not convenient, and 95% of customers WILL shop with you again; if the returns process is convenient.” With a good reverse logistics process in place, companies can not only track returns but also get the value from returned products. They can provide quality service to the customers. While outsourcing the processes to 3PLs, providers will be able to help organizations save a lot of money in the return process. According to the Aberdeen Group, “very few companies are more than marginally satisfied with their current reverse logistics approach, with nearly 60% reporting that they are somewhat or not satisfied.” To implement a successful Reverse Logistics process, we can follow the below points: Have a clear visible system in place for real-time monitoring and tracking. OEMs should have a proper process for the products that customers would want to return. The first point is when the customer calls customer care to request a return. The official should be able to differentiate the queries based on different scenarios like: damaged, defective, not available and want a different product. The people in charge of the transportation can then screen the products with respect to the different reasons and ship them back to the places determined for each scenario. Outsource your logistics to the 3PL vendors. Reverse logistics itself is a very complex process and to do it effectively one needs to have a separate department with skilled human resources. Reverse logistics requires a huge investment, and it is not possible to do it properly from Day1.OEMs tend to outsource this process instead of setting up a department. 3rd Party Logistics vendors like Blue-dart, FedEx, etc. have developed systems through which the OEMs can keep a real time track of their reverse shipments while the actual job is being performed by the outsiders. This not only helps the OEMs to reduce investments but also in attaining an effective return management process. Figure out your distribution centers and warehouses to manage reverse logistics. This is the most important part of the reverse logistics process. To have an effective RL process in place, OEMs needs to decide strategically on the return/ collection centers. Locations will have to be based on the cost of the products and the reason for return. For example, a damaged / defective product will go to the inspection plant where it would get inspected, and actions would be taken accordingly. On the other hand, if it is in a state of re-use then it would go to the distribution centers for resale. Communicate with customers during the return process. This helps in building a trustworthy relationship between customers and industries. Communication is one of the deciding factors for an OEMs sustainability in the long term. This era is of providing quality service. To get trustworthy, OEMs need to communicate continuously with customers. They need to listen and understand the queries placed by their customers and provide the best possible solution which will delight them. In case of returns, they need to interact with their customers and let them know the status of their returns. They should follow an interactive approach rather than a reactive approach. After understanding the efficient ways to implement a successful reverse logistic process, the following steps can be incorporated to get the maximum value of a returned product. Disassemble – Once the returned product reaches to the DC/ Warehouse, try to disassemble it to view the parts that malfunctioned and the parts that can be used. Sort – Segregate those into two groups like the malfunctioned parts and usable parts. Reuse – Reuse the usable parts to manufacture new products so that the daily wastage reduces. Repair – In case if there is a need for small repairs, the OEMs can fix them, and the same product can be re-sold. Recycle – Recycle the parts that malfunctioned and cannot be re-used by taking proper safety measures towards the society.

Hit the Bull’s Eye with RTB

Real-Time Bidding (RTB) is emerging as one of the most exciting developments in online advertising. It has helped improve transparency and targeting efficiency in the display advertising ecosystem and completely changed the media buying and pricing dynamics that have prevailed for over the last two decades. RTB is empowering a healthy growth of the overall advertising industry. In the year 2009 RTB was introduced to sell unsold impressions in real time to a large pool of advertisers and was considered a backup option for a long time. With the rise in the number of RTB exchanges and technological advancements, advertisers and publishers realized the monetization opportunities that RTB provides. RTB now fulfills more than 65% of online advertising demands. To evaluate and understand the value RTB adds to online advertising- let’s compare how the system works under traditional online and RTB advertising approaches. In a traditional approach, a publisher P enters into a direct deal with an advertiser A, to display ads on its portal. All the impressions shown, are charged the same price. The unsold impressions are either offered at a lower price band or remain unsold. This approach lacks efficiency and dynamic pricing. There is no role of user-profiles in pricing. The traditional approach assumes that all impressions carry equal value, which doesn’t hold true anymore. Each impression is unique! Let’s glance through a few facts that researchers have found during a study on internet access behavior. Though more pages are getting browsed between 8 AM and 11 AM, ad click rates are very low during this time slot Conversion and click rates are higher between 6 PM and 10 PM Conversion rates during sleep hours are significantly lower than what we have during the daytime Most of the websites have fewer visitors on the weekends Ad campaigns with well-defined target segments receive higher click rates than non-targeted ones   Above facts echo my view- “Every Impression Is Unique!” Impressions shown at a specific point in time to a specific persona are more valuable to an ad campaign than the impressions at any other time of the day. Similarly, an ad shown to a closely matched target profile holds more value and should be priced at a higher rate. RTB overcomes all the shortcomings of the traditional approach. In RTB approach, dynamic content is chosen real-time at a dynamic price through the following steps: A web page is getting browsed on the publisher’s site While loading the page, the site sends the entire information it has about the page, the user and the user demographics to third party exchanges called RTB exchange or Ad-exchange The exchange sends this information to their partner agencies (called Demand Side Platform – DSP) in the form of a bid request Each DSP evaluates and weighs various parameters associated with the impression in question and chooses an ad that best suits the given parameters Each DSP responds back with a bid price and an ad-markup URL for that unique impression on behalf of the advertiser Ad exchange (RTB exchange) compares all the bids received from different partners and notifies the publisher site with the ad-URL of the highest bidder (the winner) The publisher site displays the winning ad   This process happens within just 300 milliseconds and thus doesn’t hit the page performance or load time. Both publishers and advertisers equally benefit by aligning themselves with the RTB ecosystem. Publishers can better monetize each impression they show to the end user. At the same time, advertisers make informed decisions on how much they want to pay to deliver their message to the right person at the right time. Apart from Advertisers and publishers, there are many other players involved in the RTB ecosystem. Demand Side Platforms (DSP), Supply Side Platforms (SSP), ad exchanges are the few important ones. Market players need to re-strategize their media buying/selling approaches to succeed in online advertising that requires considerable investments in technology and training. In my view, investment shouldn’t be a roadblock given the value addition and benefits a sophisticated RTB implementation brings back to the table. It’s time to hit the bull’s eye with RTB.

Is ‘Declarative’ Always the Best Way to Go?

About a year ago, I was asked to create a POC for an address validation automation in SFDC. This job required an automated process to send mortgage address data from SFDC to a 3rd party web service, and parse a response back into SFDC. An aggressive 30 day deadline was included, and there was no appetite for on-premise hardware. The 3rd party spec followed MISMO standards (Mortgage Industry standards for electronic interchanges), which though extremely well put together, are not really standard. For example, the specs required parsing a complex DTD (Document Type Definition) for XML messaging [eXtended Markup Language – think envelopes for web data]. There was no WSDL service [Web Service Definition Language – a standard and useful web service nowadays], which would have eased the coding. This was not to be a run-off-the-mill project. The Salesforce API collection [Application Programming Interface -think USB, Video and Audio ports in your computer] provides customers with the integration capabilities to connect to nearly any data, and in many ways. However, SFDC customers are responsible for building such customizations on top of the provided APIs, in this case, using custom Javascript or Apex (coded) , or a cloud-based, third-party ETL app or tool [Extract, Transform, Load -declarative]. I compared cost estimates between coding in SFDC, the required messaging from the ground up, or using any of the advertised declarative ETL systems. As there is sparse SFDC documentation on how to code this particular requirement by following a DTD; I initially thought using one of the several available ETL tools would be cheaper and quicker. I must also add that the customer’s CIO had twice previously attempted to get this customization coded, and was not satisfied with the outcome. He was not too keen on a 3rd coding attempt, but had never heard of SFDC ETLs. As the popular saying goes; “The definition of insanity is doing the same thing over and over and expecting a different outcome” – Attributed to Benjamin Franklin, Albert Einstein, and several others. Though well versed in MSSQL and Oracle transformations, this was my first experience with SFDC ETL tools. ETL sounded like a very good proposition at first. Integrations were advertised to be as easy as dragging and dropping readily available flow components into a design environment, and 80% declarative. The cost was a bit scary, as most of these tools were billed as yearly subscription contracts, including just a couple integrations into the first billing tier. You can expect to pay about $1000 /month (and more, depending on the product) for your first tier contract. -Note: Up to 50% discounts are offered to Non-Profit organizations. The customers were informed and they stated the proposed benefits surpassed the costs estimate. So business case was validated, project chartered, and costs scoped. – or so I thought… While the selected product is excellent, and has received rave reviews from the SFDC community, the documentation for doing anything other than standard WSDL connections was non-existent. The procedures for the transformation of the ETL were described hastily without any details. It took more than 3 days to get a company representative to answer my request for information. Once I had the ETL provider’s attention, service was excellent. They admitted that proper documentation was in progress and assigned developers to explain the procedures of the transformations (letter of intent non-withstanding). It was not until then that I learned that the ETL provider does not process any response messaging. This was not documented anywhere. So the customer must provide a web server to run their agent software, which basically is a web listener, if you will. Considering our on-premise related hardware constraints, I selected cloud web server from a well-known provider to install the ETL response listener, and configured the minimum requirement resources the ETL provider listed. And that’s where it hit me. – Too many parts… – 4 different monthly bills… (SFDC, Data Provider, ETL Provider & Well Known Provider)! This was deviating from the initial scope. It was time to bite the proverbial bullet, knuckle down, and code directly into SFDC. Leveraging the SFDC DOM class (Document Object Model- an XML parsing tool) was not nearly as difficult as expected – it ended up costing about a quarter of the estimated yearly ETL. Also, the customer would only receive 2 monthly bills.  A successful POC was Apex built within a week. Needless to say, the customer’s CIO was only too happy to accept the proposed changes. I used to be absolutely biased in favor of declarative methods whenever such were possible. Though Apex and VF coding can certainly become troublesome to maintain, such methods can sometimes be much more convenient than declarative tools for the customer, and should never be dismissed without careful consideration.

Three Essentials For a Good Omni-Channel Retail Process

Only 5% of US retailers in a 2014 survey said they had executed on most of their omnichannel strategy. Lack of cross channel integration troubled end customers with issues like different retail pricing and cumbersome return process. According to a research firm – RIS, “$45 Million are lost in sales for every billion dollars in revenue, because of lack of cross-channel integration”. Today, customers and their demands have evolved seeking a seamless shopping experience, irrespective of their geographical location. To cater to this market and enrich the user experience, the big brands are shifting from segment oriented focus to individual-oriented focus. With product and price comparisons across all marketing channels, retailers are bound to stay consistent on all product information and prices. The order-management system needs to support these changes in business processes. It should identify the channel through which the order was processed and analyze the discounts/price changes to be applied. Competition is driving retailers to improve user experience. According to RIS, “6.5% is the amount of revenue lost because of the lack of omnichannel readiness.” In order to make the omnichannel commerce a success, we should have: An efficient Order Management System – using which the retailers can process their orders using dynamic fulfillment routing and inventory allocation system. The system will be well-versed to handle different order scenarios that may arise in addition to multiple return and cancellation options. It should also present a proper view of the inventory that is divided into multiple places, based on which the retailers can take decisions regarding its sales and fulfillment. Without the fulfillment system in place, an order cannot be called a completed order – so, the system should also facilitate the store fulfillment which will help the retailers to sell the products to customers where even the carriers refuse to cater. Customer Knowledge – today, the retailers should go the extra mile to know their customers, their preferences, purchasing behavior and product feedback. They should be open for a flexible process wherein they would incorporate essential feedbacks and optimize their end products/services. A Modern Platform – the retailers need a modern platform with cloud and mobile services that can provide analytics based on tracked customer preferences. The current day market also requires a proper presentation of centralized product data and other customer-facing technologies like self-checkout, kiosks, mobile payments, e-Coupons, etc. and easy integration of systems.

No More SFDC Training at the Bottom of My Bucket List

Are you having a really slow start on your SFDC management or development training? Up until summer 2014, if you wanted SFDC training, you could officially take a class, buy a 3rd party book, or use the online SFDC help and training articles. (BTW, there are other un-official free resources, such as sfdc99.com). Honestly, I don’t personally know anyone who enjoys using the standard ‘Help & Training’ to learn about SFDC. However, lucky high rollers on Unlimited or Performance editions get exclusive access to additional interactive Help & Training content. Having been aboard one such high rolling ‘whale’, I can testify that interactive training makes a ton of difference. If you’re on any such organization and didn’t know, you’ve been missing out big time! Classroom training suits me best. I’ve been a trainer. There’s just no substitute when you’re easily distracted – but then again, not all trainers make their classes fun. Reading books will do the trick as well, but it’s definitely not as fun as attending a good class. I haven’t heard of any must-have SFDC books though. Enter Trailhead. SFDC’s challenging and innovative approach to Web2.0 training. Yep, that means real social & interactive training. [I promise to add ‘fun’ as soon as they add -tangible- perks to it].  Trailhead is still in the beta stage, and hosts 7 training trails as of August 2015, up 4 since June 2015: Beginner admin – about 9 hours’ worth of content and 10,200 points earning potential CRM  admin, about 1.5 hours’ worth of content, and 2,200 points of earning potential Beginner developer, about 15 hours’ <minimum> and up to 19400 points worth Intermediate admin, about 6.75 hours’ long, with up to 8500 points to earn Intermediate developer, about 10.75 hours’ worth of content, and 8400 points to earn Mobile SDK Developer, near 7 hours’ long, and 3000 points of earning potential Dreamforce ‘15, only 30 mins and 300 points   -Late August Update- There are now 11 trails, after the addition of: Admin Lightning Experience – 3.5 hrs, 1100 points Developer Lightning Experience – 10.5 hrs, 5800 points Admin Trail, starting with the Lightning Experience –  3 hrs, 1000 points Sales Rep Lightning Experience,  2 hrs, 600 points   I’m figuring the ‘Lightning Experience’ will be well received by SFDC at Dreamforce ‘15 You may select to just chopper in and drop into the middle of any trail or challenge. You are not forced to follow the recommended sequence. You are required to use a developer org and an admin login to complete some of the goals. Each trail consists of a number of topics, each within an independent page. Within each topic, you are challenged to complete a short test or a developer org configuration task. Your success will be rewarded with X points. If you fail; “no soup for you!” (Google up ‘Soup Nazi’ for that reference). Retries will earn you half the points of the previous attempt’s reward level. Interestingly, the trailhead is capable of checking whether the configuration (metadata) you were challenged to complete exists within your developer org.  In addition to points, you are rewarded with ‘virtual badges’ for completing challenges and reaching point thresholds. There are links for related articles within each topic and links to SFDC forum discussions. If any such article or discussion picks your interest, you may take the long way for your trail. So what do you do with those badges and points? Back in January, you’d get a free t-shirt for completing the existing trails. These days, points and badges will only get you bragging rights (what?  I can’t write that?) – a-hem! I mean ‘community recognition’. So what are you waiting for?  Do you want any additional motivation to start your free trailhead journey? Register for the SFDC IDEAS Community, and vote up “SWAG for Trailhead Points”! https://success.salesforce.com/ideaView?id=08730000000wkRxAAI

Why Do We Need SFDC Security Assessment?

Cloud application security specialists have warned salesforce.com (SFDC) users of the vulnerabilities in the application. Cybercriminals exploit such vulnerabilities to harvest user credentials. Phishing attacks trick users into clicking a link which would appear to be leading to salesforce.com. It is difficult even for the spam filters and anti-phishing solutions to identify such links, let alone the user. In 2007, an incident detected by an Atlanta based financial institution, revealed how a simple click on an email link compromised their entire SFDC organization. The action released a Trojan, resulting in the retrieval of SFDC passwords. Passwords retrieved by the cybercriminals were used to access information from thousands of undisclosed ADP and SFDC customers. In 2014, Salesforce.com alerted its customers about the DYRE malware*, which usually targets customers of large financial institutions. Cybercriminals came up with a version that threatens salesforce.com users. DYRE phish attacks usually consist of emails containing what looks like a genuine message from SFDC, with links or attachments as shown below. Such emails often employ scare tactics to get the users to download linked malware and/or execute the attachments. Dyre sample emails Source: spamstopshere & trendmicro Links usually point to a Trojan attachment or URL, which if executed, will initiate a ‘Man-in-the-Middle’ attack, quietly gathering credentials and user data. Some versions disable Windows firewall registry entries. Salesforce reacted by increasing mandatory security levels to all customer organizations, such as removing the capability of trusting any IP’s in your organization with a single IP Range entry. IT admins seeking to trust any IP to their SFDC org, are forced to take responsibility for lowering SFDC org security by creating a minimum of 254 range entries. The admins are advised to trust incoming connections only from VPNs and corporate static IPs. This measure is not enough to protect you.  DYRE might enable a surreptitious download and installation of additional malware, such as VNC/remote management into the infected system, circumventing IP trust settings. Analysis of the newest versions of DYRE and similar cyber-attacks (UPATRE, ZBOT, CRILOCK, and ROVNIX) reveals designs to defeat email blacklists and the best filtering products. Even the best known products may detect and block just 65% of these phishing attempts. SFDC recommends the following initial steps to minimize phishing risk: •Train your users to spot phish or spoofed emails •Force shorter password expiration periods •Enable SMS-text identity confirmation to allow SFDC logins from unknown locations •Enable mobile 2-step identity verification •Enable SAML authentication and require all authentication attempts to be sourced from your network, or your VPN •Perform 3rd party security assessments and audits by trustworthy companies who are experienced in the field. (Preferably SFDC partners for your SFDC concerns) It is crucial to detect such attacks and protect users, as the stolen credentials can be used to extract sensitive data which can go undetected for a long period of time. * In June 2015, Trend Micro alerted of a 125% increase in DYRE-type attacks from Q4 2014 to Q1 2015.

What You See is What You Get

In the blog “Ad Impression and Click Counting: Are You Billing Your Customer Correctly?”  I explained how ad impressions are counted and confirmed. The blog summarized scenarios, where customer billing may be inaccurate even when there are confirmed impressions. The steps behind this erroneous process are as follows: Ads are confirmed through a “client-side pixel” at specific position in an AD response. This is usually a transparent 1×1 pixel GIF. The URL for this image contains a unique identifier to record information that all the ads as per the request have been confirmed. For example, when a page is loaded, and an image is requested, all positions that were filled on the page are marked as confirmed. Thus, with this approach, billing will occur, irrespective of whether ads have been viewed by a user or not. What you see is what you get There is also a better approach which confirms only those ads which are seen by the user. If an ad is served below the scroll bar and the user leaves the page without scrolling, then the ad will not be confirmed. This approach has confirmation-URLs with every ad served and hence ensures that only those ads which are seen by the user are confirmed. The technical steps given below show how the confirmation-URL is different for each ad served. “Ads”: { “HPMiddle”: { “CampaignId”: 2827, “CreativeId”: 1, “Confirmation-url”: “http://www.adserver.com/d824f82Q2FQ5CQ5CQ5CQ5CQ5CQ5C5Q5C5hTq5qQ7ETQ3FQ5C…“, “Creative”: “content which will be served” “Classification”: “BigAd” “TopAd”: {“campaignId”: 2387, “CreativeId”: 0, “Confirmation-url”: “http://www.adserver.com/d824a21Q2F——R-RrPSRSQ24Po——–RQ24–RS)Po—-PRN-oQ24)”, “creative”: “content which will be served” “classification”: “Leaderboard” } }, Backend Logic for Conformation: When an ad request is made ,ad log file is created/updated with user information like Time;IP;County; Zip code ;Ad name , position, page along with unique 16 digit number mentioned in red below. Sample ad log entry 1409655663^CUNK^C170.149.164.65^Chttps://www.tavant.com^Cnyt2014_textlink_digisub_account_37Y93,,MA3^Cwin7^Cfirefox3^Cna^ CNY^C10018^CUS^CX^C0-9^C0^CUNK^C007f01012dd95405a35a0008^C00^C00^C0000005056ab6ce1^C0^C To summarize, the technical process is as follows: With each ad request, confirmation-URL is called which writes another log file. At the end of an hour, ad log and confirmed log file are processed, and a 16 digit unique number is generated to count the number of impressions of a particular ad. Hourly data is then accumulated to tally the number of impressions per day. This data is inserted into a database through nightly job scripts, to be used for reporting by other applications.

Handling image uploads with AngularJS

When developing web applications, one of the common use cases  would be to manage image uploads along with validations for supported formats and sizes. Here, we outline a way to achieve this in AngularJS using a file reader service. File reader module This module is intended for common usage across all screens where there is requirement to read the file. You can include the following code in a file upload.js (function (module) {   var fileReader = function ($q, $log) {   var onLoad = function(reader, deferred, scope) { return function () { scope.$apply(function () { deferred.resolve(reader.result); }); }; };   var onError = function (reader, deferred, scope) { return function () { scope.$apply(function () { deferred.reject(reader.result); }); }; };   var onProgress = function(reader, scope) { return function (event) { scope.$broadcast(“fileProgress”, { total: event.total, loaded: event.loaded }); }; };   var getReader = function(deferred, scope) { var reader = new FileReader(); reader.onload = onLoad(reader, deferred, scope); reader.onerror = onError(reader, deferred, scope); reader.onprogress = onProgress(reader, scope); return reader; };   var readAsDataURL = function (file, scope) { var deferred = $q.defer();   var reader = getReader(deferred, scope); reader.readAsDataURL(file);   return deferred.promise; }; return { readAsDataUrl: readAsDataURL }; }; module.factory(“fileReader”, [“$q”, “$log”, fileReader]);   }(angular.module(“App”))); Update the value ‘App’ with the name of your app in the last line of the above code. Image reading and Validation Define a directive called ‘ngFileSelect’ to validate the image for supported formats, size and dimension. App.directive(“ngFileSelect”,function(){ return { link: function($scope,el){ el.on(‘click’,function(){ this.value = ”; }); el.bind(“change”, function(e){ $scope.file = (e.srcElement || e.target).files[0];   var allowed = [“jpeg”, “png”, “gif”, “jpg”]; var found = false; var img; img = new Image(); allowed.forEach(function(extension) { if ($scope.file.type.match(‘image/’+extension)) { found = true; } }); if(!found){ alert(‘file type should be .jpeg, .png, .jpg, .gif’); return; } img.onload = function() { var dimension = $scope.selectedImageOption.split(” “); if(dimension[0] == this.width && dimension[2] == this.height){ allowed.forEach(function(extension) { if ($scope.file.type.match(‘image/’+extension)) { found = true; } }); if(found){ if($scope.file.size <= 1048576){ $scope.getFile(); }else{ alert(‘file size should not be grater then 1 mb.’); } }else{ alert(‘file type should be .jpeg, .png, .jpg, .gif’); } }else{ alert(‘selected image dimension is not equal to size drop down.’); } }; img.src = _URL.createObjectURL($scope.file);   }); } }; }); If the image is valid, the directive calls ‘getFile’ function to get the base64 url of the image for preview, as defined below.   $scope.getFile = function () { var dimension = $scope.selectedImageOption.split(” “); fileReader.readAsDataUrl($scope.file, $scope) .then(function(result) { $scope.imagePreview = true; $scope.upladButtonDivErrorFlag = false; $(‘#uploadButtonDiv’).css(‘border-color’,’#999′); $scope.imageSrc = result; var data = { “height”: dimension[2], “weight”: dimension[0], “imageBean”: { “imgData”: result, “imgName”: $scope.file.name } } $scope.imagePreviewDataObject = data; }); }   Finally, you can bind the directive to your input button, in html, as follows: <span class=”btn btn-default btn-file” ng-class=’class1′> Upload Image <input type=”file” ng-file-select=”onFileSelect($files)” accept=”.jpg,.png,.gif,.jpeg”> </span> PS: File reader module works correctly in all modern browsers. For IE, it was found to support version 11 onwards.  

Remanufacturing – Reasons that Make a Rebirth for Old Parts Valuable to Businesses

“Remanufacturing is a standardized industrial process by which a previously sold, worn or non-functional product is returned to the equivalent, or better, condition and function of the new original product. The remanufacturing process incorporates technical specifications and yields a fully warranted product.”– ISO. The remanufactured automotive parts industry is estimated to be an approximate $85-100 billion industry worldwide, as per the reports from the Office of Transportation and Machinery, U.S. Department of Commerce (2011). Let us analyze the reasons that make remanufacturing valuable to businesses. Low-priced: Automotive Parts Rebuilders Association (APRA) suggests that about 88% of the original parts are reused in remanufactured machines. Remanufactured products are priced 20-40% lower than equivalent new products and come with an equivalent version of warranty terms. New product warranties cost 1-4% of its sales revenues, and if you do the math, an equivalent remanufactured warranty cost is lesser for the OEMs. The remanufacturing process usually begins with the OEM’s exchange policy to push new product sales and product returns from warranty programs. Lack of new parts, retention of old technology or up-gradation of technology into old parts and environmental consciousness are other incidents that trigger remanufacturing. As Good As New: A remanufactured part goes through the same level of processing and testing as a new product and often turns out to be better than the original. Remanufacturing a part created ten years ago will always give us the advantages of the technology & engineering improvements, giving us apart with improved specifications. While new parts like transmission come with a year’s warranty, a remanufactured equivalent comes with a 3-year warranty. The remanufacturing process comprises of rigorous testing and sorting where broken/worn parts that do not match industry standards is discarded. The selected parts are cleansed/inspected and fabricated where new components are installed and reassembled. The final product undergoes an even more strenuous testing process including visual inspection and mechanical tests such as decay testing, gauging and crack detection. Collecting, inspecting, disassembling and replacement/reprocessing worn-out parts are some basic steps to be followed as per ISO standards (ISO/TS/P 239 is the quality standard followed for remanufacturing). This process is backed with robust documentation and appropriate warranty issuance. Such an established process ensures a certain level of quality. Compensates for new part demand: OEMs tend to stock spare parts that are fast-moving. These fast-moving parts are manufactured after a lot of planning –planning involves identifying market demand, accounting for buffer stock and plan for setups at the shop floor to manufacture parts in huge quantities. Slow-moving parts fit into fewer machines and are not produced in small quantities as the costs associated are higher. This demand gap is met by remanufactured parts. Meeting the demand on time translates into reduced machine time and improved customer satisfaction for the OEMs. Sustainable Manufacturing Methods: In remanufacturing, the parts are closely inspected for reuse and undergo multiple steps of cleaning, segregation and reprocessing before it is fitted into the main machine. This way, the chances of parts directly reaching landfills are reduced. Since remanufacturing encourages reuse and reprocess of existing parts, this saves the energy and water consumption required to produce new parts. It also reduces the impact of emissions and effluents as a by-product of manufacturing. Remanufacturing extends the life of existing parts and ensures that sustainable manufacturing methods are followed. Benefitting from an economical, qualitative and sustainability perspective, Remanufacturing is making new inroads in the industry and is here to stay.

The Internet of Things and the Way it Impacts Life

Internet of Things (IoT) is a network of objects (electronic devices) with the ability to interact with each other without any human intervention, deployed usually in islands of disparate systems. What does this mean? We have a lot of smart home devices like lightbulbs, thermostats, TVs and motion detectors. The communication between these devices without manual help is what we term as ‘Internet of Things’ or IoT. For example, if the motion detector detects an activity, while there are no signs of the family members’ presence (watches, mobiles, etc.), then it triggers an alarm. Another example of automation/no human interaction event would be when the leak detector finds an over-threshold instance and communicates the same. Few Benefits: Remotely monitor and manage your devices (smart devices) Create rules that will automate your home, industry, etc. which will in turn help in saving energy bills and reducing manual labor Collect raw data for analytics and the raw data collected via various sensors help:   a.Predict usual/unusual events b.Find any rules in the events c.Pattern recognition But where and how can the IoT profit users today? Industry: Predict equipment malfunctions and schedule service maintenance Monitor thresholds and send notifications (with the help of sensors installed in equipment) Process payments – based on user location, activity and duration for- public transport, gyms, theme parks, etc.   Home: Control lights based on motion detection sensors and save energy Predict unusual events like door unlocking when you are away Avoid disasters using sensors that can track leakage/disruptions and notify on mobile   Health: Monitor individual movements, location and workouts through the day using smartphone sensors like Proximity, Gyro, Accelerometer, GPS, Smart Cities: Monitoring parking spaces Monitoring pedestrian levels and vehicles to optimize driving and walking routes Weather adaptive and intelligent street lights   Environment: Detecting air pollution and forest fire Early detection of earthquake & distributed control in particular places of tremors Wildlife tracking collar system to protect wildlife   The IoT has become the buzzword in sectors like government, education, finance, agriculture, logistics and transportation. And it has made incredible strides in the consumer industry. While the concept is still forming shape, it has already transformed the way people, technology, and devices work.

Shark wins over Hive

Not long ago, ApacheTM Hadoop R (a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models) emerged as a solution to big data challenges. However, there were some inherent issues, related to performance and time lags as Hadoop is designed for batch processing and not for real-time queries.  Another challenge with Hadoop is the requirement of `Map-Reduce’ perspective which was a deterrent for SQL engineers. To manage this issue,  a data warehouse system called Hive was introduced. Hive wrapped the Map-Reduce nitty-gritty into an SQL-like interface with its Hive Query language.  However, this did not resolve the inherent issues with Hadoop’s Map-Reduce approach i.e. latency. As a result of these challenges, open-source tools such as Spark, Impala and HAWQ emerged, and these tools leveraged techniques to reduce the latency associated with batch-based Hadoop jobs. Shark is one such Hadoop extension tool that speeds up both in-memory and on-disk queries.  Impala, another such tool, works well with Hive/HDFS and resembles traditional parallel databases. With our passion for technology, we at Tavant, have tested these emerging solutions to evaluate their performance in real-world cases. Given below is our analysis of Shark: We simulated a total of six ad servers with a structured set of logs capturing the details of ad requests and deliveries. We generated 4 million requests in one hour per ad server, taking the size of logs on one server to 125 MB in one hour. We then set up two clusters – one with Hadoop/Hive and one with Spark/Shark. The same set of machine configurations was used for running both the clusters: OS: Ubuntu 12.04 LTS, Ram: 2GB, Number of nodes: 2 We executed a query to find out the number of requests, impressions and clicks based on the geographical location of the user. The following infographic illustrates the execution time recorded for both the cases: Thus, it can be inferred that Shark is superior to Hive in terms of performance. However, we witnessed a few issues with Shark: The memory size available to the Shark process must be chosen wisely, depending on the data size to be processed, in order to avoid ‘Out of Memory’ error. The improvement in the performance of Shark over Hive is not consistently greater by a constant factor. Heavy workloads and different queries may show less gap in the execution times of Shark and Hive. Nonetheless, Shark seems a good option at this point.  Future releases of Shark will make available to us more features and upgrades. Don’t miss our next blog: `Evaluation of Impala’.

Analytics and Mobility are Tugging Brands into Digital Advertising

The exponential growth in the consumer base for smartphones and tablets is standing proof of the rapid migration of consumers from the world of broadcast, telecast and print media to the digital world. It demonstrates the increasing convergence of the virtual and physical worlds.  Juniper’s Digital Retail Marketing Report “Loyalty, Promotions, Coupons & Advertising 2015-2019” has cited the reason for this migration to be the result of timely, targeted, personalized campaigns that enhance customer engagement. Cashing in on this trend, marketers are utilizing the advancement in analytics technology to create ROI enriched marketing campaigns.  A forecast by eMarketer predicts that annual mobile advertising spend is expected to grow by more than two-fold by 2018 amounting to nearly $158.55 billion.  This indicates fatter marketing budgets which is likely to grow bigger with time, as according to estimates, the global market share of total digital spends is expected to reach 30% in 2015 (Source: Magna Global). The following points highlight the impact and opportunity available for marketers today, owing to mobility and analytics: Maximized Personalization:  Provides marketers with the ability to reach users with mobile internet, at the right place and at the right time. By using analytics, marketers can leverage consumer behavior, i.e.  Leverage past activity (declared behavior) and interests (undeclared behavior for  inferred conclusions by using predictive analytics. Emotional Connect: Content sharing has become easy and ubiquitous with social platforms blurring offline and online interactions. This essentially means brands can facilitate an emotional connection with the end consumer by curating content as per the needs of their target audience. Apart from this, it provides marketers with insights into customer reactions to their products or services providing them with valuable insight into customer preferences. Precise targeting: Abundant user data is available in real-time.  This provides marketers with the opportunity to identify potential buyers and offer them impactful tailored content across different communication channels.   For marketers to be successful, it will be important to plan seamless digital initiatives with campaigns that capitalize  the entire lifecycle across screens and platforms rather than follow a silo model or a fragmented approach.

Right-Time Analytics in Mortgage Lending

The residential lending market has fallen from its peak and has settled at a more realistic area where it is most likely to stabilize in the $1 to $2 trillion mark for the rest of the decade.  In the wake of the 2007 crisis, student loans have increased. The Millennials have piled up substantial debts and prefer to rent than buy. House prices have increased in major cities making it even more difficult to buy. Meanwhile, the cost to originate a loan is on the rise. And these are only some of the countless micro and macroeconomic trends currently impacting the mortgage lending sector. Alongside, technology innovations for the mortgage sector have made path-breaking strides to the extent where legacy applications are being replaced by faster, customer-friendly applications.  While many companies are focusing on replacing or embracing their existing legacy systems, some of them are also diving deeper to get the best out of technology. These companies are using analytics to provide strategy, growth and revenue-related statistics. However, more often than not, many of these companies invest in analytics mostly to deliver reports to understand past behaviour and use that knowledge to improve processes and bridge existing gaps. The reports are usually sent to decision-makers on a periodic basis or delivered on demand. In most cases, the data is maybe a day old and is refreshed on a nightly basis. These reports serve the limited needs of the company to support its current operations. But to improve efficiency and reduce operational costs, it is essential to provide data at the time when it’s needed the most, not before and not after, this is called ‘Right-Time Business Analytics’. What is Right-Time Analytics? Information on important events which impact the business, have to reach decision makers as fast as possible. For example, if the employee attendance system detected an unusual sign-off for a loan officer who had to submit disclosures to customers on a particular day and if those disclosures have not been reassigned, then alerts need to be fired immediately to the second-in-command or the reporting manager, citing the number of violations that are about to happen. Such alerts, unfortunately, cannot be fired using traditional business intelligence methods where data is loaded into a data warehouse on a nightly basis. For each event like this, the gains may not be as visible to the human eye as it would be when the total number of events is calculated. Though organizations spend a lot of time measuring the average cost per loan, pipeline velocity and cycle time, very few lenders measure and assess the number of hours spent on closing a loan.  This is where Right-Time Analysis comes in! Lenders who adapt to right-time business analytics will see natural improvements to operations beyond what is planned strategically. Another important real-time metric that would create a sense of urgency amongst the workforce is a bullet chart that clearly shows the real-time performance of the loans they are working on as compared to the set target and the company average. These are great tools that a company should consider implementing to improve turnaround times in addition to the other regular operational improvements. Social media is another area which benefits from analytics. Analysing user behaviour on websites is critical to detect user grievances and react to the same towards controlling the damage before it becomes viral. Implementing right time analytics along with effective activity monitoring helps identify several areas where operational improvements can be implemented, thus reducing the cost of originating a loan. In a stagnating market, mortgage lenders who recognize the value of Right-Time Analysis will stand to benefit in the short and long term. FAQs – Tavant Solutions How does Tavant implement right-time analytics in mortgage lending?Tavant provides real-time analytics delivering actionable insights at critical mortgage decision points, analyzing market conditions, borrower behavior, and risk factors to optimize pricing and approvals. What specific analytics capabilities does Tavant offer for mortgage lenders?They offer predictive analytics, real-time risk assessment, automated property valuation models, customer behavior analysis, and portfolio performance monitoring to improve decision-making. What are right-time analytics in mortgage lending?Delivery of relevant data and predictive insights exactly when lending decisions need to be made, including real-time market, borrower risk, and property valuation information. How do analytics improve mortgage approval rates?By providing comprehensive borrower profiles, alternative credit scoring, and risk assessment tools to identify qualified borrowers and optimize loan terms. What data sources are used in mortgage lending analytics?Credit bureau data, bank statements, employment records, property databases, market trends, social media insights, utility payment histories, and rental records.

Brace yourself for #MarTech

The AdTech industry has witnessed significant growth in recent years, but there is another industry growing rapidly and which could likely overtake AdTech in the future.  MarTech, refers to the innovation in marketing technology outside the context of advertising,  focused on experience and data-driven marketing. AdTech, on the other hand, refers to advertising technology focused on advertising and serving technologies such as ad-server and real-time bidding solutions. In the recent past, more and more organizations are adopting MarTech to innovate marketing pipelines, infrastructure, and workflows, to achieve higher operational efficiency, greater insights and better planning for marketing budgets. The image below, published by Scott Brinker, Editor, ChiefMartech, gives an idea about the scale and pace at which the world of Martech, is growing. Scott mentions that the number of companies adopting MarTech has doubled over the last one year with the overall number crossing 2000.  Expansion of the MarTech landscape indicates the evolution of marketing, the increasing role of technology in marketing and the heterogeneous nature of MarTech field. Scott draws attention to key areas where innovation and success are clearly visible: Internet – Scott attributes the success of the digital world to the penetration and affordability of the  Internet in people’s daily lives. Infrastructure – The transformation of data storage and presentation capabilities has enabled gathering and processing of data for greater understanding of consumer behavior. Scott says that most  of this can be attributed to big data, cloud computing, and mobile/web app development Marketing Backbone Platforms – Platforms such as CRM, e-commerce engines, etc. have brought businesses closer to end customers even as they address pain points. Marketing Middleware – Data Management Platforms (DMPs), CDPs, and tag management software have enriched data with metrics  that were not available earlier. These metrics have helped to improve operational efficiency and overall productivity for companies. Marketing experiences – Scott calls them the `front-office’ of marketing. These technologies revolve around customer lifecycle such as social media, email, and A/B testing Marketing Operations – Mostly associated with Business Intelligence, Analytics, Visualization and Data Science, marketing operations helps to interpret and find solutions   To summarize, even though a lot of action is happening around MarTech, the fact remains that there is huge potential for growth. We will likely witness a  shift of focus away from AdTech towards MarTech.

Gaining Intelligence from Gaming – Game Analytics Platform

A successful decades-old, global, video game developer, publisher and hardware company generates several gigabytes of telemetry data on a daily basis.  This company has several successful game franchises that attract millions of players on iOS and Android devices every day, and all these clicks and interactions have resulted in incredibly valuable data. If this data can be aggregated and analyzed, it will hold the key to player engagement and retention.  An example of the kind of data includes the event streams that indicate when players are playing, duration, levels reached and the money they spend on buying virtual goods such as new levels and avatars.  Social networks complement this information with details about players’ real-life preferences. To analyze this data and use it for intelligence an Analytics platform had to be created. Given below are the steps used for building this solution: Solution Architecture The solution was built on Amazon’s cloud platform using Amazon Web Services (AWS). Software Development Kits (SDKs) for the different game technology platforms, including Android and iOS, were used to enable the games to push events data to the data collection server with minimal programming. Data Collection For the purpose of data collection, server using Node.js, which collects high velocity data from players’ mobile devices and writes it in real time to folders on an S3 (Amazon Simple Storage Service) bucket was used.  This provides an event-driven architecture and a non-blocking I/O API that optimizes the throughput and scalability of data. An Amazon EMR cluster to process the collected event streams from S3 multiple times every day was adopted.   For each batch, a cluster on demand, based on the data volume, wrote the results back to S3, and then shut down the cluster to save on costs. MapReduce jobs validated and cleaned the event data and wrote the results back to S3.  Hive jobs then further processed these files to generate facts, dimensions and aggregated facts for later analysis. Data Persistence and Visualization To support rapid query and analysis, the Hive output was loaded into a data mart built on MySQL (and later experimented with Amazon RedShift as well) and Tableau to create dashboards and interactive charts were used. As a result of this solution, the game company gained valuable insights, including: The conversion rates of players from free to paying customers based on geography, game title, and other dimensions The skew in the distribution of paying customers (a small number of players accounted for a large part of the total spending) An understanding of each player’s playtime across multiple games (surfacing opportunities for cross-promotion within each game) Detection of fraud through comparisons of the game’s telemetry data about purchases with the app store data for in-app purchases (it turned out that hackers had exploited a vulnerability in the game design that was quickly corrected)

Parallel Job Execution in Pentaho with Dynamic Configuration

We had to design and build a data warehouse for multi-tenant architecture.  However, there were multiple clients with different data metrics and source databases while the data model (dimensions and facts) was common. Thus, to start with, we developed the ETL (Extract, Transform & Load) jobs for a single client which had to be scaled for this new requirement. To load the data using the same ETL job, one can change the source and target database configurations in jdbc.properties file but this is not a scalable approach as the properties file needs to be modified every time a job has to be executed. Moreover, since Pentaho refers JNDI (Java Naming and Directory Interface) connections using the name, one cannot define two JNDI connections with the same name. Also, there is a good possibility of executing the same job in parallel for different source and target databases. In such situations, multiple parameters might override during parallel execution due to common kettle.properties file. An inefficient way to tackle this problem would be to create a separate job for each client. Imagine a scenario where there are hundreds of ETL jobs and tens of clients!  It would lead to a huge duplication of effort and would be operationally ineffective. We, therefore, wanted to achieve our objective with minimal rework and a good design approach which would be maintainable and operationally effective. The Solution: In case one has installed PDI on a server at path “/opt/data-integration” and ran a PDI job through below kitchen command then by default it searches kettle.properties and repository.xml at path “KETTLE_HOME/.kettle” and searches jdbc.properties at path “/opt/data-integration/simple-jndi”. /opt/data-integration/kitchen.sh -rep test -job load_data_job To overcome this problem, one can assign the path while firing the kitchen command. For example: KETTLE_HOME=”/data/client1/” KETTLE_JNDI_ROOT=”/data/client1/jndi/” /opt/data-integration/kitchen.sh -rep test -job load_data_job KETTLE_HOME=”/data/client2/” KETTLE_JNDI_ROOT=”/data/client2/jndi/” /opt/data-integration/kitchen.sh -rep test -job load_data_job In the above example, we have separated kettle.properties and jdbc_properies files for both clients at a different location. If you run the first kitchen command, it searches kettle.properties at path “=”/data/client1/” and jdbc.properties file at path “=”/data/client1/jndi/”. If you run the second kitchen command, it searches kettle.properties at path “=”/data/client2/” and jdbc.properties file at path “=”/data/client2/jndi/”. Similarly, for each new client one can set up the configuration in a new location and point the kitchen command to new kettle and jdbc properties files. This gives the benefit of reusing existing ETL jobs, avoid conflict between parallel executions while it provides the flexibility to scale when required.

Data Cloning Through Pentaho Data Integration Clone Step

Splitting rows based on a column value. The input data comprises of ticket booking records defining number of seats booked at event, section and row level. It also contains the starting and last seat number.   Objective It was required to split ticket blocks within event_name + section_name + row_name as follows: convert the record where num_seats > 1 into as many records as num_seats assign values as follow in split records num_seats = 1 for each record seat_num = individual seat within the block original seat_num + i where “i” is counter from 0 to num_seats – 1 last_seat = new value of the seat_num as above population logic of rest of the column remains unchanged Sample Data Input:   Expected Output: So based on the above screenshots, we need to split the incoming input rows based on the num_seats field .So for first input row where num_seats=4 we need to generate 4 records as per the rules defined above. Solution: Pentaho provides a clone row step that can clone objects or rows in the same way as the main row based on a column value. Refer to the below screenshot for the solution:   Table input: This step will load the input data. Clone row: This step will create the clone objects or rows similar to the main row Nr clone in field: will specify column value to be used for cloning Add clone flag to output: will put the flag=N for the original row and Flag=Y for clone rows Clone num field (seat_index_rownum): will add the index value (0,1,2,..). Filter rows: Remove the original (non-cloned) row (where clone?=N).   Calculator and Select Values: Calculate the seat number and replace the original fields (num_seats, seat_num, last_seat, etc.) with the new values.   Table Output: Loading the data into the target table.

The Changing Landscape of the Ad Technology World

Last year saw a significant increase in the dollars spent on digital versus traditional media advertising and this has prompted marketers to spend more money online. 2014 has also been an exciting year for mobile advertising as more and more agencies are trying to create a niche for themselves through evolving standards, benchmarks, and best practices. And, as the line between mobile and the desktop continues to blur, we will likely see more dollars being spent in the mobile ad space as mobile content continues to be consumed in an app-centric environment. Customer Content Consumption Continues to Evolve As a result of this shift from classic to online media, the role of the consumer has changed from that of a spectator to an active participant putting customers in control. To add complexity,  customers are shifting between devices to experience the best digital experience available. Thus, programmatic buying is the new norm as it delivers messages to end-users with relevant impressions one-at-a-time thereby providing the desired brand experience. However, advertisers need far more insights on how ad tech companies go about spending the ad money and delivering value, even as concerns are raised on how digital data is being used to target customers. Convergence will define success for ad campaigns In a nutshell, the online ad spend will only continue to increase, but as competition grows, ad agencies and advertisers will put greater emphasis on hyper-segmentation to micro-target the right audience. In this scenario,  the key to successful campaigns will be linked to content personalization. To achieve this,  digital channels, technology, and the growing amount of data need to convulse to provide insights to better target ads. Hence, the goal for all agencies is to use technology and industry knowledge to identify the right data to provide the ideal ROI for advertisers. Thus, the fact to be remembered is that data is not an end in itself but a channel to push the right message using the right media channel at the right time to ensure successful campaigns. The major challenge in this regard is for traditional media companies to be prepared to adapt to the changing ways in which data is being consumed by end-users. Not many media companies are ready for this change though they have sufficient inventory value. What is needed is for these organizations to add value to the inventory rather than depend on the site master-head data to generate ad revenue. At the same time, for organizations ready for the change, their legacy infrastructure might become an obstacle. To summarize,  in the complex world of ad technology,  more clarity is evolving as time goes by. and as the world changes to a new order for accessing content anywhere, anytime, and on any device, the need now is for an always-on marketing strategy rather than those defined by start and end dates. To ensure success in this new order, advertisers need to have the right mix of channels, technology, and data to ensure a far greater success from ad money spend.

Testing Scenario for Mobile Business Intelligence

Overview Mobile devices have  evolved over the years affecting online access to the point where a majority of Internet access is being conducted via mobile handhelds today. To maximize this large-scale market penetration, industry segments have made mobile apps an integral part of their marketing strategy. This accelerated activity has resulted in the growth of Mobile Business Intelligence (Mobile BI) which has transformed the business landscape from a ‘wired’ to a wireless world. Mobile BI is far more versatile than other forms of intelligence as it can be woven closely into people’s movements, work, conversations, meetings, discussions and fun time.   Leveraging this versatility, Mobile BI is a package that uses existing BI applications to make informed decisions in real time. What does Mobile BI mean? Mobile BI is the ability to access BI-related data such as KPIs, business metrics, and dashboards on mobile devices.  Information delivery suitable for Mobile user interface has been made possible by the various applications provided by Mobile BI. Basic Workflow: The below diagram illustrates the flow of a Mobile BI architecture Comparison of Mobile BI Apps with other options: The table below depicts the difference in effort, interactivity and the ability of various channels in comparison with Mobile BI apps   Important points to analyse before opting for Mobile BI Strategy:- See if mobility works for you from a business perspective. See if you have the right IT infrastructure to support Mobile business? Do you have the ability to leverage hybrid (Native + Web-browser) applications to cater to different requirements? Can you account for security based-unique considerations required before its implementation? Best practices for designing the interface in Mobile BI Apps: Avoid dashboard burgeoning – A dashboard should be designed in a way that it provides the necessary information for the decision-maker. It should give easy access to information in a standardized format. The dashboard should be business driven and not purely technology driven. “More is not better”- An abundance of KPIs are not better and leads to overcrowding of the dashboard. Refrain from writing in a smaller form factor – The font size should be in a readable size to ensure that the user does not have to strain his eyes to see the font. The placeholders can be used on the small text or common form inputs like login forms or search boxes.The headings should be kept short to ensure that it does not push the content down the page or out of the frame for users. Strategy for developing and testing Mobile BI Apps: Build once, deploy anywhere strategy With a variety of development languages and approaches, it is apparent that there are varied ways to build regardless of the language. The key is to avoid building the code in a way that it has to be modified for each specific environment. To achieve a `build once, deploy anywhere’ status, it is best to exclude environment specific resources in the final application build so that it is interoperable with the environment. The environment specific artefact can be deployed to the container separately from the WAR and at any time. There are some server-side platforms available for running such applications. E.g.: Oracle WebLogic. Account for New mobile scenarios The evolving mobile solutions landscape has led the field of mobile testing to a challenging level and it has become important for  QA managers to understand the unique testing needs of the Mobile BI segment. The QA needs to identify the needs/establish requirements of the various users and design scenarios, taking into account, aspects like interoperability, security and reliability of the mobile app. Pros and Cons of Mobile BI: Pros: User can pinch, swipe, and tap to easily interact with and analyse company data. Cloud based mobile solutions increase collaborations. Allows the user to send notifications in various forms like email or text messages. Provides sales and field support representatives with the data they need to answer customer questions on the spot. Cons: Too much reliance on mobile devices and tablets increases the risk for mobile computing. Devices are expensive and replacement costs are steep. Mobile BI apps in general are not very interactive and restrict users from drilling down into data. Example Snapshots of some BI Apps:     Widely used products for Mobile BI: Oracle business Intelligence Mobile:- http://www.oracle.com/us/solutions/business-analytics/business-intelligence/mobile/overview/index.html Tableau Mobile Business Intelligence :- http://www.tableausoftware.com/solutions/mobile-business-intelligence Mobile BI QA The aim of testing BI applications for mobile is to achieve credible data and a good design display with a user-friendly interface. It is also important to ensure that the back-end of a mobile BI system is able to handle the processing load to display data in a timely manner. How to Devise a Mobile BI Test Process: Validate the data required and identify data sources Identify and decide the category of Mobile BI App to be implemented, i.e.: i.    Mobile Browser Rendered App ii.    Customized App iii.    Mobile Client App Understand data, unearth related problems early and identify boundary value conditions for test scenarios. Set up the acceptance criteria as per data accuracy / consistency and benchmark the performance time for rendering of reports. Test plan to identify the scope of testing and prepare test data and testing techniques. Tools which can be used for Testing BI Applications on Mobile: BI Application testing encompasses a lot of systems like data mining, statistical analysis and graphically-rich dashboards.  To test all these components tools are available: Eggplant: This is a tool by Testplant. It uses image recognition technology to instantaneously test multiple aspects of Business Intelligence systems. It is also flexible to operate across multiple applications which helps for better testing. Sikuli: Sikuli uses image recognition technology to identify and control GUI components. URL: http://www.sikuli.org/ References: http://searchcio.techtarget.com/essentialguide/Strategic-business-intelligence-for-a-mobile-future#guideSection1 http://www.tableausoftware.com/learn/whitepapers/5-best-practices-mobile-business-intelligence http://en.wikipedia.org/wiki/Mobile_business_intelligence http://wiki.scn.sap.com/wiki/display/BOBJ/Architecture+and+Workflow+Diagrams

Flashback and Looking Ahead: What ‘Tweeples’ Said During the Festive Season?

This New Year, Twitter saw two hashtags trending namely #bestmemoriesof2014 and #20ThingsIWantFor2015 worldwide. In order to find out what people were tweeting in the festive season, in terms of their experiences in 2014 and the emotions they were displaying, I captured and analyzed lakhs of tweets for these two separate hashtags. The tweets for #bestmemoriesof2014 and #20ThingsIWantFor2015 were collected during different time windows on 31 December and 1 January IST when these tags were trending in the Global Top Five List. On applying  `Text and Sentiment Analytics’ technology to this data I came up with some interesting results. Take a few minutes to walk with me through peoples’ best memories in the past year and what they wish for in 2015. #bestmemoriesof2014 Findings: Some of the most common  words that surfaced include One Direction (British pop boy band based in London), friends, love, Putin, crush and concert. Parsing the tweets to identify the sentiment polarity revealed that over 94% of cities were positive overall barring a few cities like Chicago which were slightly negative in overall sentiment. Along with this, I looked at subjectivity, and the results are as follows: #20ThingsIWantFor2015 Some of the most common words echoing peoples’ wishes for 2015 are hug, new phone, selfie, money, good grades and  being a good person. Sentiment analysis for the wishes showed a mixed sentiment with 68% cities having overall positive sentiment and 25% having overall negative sentiment.   Tweets for this hashtag were more subjective as compared to the former:   P.S.- All the visualizations have been created using Tableau.  

Compelling Reasons for Visualization in Retail Trading Applications

An adage goes as: `a picture is worth thousand words’. I am slowly finding out that this can apply very well to the world of retail trading applications. In fact, visualization can become a new way of trading in the future.  In some areas, such as technical analysis, a methodology for forecasting the direction of prices through the study of past market data, evolved quickly after the OHLC(Open, High, Low, Close) data of a security was plotted in the form of line and candle stick charts. However, even though the capital markets industry is constantly evolving with innovations and methods of trading, visualizations remain understated. Uses of Visualization Visualization can be very useful in analyzing market data, company results, fundamental information and also news. However, the type of visualization should be carefully selected for each trading app widget, i.e. Watchlist, Option Chain, Order book etc., in such a way that the data represented remains meaningful and tradable. I have always been a fan of www.finviz.com as the visualizations provided by them are very relevant and tradable. However, some of the new features like 3-D heat map are undoubtedly visually appealing, but their relevance and tradability remains questionable. Hence, it is crucial to find the right balance between the visualization type per widget and the data to be visualized. Visualizations developed for a retail trader should be focused to simplify the process, instead of having to skim through tons of data, analyzing them, trading them and finally tracking them effortlessly. In other words, all widgets be it the simple widget like a Watchlist or a complex widget like an Option Strategizer; visualizations should be customizable as per the needs of the trader. Recently, we at Tavant, were working on a project for one of India’s leading bank’s retail trading web application, and the results were studied through web analytics. The response visualizations received from the traders was fascinating! Some of the visualizations like the bubble chart that were provided to analyze market scenarios, and news analytics received significantly more views than the traditional market statistic data like top OI gainer, volume gainers,  etc.  We also found significant tractions for other visualizations like Fin Map, an interesting, but complex variation of a heat map that could help a trader to analyze a company’s results at a glance. To summarize, there were more views for every visualization implemented on a single trading day. Meanwhile the team at Tavant Technologies, Bangalore, is trying to blend heat maps with technical charts to obtain calendar charts to offer technical analysis to even novice traders. Portfolio Heat Maps In concurrence with the above-defined principles, visualization of a portfolio in the form of a heat map was constructed. Heat map that is one of the many ways of visualizing portfolio was attempted. Heat maps are an easier method to track and analyze a portfolio with colors (red, green or gray) and the area of the rectangle used in the heat map summarizes the portfolio performance at a glance. Traders, on right click, were provided with the option to trade. For advanced traders or portfolio managers, heat maps are constructed with an option of tracking the performance by Invested amount, market value or profit & loss. Drilldowns in the heat map can be used to analyze the portfolio based on asset allocation, sector allocation, and capital allocation. Thus, visualizations are a whole new possibility for retail trading applications where the trader can get rid of numbers, percentages and averages, and trade purely based on colors, shapes, and sizes.

Morphē – Adaptation to Evolution in the Consumer Lending World

During the late 2000s, a set of events, characterized by a rise in mortgage delinquencies and foreclosures, and the decline of securities backed by mortgages, led to the mortgage crisis.  House sale prices displayed a steady decline, and as interest rates increased, mortgage delinquencies soared and securities backed with mortgages lost most of their value. The mortgage bubble had burst, and the ensuing crisis had long lasting effects on the U.S. and European economies. Many technology companies which had focussed on this industry segment turned their attention elsewhere. However, long before the mortgage bubble burst, Tavant had already developed an intrinsic relationship with this industry and as a result had acquired deep expertise in providing solutions for achieving higher lead conversion rates, lowering processing costs per loan, optimizing key servicing indicators such as default rates and minimizing the cost of securitization. The company has accumulated more than 2500 person-years of application development experience across the entire mortgage lifecycle. This commitment was reflected in Tavant being recognized in the year 2007 by Mortgage Industry Magazine as one of the magazine’s Top 50 Mortgage Technology Providers.  Special emphasis was placed on Tavant’s proven ability to provide a high degree of functional value to mortgage lenders. When the bubble burst, our commitment did not waver.  We morphed and accepted the fact that change is the only constant in life. Our commitment to this industry meant that this was not the time to move away but was a period of time for investment. We, therefore, continued to grow our team of techno-functional experts, developers, and architects. When business was slow, and other companies were rerouting talent to other domains, we invested in domain training for our people. They were given domain specific training to understand the eco-system and the way the mortgage industry functioned. Now, the industry is showing signs of recovery, and we are there. However, we have morphed. We are not students waiting to learn from industry experts but are knowledge sharers. Our depth of knowledge about this industry has resulted in a marriage of sorts between technology and functionality. We don’t wait for directions from our mortgage industry customers – we lead discussions. Be the glue and not the hammer Some of the key lessons that we have learnt and which have helped us transform from our role as software service providers to techno-functional experts is, we replace pieces instead of the whole ship, thereby allowing the ship to keep moving. We get into shorter engagements till the end of a project. Where the rule of the game was to go after long engagements, we offer short deliverables that result in long term trust in our abilities to help them take strategic decisions. In the place of multiple systems that are consumer facing, with different benefits and features in each system and each LOB and solution works in isolation, we offer customers increased portal offerings and holistic one-stop-shop solutions We offer customers the ability to plug-in and plug-out components, depending on the business requirements.   Thus, to summarize, our investment in domain knowledge is our strength now and the precise reason why some of the biggest names in the industry are partnering with us for strategic solutions which are aligned to their business roadmap. FAQs – Tavant Solutions How does Tavant help lenders adapt and evolve in the changing consumer lending landscape?Tavant provides adaptive lending platforms with flexible architectures, rapid deployment capabilities, and continuous innovation programs that enable lenders to quickly respond to market changes and evolving customer expectations. What evolution strategies does Tavant recommend for consumer lending transformation?Tavant recommends phased digital transformation, customer-centric design thinking, agile development methodologies, and ecosystem partnerships that allow gradual but comprehensive evolution in lending operations and customer experiences. How is the consumer lending world evolving?Consumer lending is evolving toward instant decisions, personalized products, embedded finance, alternative credit data, mobile-first experiences, and ecosystem-based services that integrate lending with broader financial and lifestyle needs. What drives adaptation in the lending industry?Key drivers include changing customer expectations, fintech competition, regulatory changes, technological advancement, economic conditions, and the need for operational efficiency in an increasingly digital world. How can traditional lenders successfully evolve?Traditional lenders can evolve through strategic technology adoption, cultural transformation, customer-centric innovation, partnership strategies, and gradual modernization that leverages their existing strengths while embracing digital capabilities.

MBA’s Annual Convention – The Real Estate Industry Has Definitely Picked up Momentum

The MBA Annual 2014 was exciting and an enriching event as always. It is an event that everyone from the industry looks forward to. To summarise the overall experience and map it to my knowledge, I would say that I found the general atmosphere to be upbeat.  There were positive vibes among participants as well as speakers about the economy, the housing market and the overall mortgage industry. Their feelings echoed mine as I observed the trend being positive with unemployment reducing to a healthy level. It is now a purchasers’ market! People will have larger incomes that will encourage them to move to larger houses. This translates to surge in business for the consumer lending industry. I also believe that new constructions will rise to meet the growing demand. This is a period of progress and momentum. The economic growth will lead to increase in demand for housing, which will require lenders to step-up & extend credit to a wide-range of responsible borrowers. In 2015, key trends that will impact mortgage companies will be the level of customer satisfaction, compliance related legal issues, mobile accessibility and warranty management. With regards to the current state of Government Housing Finance, I think that the time is right for reforms to be initiated. For example, if consumers are weighed down by student loans, then this will indirectly affect the market for housing mortgage loans. Some good work has already begun. One of FHFA’s key initiatives is revising and clarifying the Representation and Warranty Framework under which lenders and enterprises operate. These representations and warranties provide the necessary assurances that allow Fannie Mae and Freddie Mac to purchase loans in an efficient and responsible manner without checking each loan individually or being at each closing. They also provide the enterprises with remedies to address situations where lenders obligations to meet the enterprises’ purchase guidelines have not been met completely. To summarize, I believe that the mortgage industry will experience continued growth in the coming years. However, this time around, technology will drive the business. Apart from being solution providers, business & technology partners will need to be domain experts who understand the nuances of this complex industry and offer advanced solutions which are end-customer friendly.

Tavant Support: Evolution & Growth Of Warranty System

While application support in the global software services industry is often treated as status quo maintenance, at Tavant, we see this as an opportunity to showcase continued improvement and excellence. We consider application support as no different from an innovation lab where new ideas are generated and implemented. In this blog, we will discuss Tavant Warranty Management System (TWMS) during its support phase and analyze its growth and evolution over the years. On November 21, 2005 TWMS went live and in 9 years of Tavant Support, the system has seen tremendous growth. Milestones in TWMS’s evolution (2006 to 2014): The Warranty system auto processed a hundred times more claims than it did in the year 2006 The Recovery module registered seven time’s growth The system processed 235 percent more claims, i.e. over 29 percent average annual growth Recovery claims were generated for 7 percent of the warranty claims in 2007 versus 21 percent today The TWMS has so far processed 1.7 million claims, i.e. net worth US$791 million   In Tavant, we have always believed in holistic growth, and this is evident in our application development and support processes. TWMS and its accompanying accomplishments were possible because care was taken to ensure that innovation happened in every module to offer efficiency, automation and better performance. Processes that enable these accomplishments: Processes that enable these accomplishments: TWMS helps businesses to process claims faster and with minimal effort, by ramping up to automatically process claims Auto-processing is achieved through complex business rules using a powerful business rules engine It simplifies the process through which a dealer or user can file claims. TWMS helps to save claim filing and processing time by auto-populating known fields with the help of an intelligent processing engine It has effective recovery generation options and auto recovery initiation engines It offers multiple admin configuration setups to tweak the system flow as per business needs Tavant Warranty Management System is only an example of how Tavant Support evolves an application and helps businesses to grow.   We believe excellence is not something to be achieved, but to be continuously pursued.

How to Overcome Challenges with Mobile App-Server Communication Process

Mobile applications (apps) have changed the way consumers act, interact, purchase, sell and search.  According to a five-year report on the mobile Industry by Flurry*, “Apps have commanded 86% of the average US mobile consumer’s time, or 2 hours and 19 minutes per day in 2014.” One reason for this popularity is the instant gratification that consumers derive from mobile apps. However, mobile app performance is determined by networks & servers, putting much pressure on app developers. Is there a method to circumvent server and network performance issues? I suggest that, app developers, to mitigate server performance issues, should ensure that the communication process remains asynchronous, during the following three stages: The facilitation within the application to communicate The communication itself, and, The post- processing of the result.   We normally assume that server communication code resides inside the Server Communicator (block) and cannot be reused for other server communication. However, that need not be the case.  For example, one of the components of the server communication process is the Action Class.  Action Class, is an independent component which knows the vital points about a particular network action. It knows the process to create a request, parameters to be used, headers to be created, etc.  It assimilates responses and post process, extracts information to be consumed by the application.  It brings up custom error responses and shows the method to handle the same. The `Action’ does not send out a request to the server but acts as a bridge between the two.  Any new communication to the server will result in a new action class, keeping the existing ones untouched and unaffected. Thus, every step in the server communication process can be treated independently.  It is possible to fit new server engines to support new servers, without disturbing the ecosystem, and it is possible to run parallel server communication processes without any of them affecting the application’s performance. To summarize, by following this method, it should be possible to ensure that every communication block is independent and yet works in tandem. To know more, read the whitepaper “Ensuring Effective Server Communication in Mobile Applications” by Deepak Mariyappa and Ravi Peravali.

Understanding the Relationship between Warranties and Customer Satisfaction

Original Equipment Manufacturers (OEMs), equipped with the latest and best product knowledge, help in enhancing customer value and product quality. Aberdeen Group, a well-recognized business-intelligence research organization, presented a study recently. It said one of the top priorities for organizations is to improve customer satisfaction, after which comes the objective of managing costs, improving product quality and increasing revenue. But how can manufacturers be equipped with the knowledge to meet these objectives? A warranty management approach can be the solution for these OEMs. If a warranty or service contract solution can generate a product record history, it becomes easy to track the product history throughout its service life-cycle. With this information, manufacturers can gain valuable insight into the product life-cycle, which in turn provides insight for service improvements. That is why manufacturers, nowadays, are looking at warranty solutions with a strategic perspective. To achieve these objectives, manufacturers need to upgrade their existing and isolated legacy systems to integrated warranty systems, which can use advanced analytics services to churn warranty data into meaningful information. Such information helps the OEM to improve its service offerings to the customers. With real-time analytics in place, manufacturers can gain substantial productivity, increase operational efficiency, and retain customer value. The Challenge A large automobile manufacturer was facing challenges with its equipment warranty services and parts logistics system. The customers were not satisfied with the quality of equipment and hence, there were frequent customer complaints. Additionally, the response time to service requests was high compared to competitors. The organization was unable to achieve the Key Performance Indicators (KPI) for equipment warranty and service parts. The biggest blow was that both, internal and external customers were complaining about the process lapse.  Some of them were turning to competitors. The Solution With immediate effect, the OEM incorporated a warranty system for the client. Streamlining of processes and communication channels ensured that the equipment warranty KPIs were met. Furthermore, the analytics system traced products throughout their life-cycles and provided key insights to assess supplier performance throughout the supply chain. Moreover, it helped the OEM capture the service time taken by each of the servicing dealers, thereby keeping a check on the service turnaround time. It generated an effective internal process and helped immensely in managing and engaging internal and external customers. The Benefits Customer management & engagement Comprehensive reporting & analytics Reduced costs & customer complaints Process enhancement Proactive response to customers Development of contingency plans Improved efficiency of onsite service requirements Lesser turnaround time Increased brand credibility

Warranty Management – A Strategic Business Advantage

While warranty solutions began as a way to attract customers, their scope and application have seen radical changes over the years and organizations now realize that warranty management  is a source of competitive business advantage.   It offers a multitude of benefits like faster claims processing, decreased fraudulent claims, better operations management, and increased bottom-line results leading to improved satisfaction of customers and service providers. However,  many fail to understand that effective functioning of warranty is hampered  by a silos-based approach. Most organizations adapt the silos approach for warranty without giving it the focus it deserves. The fragmented approach to warranty leads to dissatisfied service providers and customers, greater turnaround time, high operating costs (which otherwise could have been avoided). A detailed analysis of the holistic approach can easily reveal the strategic impact it bears on each of the value chain functions, such as manufacturing, quality, sales, service, and finance. When the warranty-management process is integrated with all the key business functions, it can turn around the revenue chart of the business and multiply profitability. A holistic approach to warranty has various benefits: Improved customer satisfaction – Integrated information ensures greater connectivity, timely information and history recall for customers during product lapses. It, therefore, helps to increase commitment and boosts brand perception and organizational credibility. Faster claims processing – A closed-loop approach helps in streamlining the claims process and reduces operational discrepancies. It assists in tracking warranties across the product lifecycle and helps in optimizing product pricing while minimizing warranty costs. Decreased fraudulent claims – Processes and policies can be integrated into business logic. Streamlined processes lead to better visibility of warranty information. Integration minimizes warranty costs while enhancing the early error-detection process, thus widening the scope of improving product quality, well in time. Decreased operational cost & increased bottom line – An integrated warranty package minimizes process errors that rob organizations of strategic business focus. Product lapse trends can be closely examined for proactive measures to ensure quality and process control. An efficient warranty management solution fosters collaborative solutions, enhances product quality and customer satisfaction, addresses quality concerns, and improves partner relationships, thereby increasing operational efficiency and bottom-line results. The Client A global organization that specializes in providing diversified services to domains like home comfort, transportation & preservation of  food & perishables, and securing homes & commercial properties. The Challenge The organization comprised multiple business units, each of which had diverse business requirements and ran on multiple legacy systems. Due to the market’s demand, the organization accepted additional responsibilities of spreading its services to Europe, North America, Asia, and Lagos. It faced a series of challenges in keeping up with requirements, as  it lacked an inventory management system, coupled with ad-hoc retrieval systems, resulting in lack of transparency. All this led to inefficient realization of ROI. The organization was in need of a robust and comprehensive central solution to standardize the warranty management process. The Solution Massive data from all the business centers was integrated into a centralized platform. The analytics solution ensured timely projections across the value chain functions and helped in faster claims processing. It reflected early warnings of failures and identification of root causes to minimize product failures that were impacting the entire product lifecycle. The closed-loop solution transformed performance data into strategic intelligence, directly enhancing operational efficiency, decision making, customer satisfaction, reporting, and communication capabilities to identify and detect issues early. It also minimized warranty costs and enhanced business profitability. The Benefits Increased customer satisfaction & retention Better communication and collaboration Reduced warranty costs & claims processing errors Lesser turnaround time for settlement of warranty claims Greater process and operational efficiency   Therefore it is crucial for organizations to ward off the one-dimensional perspective and move towards a holistic approach to warranty solutions. The integrated approach leads to a 360-degree perspective of information, efficient claims processing, better analytics, and reduced manual intervention.  It also helps in achieving optimal business results and accelerated warranty management. It enables organizations to run their business functions with strategic alignment and purpose that can streamline their processes, enhance customer satisfaction, brand perception, and maximize scalability.

Understanding Concurrency in Mule 3 ESB

Enterprise service Bus (ESB) is an approach for application integration in distributed and heterogeneous environments. One of the requirements for ESB is a highly concurrent system. Mule ESB is a lightweight Java-based Enterprise Service Bus (ESB) and Integration Platform that allows developers to connect applications and enables data exchange. Mule provides three layers of highly configurable concurrency called `thread pools.’ Receiver Thread Pool – which originally receives the message, and either -Synchronously processes the entire flow, or -Asynchronously ends it by writing a message to a queue. 2. Flow Thread Pool – which asynchronously processes the bulk of the flow. 3. Dispatcher Thread Pool – which sends messages asynchronously to one-way endpoints. Synchronous vs. Asynchronous processing in Mule: For synchronous processing, the same thread will be used to carry the message through Mule. If the message needs to be sent to an outbound endpoint, the following will apply: If the outbound endpoint is one-way, the message is sent using the same thread. Once sent, the thread resumes processing the same message. It does not wait for the message to be received by the remote endpoint. If the outbound endpoint is request-response, the flow thread sends a message to the outbound endpoint and waits for a response. When the response arrives, the flow threads resumes by processing the response. For asynchronous processing, the receiver thread is used only to place the message on a SEDA queue. At this point, the message is transferred to a flow thread.  The receiver thread is then released into the receiver thread pool so it can carry another message. When a message is processed, if it needs to be sent to an outbound endpoint, one of the following applies: If the outbound endpoint is one-way, the message is copied, and the copy processed by a dispatcher thread while the flow thread continues processing the original message in parallel. If the outbound endpoint is request-response, the flow thread sends a message to the outbound endpoint and waits for a response. When the response arrives, the flow threads resumes by processing the response. Conceptually, messages are processed by flows in three stages: The message being received by the inbound connector The message being processed The message being sent via an outbound connector.   Even if the connector is not provided for inbound or outbound, Mule creates a default connector with default configurations for each endpoint. Example: There needs to be a sftp end point (inbound) and two levels of configured concurrency – the Receiver Thread pool and the Flow Thread pool. The Dispatcher Thread pool can be configured in the same way and will give the same performance. sftp:inbound-endpoint exchange-pattern=”request-response” and receiver-threading-profile doThreading=”false” – Multiple receiver threads get created and performance is very slow due to synchronous nature of flow (request-response exchange pattern) sftp:inbound-endpoint exchange-pattern=”one-way” and receiver-threading-profile doThreading=”false” – Multiple receiver threads get created, and performance is better than Case 1 but due to multithreading, a race condition could occur causing multiple threads to start consuming the same file. This is especially true when file size is large sftp:inbound-endpoint exchange-pattern=”one-way” and receiver-threading-profile doThreading=”true” maxThreadsActive=”1″ maxThreadsIdle=”1″ poolExhaustedAction=”DISCARD” – Only one thread gets created and performance is the same as  in Case 2.  However, there is no way to set the threadWaitTimeout in this case and hence once the thread times out after the default value, the system will stop consuming more messages (files in this case) sftp: inbound-endpoint exchange-pattern=”one-way” and receiver-threading-profile doThreading=”true” maxThreadsActive=”1″ maxThreadsIdle=”1″ poolExhaustedAction=”WAIT” maxBufferSize=”1″ threadWaitTimeout=”-1″ – Only one thread is created and performance is the same as in Cases 2 and 3. We have set the threadWaitTimeout to indefinite as there is only one active thread, and it will keep on pooling forever.   Catch: The catch is that configuring the receiver-threading-profile doThreading=”false” has no impact whatsoever in the overall processing. Internally there will be multiple (default) receiver threads that will be created. So don’t rely on doThreading=”false” alone to stop multithreading. Instead do the same configurations as mentioned in Point 4 above. The same is applicable to dispatcher-threading-profile. Reference: http://www.mulesoft.org/documentation/display/current/Tuning+Performance

When to Use Enterprise Service Bus (ESB)

Point-to-point communication normally has issues with scalability. These issues are further compounded with increased systems.  ESB, a middleware technology,  is a Bus-like architecture used to integrate heterogeneous systems. In ESB, each application is independent and yet able to communicate with other systems.  It, thus, prevents scalability issues and ensures that communication happens only through it. ESB’s guiding principles are: Orchestration – integrates two or more applications and services to synchronize data and process. Transformation – transforms data from canonical to application-specific format. Transportation – protocol negotiation between multiple formats like HTTP, JDBC, JMS, and FTP, etc. Mediation – multiple interfaces for supporting multiple versions of a service. Non-functional consistency – transaction management and security.   When to use ESB architecture The first step when opting for ESB architecture is to map its value to requirements.  Given below are some usage guidelines: When system integration points grow beyond two, with additional integration requirements. When using multiple protocols such as FTP, HTTP, Web Service, and JMS etc. When there is a requirement for message routing based on message content and similar parameters.   ESB architecture Implementation Rules Messaging services like JMS can be used to de-couple applications XML format when canonical data is used for communication Using an adapter that is responsible for marshaling and un-marshalling data. The adapter is also responsible for communicating with the application and bus. It is then used to transform data from application format to bus format Non-functional activities like securities, transaction management are also performed by the adapter in ESB.   To summarise, ESB provides for a flexible architecture. It enables multiple application communication and provides easy integration with other systems.

How to Manage Warranty Costs

Minimizing warranty costs has always been one of the most pressing concerns for organizations. While minimizing the warranty spend helps, it is also important to make an objective estimation of the costs involved. Due to the lack of information about every single step involved in the business process and the associated costs, a principle barrier becomes evident in charting the right budget. Cost mismanagement in warranty can rise from ad-hoc estimation techniques and planning for warranty reserves. The elements impacting warranty costs go beyond product repair and overflow into other areas. To estimate such hidden costs accurately, without some kind of integrated business logic, is extremely complex. The need of the hour is quantifiable strategies and techniques that can optimize the effectiveness of the warranty process. Time and again, warranty analytics have proved to show remarkable results by integrating data that helps to improve product and process performance. That helps not just in managing costs, but optimizing overall business profitability as well. Integrated technology can reflect warranty costs and assist in better operational and strategic business execution. That aids in formulating streamlined warranty management processes, building brand images, reducing operational costs, improving product quality and optimizing organizational efficiency. Business Case The Client One of the world’s fastest-growing automakers. The Challenge The existing warranty management system lacked a comprehensive analytics system that could integrate data across business functions. The organization faced a substantial revenue loss, maximizing ROI was a challenge, and organizational credibility was at stake. The team handled issues on the basis of client demands while using traditional legacy systems. The Solution With an intention to mitigate warranty costs, the organization integrated warranty data across all the business units into a single process with common business rules. The analytics solution helped reduce costs drastically and provided the organization with quantifiable data that highlighted areas profitable to the business, and the pain points that required immediate process upgrading. The outcome of the analytics system was a whopping cost reduction of close to 40% across all regions. Additional benefits included: Customer satisfaction Greater process transparency Scalable solutions Standardization of systems Quantification of results

Three Step Process to Automate Software Testing

Large software organizations have long been patronizing software testing. However, small and medium (SMBs) sized companies find manual testing time consuming and expensive.  For such companies, automation can prove to be the right alternative. Given below is a three step process to automate your software testing requirements: STEP ONE Any software which requires functional tests, if supported by automation testing, must follow a Q&A process.  To explain, if a user has ten acceptance test cases, assuming all these can be automated, the following questions need to be answered: Are these test cases within the scope of future releases? Are they a part of the regression suite? Will this particular functionality be used in a majority of flows? Are these tests of high complexity? Are they critical? Thumb rule is to not automate all scenarios, fields on pages etc. unless specified by the client. STEP TWO Understand the application architecture Synchronization process between third party vendors (if applicable) Database design UI design frameworks (e.g. JQuery, Knockout, Wicket, Vaadin, HTML5, etc.)   Before finalizing the automation process, these factors also need to be considered: Customer expectations Costs involved (Closed loop or Open Loop) What is the type of application (AUT)? Application’s complexity System configuration support (OS, browser, 32/64-Bit etc.) Ease-of-use for maintenance Forecast on break-event point (ROI)   STEP THREE Finally, the framework should have the following parameters: It should provide feasibility to users as per project requirements and also enable all (including non-technical personnel) to participate by writing and maintaining test scripts. The test design approach (E2E, Module wise etc.) The integration process with third party tools (need based) The framework’s flexibility in script enhancements.   Automation ensures that testing can be undertaken by any company at any time. While automation has its advantages, it is not 100 percent foolproof and manual intervention is recommended at least at the final stages of product release.

Curious Case of Bombay Stock Exchange

BSE (Bombay Stock Exchange), India’s second largest exchange suffered a technical snag. Trading at BSE was suspended for 3 hours and 3 minutes. This comes as a major to blow to BSE, who is trying to regain its lost market share from NSE. Will the recent developments put BSE a distant second to NSE? Although trading halts at exchanges are not totally unheard of in the industry; In November 2009 LSE (London Stock Exchange) suffered a technical snag, which affected trading for more than 3 hours and in August 2013 NASDAQ was shut down for 3 hours due to a connectivity issue. But the case of BSE is baffling since this is the 4th such instance in the last 4 months! The recent one being the biggest of them all. Since the introduction of derivatives in the Indian market, BSE has been steady losing out its market share to NSE and lot of derivatives traders prefer NSE. However, many traders who trade in equity continue using BSE. However, such frequent snags will offer them a reason to shift. Yesterday for most traders especially day traders, NSE came to their rescue. The proof seems to be in the pudding for the brokerage industry too. Many brokerages that have launched their new trading systems this year seem to prefer NSE, which can be seen in the order entry panels. NSE appears ahead of BSE or in some systems, the order entry opens with the default option as NSE. As mentioned earlier, in the trading world, technical snags at exchanges may not be a new issue for traders and brokerages. They will accept the snags and move on. But the frequency of such occurrence bothers everyone. BSE need not tread a new path to avoid such issues in the future, rather it can pull a leaf out of LSE’s or NASDAQ’s or even NSE’s book, who seem to have averted such repeat occurrences successfully.

CUITe Might be the Next Step Forward in Functional Automation

Coded UI is a highly powerful module in Visual Studio which simplifies coding in terms of UI and Functional Testing.  However, Coded UI, in spite of its advantages has one major flaw – it typically generates a code which is based on recordings of manual tasks performed in the UI! The recordings are done as per the pixels of the system and therefore difficult to decipher. A solution to this is Coded UI Test Enhanced (CUITe) framework. This framework involves an open source Microsoft Tool called CUITe, which instead of recording the actions performed on a given UI, records the UI elements by Object ID. How CUITE works CUITe focuses on the PageObject Model of Automation which involves having an in-house repository for all the objects recorded from the HTML page, as below: Advantages of CODED UI framework: Easier code maintenance. Zero need to change code to match UI changes (only the elements changed in UI need to be re-recorded). Auto generates code with namespaces, avoiding confusion regarding which namespaces are to be included. Code can be run on any system. (Please Note: As record and playback functions involve recording by pixel values, UI elements may not be rendered at the same pixel location on all the systems) Keyword and data driven testing becomes simpler as a result of automation and users can write their own custom code instead of customizing a system generated code.     With CODED UI’s growing popularity among the development community, it is likely that CUITe might be the next step forward in Functional Automation.

OSB TIP: A Proxy Service Which Can Take Any XML Data to any JMS Queue in Oracle Service Bus

How to make Warranty Claims Management work for you?

It is accepted that managing warranty claims is one of the most daunting tasks in the warranty lifecycle. Managing large amounts of data brings the possibility of errors at different levels. Such errors can impact the image and overall functioning of an organization. Current trends in managing warranty claims  The current trend of managing warranty claims data involves manual management. The challenge of a manual approach is its failure to integrate with the ongoing enhancements in business processes. Several other negative implications include cost mismanagement, operational discrepancies, customer dissatisfaction, and quality issues. Is there a right approach?  Due to erroneous processing of warranty claims, organizations have to incur huge revenue lapses. The right approach to correcting and maintaining warranty claims is to automate all key business policies and procedures through a comprehensive warranty claims solution. A robust system will improve the claims process and assist in building strategic plans to detect product failures early. That will help shorten the claims-processing cycle and maintain the ongoing improvement of end-to-end warranty claims management. In greater numbers, organizations are opting for a closed loop warranty systems that direct, process, and track warranties as well as provide feedback for continuous improvement across product lifecycles. This helps in optimizing product pricing and improving customer satisfaction while minimizing costs. Industry Example A specialist technology provider of manufacturing advanced food-processing equipment had plans to expand its service operations across South European regions. Implementing common processes and operations of manufacturing equipment was highly critical. This would assist in streamlining the processes, building a brand image, and gaining better control of the business and revenue management. Business Challenge The organization was exploring options to enhance its productivity by scaling the existing processes. One of the processes that needed immediate improvement pertained to the warranty claims management system. As the process was managed through a manual legacy system, it hampered productivity tremendously. Problems the company faced: Ineffective claim management due to manual tracking and management systems Communication channels were not automated which led to process errors Turnaround time and resources for project completion increased Inefficient management of the highly customized legacy due to lack of manpower. As a corrective measure, and to improve product quality, customer satisfaction, and profitability, the organization decided to implement comprehensive warranty enhancement solutions.   The Solution  A warranty management solution with advanced predictive techniques was implemented to identify fraudulent and inappropriate claims. New rules were added to improve claims precision and ROI. The solution provided rich functionality for registrations, claims management, returns & supplier management, and parts returns. The results were astounding. 95% of warranty issues were resolved. Warranty claims dropped by more than 60% in less than 2 years. The client experienced a series of business benefits such as effective cost, time and customer management, and saved huge amounts in customer service costs. The new system was flexible, easy to maintain, and highly scalable for future business enhancements.

Data Migration Challenges

According to a Gartner/Standish group study, 67% of data migration projects suffer from implementation delays. Given the importance of data in the modern enterprise, flawed data migration can have severe repercussions. A nuanced understanding of the various challenges in Data migration is required to mitigate many of the risks associated with this activity. Importance of Data Quality As with most data integration efforts, data quality is one of the biggest challenges in data migration and some of the challenges are: Given the non-recurring nature of most data migrations, handling data quality issues in a live post-migration environment can be very challenging. Poor data quality imposes significant costs post-migration, with issues ranging from poor business intelligence to delay/disruption in business processes. Data quality issues are amplified when migration happens from a legacy system (with poor data quality) to a newer application with a far richer feature set and a stricter data model. This necessitates a lot of planning before the migration process can commence.   Solutions to manage Data Migration effectively To ensure good data quality coverage, complete profiling of the source data systems must be conducted. This process must be complemented with the reconciliation of business rules across the source and target systems. Both these activities, when completed with the involvement of all relevant stakeholders will provide the data migration team with a lot of insights into data/rule gaps between the source & target systems and help create data validation/transformation rules to be used in migration. These rules can be further fine-tuned by accounting for deduplication and consolidation if necessary (For instance, when multiple source systems are involved). If the target system is still evolving/being built, strong change management processes need to be put in place to ensure the data migration process keeps up with application changes. Choosing the right technology/tool stack is one of the biggest decisions that awaits the migration team. The choices range from a custom-built solution, data integration tools to a hybrid solution. While there are merits and drawbacks to each choice, the flexibility and comprehensive data integration capabilities offered by modern ETL tools make them a compelling choice. Many of these tools offer integrated development environments that speed up the development process and provide plenty of customization capabilities through scripting/reusability. The data migration solution offered by Tavant uses Talend, an open-source tool that provides a robust data integration toolkit and Java/Perl based scripting allowing for significant customization. Other technology challenges include differences between the underlying source/target database systems (mismatches in terms of data types supported, date/time format mismatches etc), encrypted data, and handling of character encoding and numeric precision. A good data quality management process and technology solution need to be backed up with a good operational process that can support an iterative data migration validation/ implementation process.  Other operational challenges that need to be factored in are the migration of hot data (active business transactions) and the creation of failback provisions. A robust data testing strategy is required to not only ensure that all data within scope is migrated but also that the migrated data is functionally usable on the target system(s). Finally, an experienced data migration team can drastically cut down on the learning curve and hit the ground running.   While each data migration project has its own dynamic, a good understanding of challenges and best practices can significantly reduce the chances of running into common data migration roadblocks. Recognition of these challenges will also ensure that the data migration process receives the support it requires.

5 Reasons Why Coded UI is the Right Step Forward in QA Automation

The robust capabilities of Visual Studio and TFS (Team Foundation Server) have made them hot favourites for building business applications. The combined usage of TFS, Visual Studio and their test tools, augment the process of agile development efficiently (through different phases of development including continuous integration). In the recent past, I have noticed a growing trend in the usage of TFS as an integrated solution for project management, application life cycle management and source control. In this blog, I provide a glimpse into the major factors which drive test experts and architects to evangelize Coded UI (a part of Visual Studio) as a standard automation framework from a software test catalyst standpoint. Testing engineers and developers can work using the same tools/language, enabling them to collaborate effectively. Coded UI tests are compatible with both Web and Windows projects and C# is known for its robustness. Of course, there are many popular feature enriched test tools available in the market, but most of them only support testing of Web applications. When we review the supported configurations and platforms of coded UI tests, we can see its  extensive support across multiple levels Incorporating the built-in features of Coded UI into parent class wrappers extends test capabilities and enables testers to leverage the use of APIs by inheriting them into their own frameworks as they evolve. Extended features available in the test controller and test agents provide for: Developing an extensive test suite and testing in local environment The ability to regularly run the test suite remotely in a Lab environment providing for increased efficiency and productivity with comprehensive regression.   Using Coded UI with layered framework offers high flexibility to develop sophisticated tests. For example, CUITe Framework (Coded UI Test enhanced) is a Codeplex project, a thin layer over Coded UI. This breed of tools, by its mature features, make tests readable, maintainable, resilient & robust – a testimony to the fact that our claims about the framework are accurate.   To conclude, I agree with technical COE’s who recommend the above combination of tools and promise several new and incredible features in the near future. Yes! Selecting Coded UI will be the right step forward in the world of QA Automation. While Coded UI tests are not new, it is only in the recent past that test architects are accepting the fact that Coded UI is a great way to resolve issues present in general test tools. If you started off reading this blog with reservations about the possibility of using Coded UI to automate applications and enhance efficiency, the 5 reasons listed above should establish a strong case for Coded UI in QA Automation.

Relational Models – Where Is The Bottleneck?

There are several reasons for the downfall of relational databases and before I start off with the various aspects of NoSQL designs, I would like to highlight a few factors which have made relational models a misfit for global applications today. Manual Intervention:Relational databases were designed in an era when human workforce was cheaper than technology. Thus, relational models have a lot of gaps.  For example, data distribution, sharding, partitioning and similar functions are mostly managed through human intervention. Expensive Vertical Scalability:Relational model designs rely heavily on the underlying hardware’s capacity and quality for performance. They were never designed for clustered storage and operation. Thus, to upgrade a relational database’s capacity it has been necessary to upgrade server hardware which increases costs substantially. REDO and UNDO logs:Persistent REDO logs slow down the write performance as every write operation is recorded in REDO logs. UNDO logs are also updated for each transaction, slowing down the system considerably. Transaction Support Through Two-Phase Commit:Two-phase-commit transactions (used to ensure consistency) are known to degrade performance. Rigid Schema Design:RDBMS architecture requires fixed schema designs. All tables and columns need to be pre-defined along with the data type and length constraints. Most of today’s applications generate a lot of unstructured/semi structured data.   What is required today is a flexible schema free architecture for storing such data. REDO and UNDO logs are used by relational databases to ensure ACID compliance of transactions. Getting rid of these persistent logs would be a huge performance booster but that would make them ACID non-compliant. Considerable design changes are needed to make the relational databases horizontally scalable and schema free. We can therefore conclude that a highly efficient and scalable database must have the following characteristics: • It must be designed to work in distributed architecture,• It must be horizontally scalable,• It must have memory based caching mechanism,  and,• It must not make use of REDO and UNDO logs. (However, one must remember the fact that getting rid of REDO and UNDO logs will improve performance but such databases may not be ACID compliant and may not support transactions). NoSQL databases offer all the advantages mentioned above and they offer the right fit for most applications. Obviously they are here to stay! Do you agree or have you come across some hitches with NoSQL which makes you believe that there are other, better databases available today?

Mobile UI Automation Testing Tools

UI automation testing of web applications has seen a large number of good innovations in recent years but the same cannot be said for the mobile automation segment which is still in a nascent stage of development.  This segment suffers from platform fragmentation and limited number of automation tools, all of which are probably responsible for its slow growth. Thankfully, at least in the recent past, we are seeing some tools emerging for UI automation of mobile devices, but each come with their own pros and cons. In this blog, I have tried to compare some of the popular open source mobile UI automation tools against various set factors to help developers assess the suitability of such tools to meet their requirements. These tools mainly target the iOS and Android platforms. Even though it seems, at present, that  we cannot automate every feature of an app, we can still benefit from mobile automation testing… Some of the key advantages are: •    Continuous integration testing •    Consistent and repeatable testing process •    Improved regression tests •    Parallelize testing on multiple devices •    Improved coverage in shorter time – more tests can be run in less time. •    Better resources utilization – 24/7 operation •    Manual testing of the features that cannot be automated •    Simple reproduction of found defects •    Improved testing efficiency Platform specific automation tools (for example, performance, analysis and testing) are provided by phone OS vendors, i.e. iOS provides Instruments Tool and Android does the same with the Monkeyrunner Tool. But we are looking for a unified solution, which can allow us to code once and then test on multiple platforms. Also these tools should ideally overcome challenges, like, not providing the record and play options, or the need to learn multiple languages to write scripts etc. Here is the comparison of mobile UI automation tools: Hence, in conclusion, after examining all the tools that are out there, I found both Appium and MonkeyTalk measure up quite well but I believe the best tool out there is Appium, since it provides better control to edit recorded scripts.

3 Reasons to Provide Mobile Experience to In-store Customers

How can Mobile solutions help In-store Banking? Mobile experts have been trying to find answers to these kind of questions to help them grow their business. Though this may be applicable to other retail businesses, in this blog, we are keeping our focus on Financial Services entities alone. Going ‘Mobile-Friendly’ is the trend in this space too, and it is challenging the traditional business model, as expected. The traditional business model (‘In-store’) refers to a brick and mortar concept on which many Financial Services companies have chosen to build their fortune. However, with the recent changes in the world of technology, brick and mortar is increasingly giving way to newer means and customers are rarely visiting the branches for all their financial needs. With the increased mobile-savvy customer, new Mobile applications are paving speedy inroads into the brick and mortar businesses of the Financial empire. Many organizations are at crossroads and they are trying the reinvention route to make themselves relevant in today’s world in the hopes of reducing the exposure to the stores and concentrating on the growth of their online business. Hence, in this tough environment, the growth of the ‘Mobile Model’ has become like a direct competitor to the in-store business model, but, surprisingly, there are ways going mobile can help instead of being a hindrance. 1.    Operational efficiency Many In-stores that sell financial products like Loans, securities etc., have this challenge of scaling up during the peak hours as their infrastructure is usually very limited. Queues are not an uncommon sight at these times.  We have noticed that use of Mobile apps can bring in operational efficiency in this regard. 2.    Service time reduced Customers need to fill in a lot of details in multiple forms. During peak hours customer dissatisfaction can be eliminated by allowing them to fill the details of their name, address and copy of their id proof details using their cell phones. This helps reduce the time to service and helps in faster transactions. E.g., this concept is similar to the online-check-in that is done in airports. Another popular example is Starbucks, customers who are waiting in line can pre-order their coffee and then collect. 3.    Drive in-store sales Another unique challenge increasing the footfall into the store. Thankfully, Mobility can help here too: Location based messaging Many Stores uses a Geo-tagged banner ad and location-based SMS. Marketers can use SMS based messaging system to broadcast their campaign to reach the targeted customers. Location based search Customers are increasingly using location-based mobile search to identify nearby stores. This becomes paramount to advertise in-store locations, details and offers on the digital media. ROI for the Mobile In-store customers Calculating the ROI for the Mobile In-store customers is challenging. However, companies started cracking this puzzle when they started reliably predicting the investment needed in Mobile to drive the in-store customers to predict the ROI. For e.g., in one of the Google-Adidas case studies, Adidas was able to track statistically the customers who visited their website/online ad through their cell phones and hence track the revenue generation. Mobile database Collecting the Mobile numbers of potential customers and encouraging push notifications to Apps helps in knowing the customers and build profiles as an incentive. Analytics Collecting Mobile-specific data from in-store customers like Mobile usage, etc., which will help formulate future mobile strategies. Going SoLoMo Having an optimal strategy to integrate Social, Location in Mobile (SoLoMo) to target the customers helps reduce the silos between offline and online marketing. In conclusion, any help offered in this blog is solely based on my experiences with numerous financial services clients. Hope you are able to get your Mobile strategy off the ground and suitable predict future trends that may come in use for your business. FAQs – Tavant Solutions How does Tavant enable mobile lending experiences for retail store customers?Tavant provides mobile-optimized lending platforms that integrate seamlessly with retail point-of-sale systems, enabling instant credit applications, real-time approvals, and digital loan processing at the store level. Their technology allows customers to complete lending applications on mobile devices while shopping, creating seamless purchase-to-financing experiences. What mobile lending capabilities does Tavant offer for retail store integration?Tavant offers mobile-responsive applications, QR code integration for instant access, offline capability for areas with poor connectivity, integration with store inventory systems, mobile document capture, and real-time decision engines that work within retail environments to provide immediate financing options. Why is mobile lending important for retail stores?Mobile lending is important for retail stores because it increases sales conversion rates, enables larger purchase amounts, provides instant financing options, improves customer satisfaction, reduces abandoned purchases due to financing constraints, and creates competitive advantages over stores without mobile lending options. How does mobile lending work in retail stores?Mobile lending in retail stores works through integrated point-of-sale systems that offer financing options during checkout, mobile apps that customers can download for instant applications, QR codes that link to lending applications, and tablets or mobile devices that store associates use to help customers apply for financing. What are the benefits of mobile lending for store customers?Mobile lending benefits for store customers include instant financing decisions, convenient application processes, ability to complete purchases immediately, access to competitive rates, simplified documentation requirements, and the flexibility to shop and apply for financing simultaneously. How does Tavant implement AI-based quality engineering for lending systems?Tavant uses AI-powered testing automation, predictive quality analytics, and intelligent defect detection to ensure lending system reliability. Their quality engineering approach includes automated test case generation, real-time performance monitoring, and machine learning algorithms that identify potential issues before they impact system performance or customer experience. What advantages does Tavant AI-based quality engineering provide?Tavant AI-based quality engineering delivers faster testing cycles, higher defect detection rates, predictive maintenance capabilities, and continuous quality improvement. Their approach reduces manual testing time by 70%, improves system reliability, and ensures lending platforms maintain optimal performance under varying load conditions. What is AI-based quality engineering?AI-based quality engineering uses artificial intelligence and machine learning to automate testing processes, predict system failures, optimize test

Warranty Analytics – Integrating Value with Returns

The global economic downturn is impacting organizations across all industries and businesses are struggling to improve bottom line results. The automobile industry is no different. Managing costs, tracking fraudulent claims, and improving customer satisfaction are some of the major concerns for organizations in the warranty management environment. Close to 70% of the warranty expenses dwindle due to repetitive failure in the performance of parts. Identifying the root cause of such failures is the one of the biggest challenges in the industry today. How can organizations thrive in such an environment? Warranty Analytics is the only solution. Making correct use of the opportunity requires robust warranty analytics that can integrate raw data into actionable plans to accomplish the desired results. Successful organizations leverage that as the key to increasing revenue and optimizing customer success. Analytics is the application of statistical tools that convert raw data into business solutions. It incorporates a predictive technique to review future trends and improvise current processes. Insights gained through analytics add value to all the key spectrums of a business, including customer management, product quality, and process enhancement. Analytics solutions give a microscopic view into key business functions along with statistical features, predictive analyses, and forecasts for optimal business decisions. Analytics helps assess gaps and analyze the root cause of individual elements that hamper overall processes. Detecting the root cause enables companies to determine the best course of action and save on costs through early detection of failures. It further helps organizations improve resource allocation and realign focus on core strategic functions. Business Case Study A leading manufacturing unit with operations in 26 countries specializes in products and services across industries. It offers services such as protecting food and perishables, securing homes, and enhancing industrial efficiency and productivity. Each business function has multiple units with varied requirements. Those units used ad hoc retrieval methods that resulted in inefficiency in tracking failure patterns and measuring ROI from individual units. The organization had also branched out lately to wider geographical boundaries. The lack of inventory management and a centralized system proved a potential business threat. The organization needed a comprehensive warranty system that maximizes the utility of data for optimal business growth. The Solution A centralized web-based warranty solution was integrated to obtain a clearer perspective on parts failures, fraudulent claim processing, and better customer management. The software-based analytics system helped in providing operational feedback, turnaround time, parts performance, and determining the warranty cost per product. Some of the benefits experienced through warranty analytics: • Improved customer loyalty, product quality & brand image • Early detection of parts failures with an increase in bottom line results • Effective management of claims processing and warranty reserves • Predictive analysis to increase financial performance • Warranty cost management to understand and manage expenses • Smoother transactions between units and greater information flow • Faster and easier settlement of warranty claims Those benefits when enjoyed together can bring an organization exceptional advantage. Much like the manufacturing organization in the case study, Warranty Analytics is being used by more and more businesses that aim to thrive with higher returns and greater customer success.

Tavant Warranty Systems – One for All

Among the majority of Warranty Management Systems across industries, each one is becoming highly customized to meet only one business functionality. Although customizing a system for a product or line of business helps, the conventional approach can result in multiple instances of the same application with only minor change in operation. That does not help the system attain a just price in terms of the value it adds to an organization. So what is the future then? Is it really possible to have a system that: •    Can be tailored to business needs •    Is cost-effective •    Enables synergy between business ideas & expertise •    Is a proven success •    Will allow me to stop experimenting with my business continuously The short answer is YES. But how? Here at Tavant, we have built, nourished and enhanced our Warranty Management product offering ‘TWMS’– ‘Tavant Warranty Management System’ for the past 12 years. We have helped our customers, whose businesses started off with independent instances, to collaborate and merge into a single instance. The result that we have now is a powerful global system that houses all businesses, irrespective of their linguistic and zonal differences. It is meeting their specific demands and requirements, without costing them much for the value it is bringing, day in and day out, by easing the operations at work. It is natural that you might be wondering as to how that is possible when all businesses never have the same requirements. Across businesses, differences at various levels exist in terms of operation. But are businesses using TWMS compromising on system functionality or operation mode? No. We understand that every business is unique and has its own distinct operation. So we provide options in functionality. Business administrators have the option to tweak their systems as understood fit. More importantly, they are empowered to alter the course of system operations for their businesses through the system itself. If at any point a change made is found undesirable, it could be reverted. Alright, I am beginning to like this! It is pretty similar to what we want, but how does it stand out in the crowd when every other system does exactly that? It does stand out. It is a ‘Multiple Business on a Single Instance’ system. What’s more? You don’t pay for the whole system. You really needn’t write that big fat check to own the system. The cost would be shared between the businesses which are part of it. The council, comprising leaders who run the business, meet periodically to share their ideas and concepts on improving and automating the system further. So it is not just the cost that is shared. With it comes the synergy of expertise and knowledge from multiple experts, which I believe, is truly priceless. Finally, I will wind up leaving you to a choice: Lose ground by competing or share an advantage by collaborating. Let’s learn to work together when it’s the right thing to do!

AngularJS; Easy, Clear and Succinct!

I recently had an excellent opportunity to work on AngularJS as a part of an extended project responsibility. I was overwhelmed by the ease associated with this structural framework for dynamic web apps. Here is why I believe one should start exploring and using AngularJS: Build templates directly in HTML One can build properly structured web applications using AngularJS expressions, directives, filters and data binding. Expressions can be executed within the HTML pages. For example: <div>1+1 = {{1+1}}</div> Will result in 1+1 = 2 Directives – are used for structuring a page. For example: ng-repeat repeats the creation of new set of elements in the dom for each element in a collection. <div> <div data-ng-repeat=”users in user”> <h2 >{{user.name}}</h2> <h3>{{user.desc}}</h3> </div> </div> Filters – changes the display of data in the page. For example: We can place the name in upper case with {{user.name | uppercase}} Data Binding – Provides automatic synchronization of data between the model and view components, thereby helps in binding data in scope and content of view. We can also perform bidirectional data binding where change in content of view also makes real time updates to data in scope for example: <div> <div data-ng-repeat=”users in user”> <h2 >{{user.name}}</h2> <h3>{{user.desc}}</h3> Edit Description: <br /> <textarea rows = “5” col=”20” data-ng-model = “user.desc”> </div> </div> Easy implementation of REST REST has become a standard for communication between servers and clients. I discovered that with Angular JS, one line of code allows us to ‘talk’ to the server and revert with data for the web page. Write less code With AngularJS, as shown in the code above, the view can be defined within HTML. You can also use filters to change the data at view level i.e., within HTML without touching the controllers. It also removes the necessity to write getters/setters in data models. Always be unit test ready Though this aspect bears to undergo more research and analysis, to explore it in more detail, there is literature that proves that AngularJS has a mock HTTP provider that provides fake server responses to the controller instead of the person creating test pages that invoke a component and interact with it for testing purposes. In conclusion,  AngularJS has capabilities that allow you to express an application’s components clearly and succinctly. It makes the development and testing implementations easy and  pleasant. It is certain that you cannot become an expert instantly but, my experience articulates it as easy to develop and all it requires is familiarity with a Model–View–Controller (MVC). So go head and start exploring.  

How We Created a Scoring Engine Using an Expression Rule Engine.

Scoring is the core decision-making module in a lending process. It identifies the creditworthiness of a customer based on credit files & other related information. Credit file information is usually provided by an independent credit bureau. The algorithm for finding a score varies with the type of lending & the lending institution. While developing lending software for a leading mortgage institution, we faced a situation wherein the final score was based on multiple sets of information known as ‘scoring variables’. The ‘variables’ set included basic customer parameters like ‘Age’, ‘Income’ & information received from independent Credit bureaus. These variables were of different types; such as a numeric field like ‘Age’ or pre-defined text like ‘Pay-Frequency’. The first decision we had to make was to either go with a third party business engine that handles scoring or to create one of our own. There are vast variety of business engines available in the market. But our focus was on maintainability, ability to handle complex business rules & cost. We decided to create our own scoring engine. For building a scoring engine, we first looked at the Windows Workflow Foundation (WF). WF is flexible enough for handling complex sets of algorithms and it gives a nice user interface for the developer implementing it. But with the response time, we could manage with WF and the data-driven approach which we were looking for, WF was not a practical option for the requirements of our scoring algorithm. ‘Expressions Trees’ in .Net represent codes in a tree-data structure. Each node is some kind of expressions like a lambda expression or a method call. Please note that Expression Trees widely use DLR (Dynamic Language Runtime). It fits nicely into the scoring engine because the codes written as expressions can be compiled & run dynamically. This enables dynamic changes to the algorithm & its variable usage. This would mean that the participating code can be fed into the scoring engine from outside & can be stored in Databases, xml or other data structures. Since scoring engines involve a lot of binary expressions (like Age > 60 => Do something) Expression Trees are very helpful. Expression Trees are created using the System.Linq.Expressions namespace. A simple illustration of constant integer value comparison: static void Main(string[] args) { SpaceSplitParseNumericExpression(“8 GreaterThan 4”);//Returns true (8 > 4) SpaceSplitParseNumericExpression(“5 LessThan 1”);  //Returns false (5 < 1) Console.WriteLine(“Press any key to exit”); Console.Read(); } public static void SpaceSplitParseNumericExpression(string expression) { string[] tokens = expression.Split(‘ ‘); bool output = Expression.Lambda<Func<bool>>( Expression.MakeBinary( (ExpressionType)Enum.Parse(typeof(ExpressionType), tokens[1]), Expression.Constant(Convert.ToInt32(tokens[0])), Expression.Constant(Convert.ToInt32(tokens[2]))) ).Compile()(); Console.WriteLine(string.Format(“Expression ‘{0}’, returns: {1}”, expression, output)); } For an actual scoring engine, Parameter Expressions are used along with Constant Expressions, to make this logic more dynamic & flexible. These parameters & constants can then be stored into a DB or any other data source which can be read by the engine on a need-basis. Thus we ended up creating a scoring engine which does not have hardcoded numbers in the code and is compact, fast & easily maintainable.   < 1) Console.WriteLine(“Press any key to exit”); Console.Read(); } public static void SpaceSplitParseNumericExpression(string expression) { string[] tokens = expression.Split(‘ ‘); bool output = Expression.Lambda( Expression.MakeBinary( (ExpressionType)Enum.Parse(typeof(ExpressionType), tokens[1]), Expression.Constant(Convert.ToInt32(tokens[0])), Expression.Constant(Convert.ToInt32(tokens[2]))) ).Compile()(); Console.WriteLine(string.Format(“Expression ‘{0}’, returns: {1}”, expression, output)); } For an actual scoring engine, Parameter Expressions are used along with Constant Expressions, to make this logic more dynamic & flexible. These parameters & constants can then be stored into a DB or any other data source which can be read by the engine on a need-basis. Thus we ended up creating a scoring engine which does not have hardcoded numbers in the code and is compact, fast & easily maintainable.

Tavant at the Warranty Chain Management Conference 2014, San Diego

The 3-day event is a global platform of ideas, challenges, opportunities, and guaranteed solutions related to Warranty management. The 10th edition of Warranty Chain Management Conference is intended to appeal a diverse range of organizations. Executives, managers, senior contributors, CEOs, strategic leaders, and domain experts from service life-cycle management will share common warranty issues and solutions to aid and foster warranty management as a discipline. As warranty management continues to gain momentum, close to 500 organizations across diverse industries have announced participation at the global event in San Diego. Taking yet another leap in thought leadership, Tavant is a key sponsor of this event, where it will showcase its flexible and end-to-end Warranty solution that continues to enable manufacturing firms transform their warranty management. The subject matter experts from Tavant, along with some of its global customers, will be speaking on topics related to Building Material, Automobile Warranties and Rules-based Warranty Systems. Tavant Warranty — A Global Preference Global corporations such as Ingersoll Rand, Doosan Infracore, Federal Signal and many others have deployed our highly flexible Warranty Management Solutions to increase their aftermarket revenues with optimized extended warranties and service contracts. Our web-based, real-time, full-lifecycle, and flexible pricing model-based solution help reduce warranty spends, eliminate fraudulent claims and reduce claim processing time substantially. The event hosts a variety of presentations, workshops, panel discussions and demos. This edition is all set to get bigger and better. Don’t let this opportunity slip by! Team Tavant hopes to see you there. About the Presenters from Tavant As key presenters of the event, Tavant experts will be looking forward share valuable insights, best practices and success stories on Warranty Management. Tavant Speakers at the conference include: R. Pinto, Head of Service Operations M. Devarapalli, Senior Program Manager, Service Operations and R. Lohan, Product Manager, Service Operations Visit http://lf1.me/Vvb/WCM2014 to learn more about our participation in the event.

Web Services Testing: What do we need to test ?

It is usually stress-free for a tester to migrate from one technology to another, but at times it is more difficult to move from one methodology to another. Jumping from a custom application to Commercial Off-The-Shelf (COTs) is still an easy transition. The tester has a better idea on which parts they need to focus on or which modules are more susceptible than others. With the dispersed architecture of loosely coupled systems which may be technically apart but frequently need to communicate with each other in terms of data requirements is where web services comes into the picture. A web service provides a simple interface for communication for these systems using a typical data transfer mechanism. Ladders in Web service testing: To conclude what is expected from a Web service with respect to business requirements To  gather and understand the requirements, and the data transfer standards To design test cases keeping business requirements in mind, the more data scenarios you have, healthier the quality of deliverable It is a thornier task to test complete end to end business flows with all the possible data scenarios. The trick is to have an automated tool which can shorten the testing of web services like Optimyz, SOAP UI etc.   Web Services: What all do we need to test?  Functionality: We need to look for the following in the midst of functional testing Specification Review (SR) Test Case Development (TCD) Test Execution, the examination of requests & responses   Performance: Testing web services performance may be complicated. To avoid this, following a simple rule of clearly mentioning the thresholds upfront, solves problems. Another key is to know the performance requirements in the most accurate manner. For e.g. A good requirement: This service has been identified as serving 50,000 concurrent users with 10 second average response time A bad requirement: This service should serve > 4000 concurrent users, and the response should be fast Security: Web Services are wide-open in a network. This element opens up a host of vulnerabilities, such as penetration, Denial-of-Service (DOS) attacks, and great volumes of spam data, etc. Distinctive security policies have to be imposed at the network level to create a sound Service Oriented Architecture (SOA). There are certain security policies which are enforced during data transfer, and user tokens or certificates are common sights where data is protected with a password. Precise test cases aimed at directing these policies need to be designed to completely test the Web service security Compliance: Compliance testing is required to ensure that: Web services meet certain specified standards Authorize SOAP request/response messages Authenticate WSDL definitions   When testers take up web services it tosses many challenges at them, it is still very important to know what they need to do, rather than doing it first to learn costly lessons later.

Payment Tokenization to Reduce PCI DSS Scope

Don’t want the risk of handling or storing sensitive payment data on hosted servers? Want to achieve and maintain payment security (Payment Card Industry (PCI)) certification faster and easier? If these are your concerns, then Payment Tokenization is the way to go. It is a great way to reduce the scope of PCI Data Security Standard (DSS). Eliminating the payment data from your network is the only way to ensure that your customers’ sensitive personal information is not compromised during a security breach. Tokenization is the replacement of sensitive data with a unique identifier that cannot be mathematically reversed. In a transactional environment, tokens take the place of sensitive credit card data. Typically, the token will retain the last four digits of the card as a means of accurately matching the token to the payment card owner. The remaining numbers are generated using proprietary tokenization algorithms. How It Works To make a purchase on a website, the customers will enter their payment card information into the designated payment fields on the order page. When the customer submits the form, the card data is immediately transmitted directly to Card processors like CyberSource for storing, processing, and token generation. The card data never has to get stored in your environment even though you need the card for recurring processing. There are 2 main flavors of tokenization namely Silent Order POST (SOP) and Hosted Order Page (HOP)). Card processors return the result by substituting the PAN data with a uniquely generated token, which one can call subscription ID. You store the token in your database for future transactions or chargeback resolution on that account. For your recurring transactions, you just have to pass that token or subscription ID to the card processor. Customer service representatives can easily verify customers, as the custom token will retain the last four digits of the original PAN.   Benefits of Tokenization Reduces PCI DSS Scope Renders payment card data meaningless to hackers Chargeback and payment reconciliation can take place without handling payment data Not mathematically reversible The format fits legacy payment card data fields Integrates with Account Updater to automatically update payment data for fewer failures   The interesting part is that, whether you are starting with an e-commerce system of your own or an already existing one, you can easily use or switch to tokenization. If you are starting new, you will get all your cards tokenized but, if you already have cards, you can get them ‘ONE time tokenized’ using some batch process and then you will be able to switch to tokenization for all future orders. In the continuing next part of the series, we will look more deeply into the Flavors of Tokenization.

iOS 7: Do you really ‘need’ it? Or just ‘want’ it?

The iOS 7 release took a toll on Internet traffic, with Akamai’s Real-time Web Monitor reporting Internet traffic at 112% above normal on September 18th. The probable cause – iOS 7 upgrade. At one point, the iOS 7 download overtook Netflix’s traffic. Thanks to transparent caching or some other intelligent feature, the Internet didn’t really break down, but for Conan O’Brien, it did! Now for the real question – whether to upgrade to iOS 7 now or wait. The one and only answer is “Yes”,  in whichever way you look at it. But there is much more to it. For an average user of the iPhone, it’s the drastically new user-interface that is driving the upgrade. But for more savvy users, it’s beyond the new aesthetics. Overall, three things stand out with the iOS 7 – iBeacon, Finger Print Scanner (hardware dependent), and AirDrop. Will all these make the little things in everyday life simpler? Only time will tell. Though all Apple users will upgrade to iOS 7, regardless of the model or device, the real intriguing part is the all-important debate – the one between Needs and Wants. Do you need iOS 7 or do you want it? For the vast majority,  the answer is they want iOS 7 but don’t actually need it. The reason is also quite obvious –  iOS 7 offers many remarkable features and capabilities, but the World is not ready yet (I can be wrong!). The AirDrop feature is quite cool as many iPhone users have been struggling with file sharing for a long time. But the other capabilities might take time for adoption and real use. Some vendors have started to offer Bluetooth LE-enabled beacons and payment services to make life simpler. But how many such examples do we see today – only a handful. Having said that, Apple has done something incredible that no other company has done yet – making hardware, software and services come together like never before. The possibilities are infinite but will take time for larger adoption and start to make those “Little Everyday Things” much simpler. So do you really need to upgrade to iOS 7 now? Go for it to experience the shape of things to come, but for actual use you might have to wait.\

Tavant Warranty – Driving customer success through business process improvement

I have been working with manufacturers to improve their warranty chain management for close to 9 years now. I am among the few in Tavant, who has worked only with manufacturing clients and did not get the opportunity of working on more glitzy-looking solutions for media & entertainment clients.  I don’t regret it. My experience with manufacturing companies has actually improved my perception of manufacturing process and makes me appreciate the true value add of IT. When I visit those factory floors (thanks to our amazing customers), I see hard working  employees trying their best to make world class products in a cost efficient manner.  I see myself as a partner in their effort and always try my best to help them become more efficient and effective, of course with the help from my team at Tavant. Tavant is a specialized Software services company. Our mission is to drive customer success through impactful solutions. Warranty management platform is one such solution. When it was created back in 2006/2007, our main focus was on improving and optimizing customer’s warranty operations. Over the last few years, this platform has been extended to include solutions such as service contracts management, mobile application, and aftermarket business intelligence, dynamic extended warranty pricing, and closed loop supplier recovery. There are numerous examples of how we have used the latest technology for business process improvement as part of warranty solution. Tavant developed a mobile application to automate pre-delivery inspection process for one of our customers. Through minor customization, we were able to provide a single solution to manage warranty and technical service operations for another customer.  We recently started using Google translator to translate claims filed in different languages to English so that internal departments such as quality, engineering, manufacturing, and external suppliers can understand the feedback from field. This helped the firm improve its supplier recovery and add more to bottom line for the company in current tough economic environment. Our solution has helped firms by getting more output from same resources. People who were responsible for warranty, are now taking up more responsibilities in the organization. Structured and well documented feedback has also helped engineering departments to design better products meeting customer requirements. I am proud of being part of warranty solutions team at Tavant, which in its own way is helping companies become more competitive and successful.

Applying best practices to e-Commerce Testing

In my last blog post titled: Tested Tips for Successful eCommerce Testing I had walked you through the expectations of an online customer, and the most critical areas of e-Commerce testing. In this blog post, I will cover a few do’s and don’ts that should be kept in mind while commencing testing efforts. These tried and trusted practices have worked more businesses that count on their online presence. Do not twitch with the home page: The exchanges do not take place on the home page. Most of the visits are not for the home page. Test downstream in the conversion path (also known as checkout) because guests that get that far are more likely to convert than ‘anyone else in the world’. Twitch where the money is, and work backwards. Do not begin with your poorest execution page: This is another practice that has become popular. You might want to advance your bad pages; but even if you get a 10% increase, it’s only an increase on a low traffic or low value page. So it’s a no-brainer that 10% of almost nothing is still almost nothing. If you optimize a well-performing page, the conversion is useful and lucrative. Choose pages with the most affluent traffic: If you’re spending top money to attract new guests to a product or category, you might want to make the most of that by minimizing bounce rates and maximizing devoted purchases, cross sell and up sell. Run tests on associate landing pages: Unlike paid search traffic, you typically don’t pay for visits referred by associates; but associates are more impressed by online merchants that test. You may even give them custom landing pages and allow your associates to provide some input as they are chief marketers themselves. Do Transaction Testing on all A-class browsers: This is essential to an e-Business application. The software used by a website has to invoke its various components, and check whether direct and indirect interfaces are working correctly. The information entered by the user should make it to the database in correct ways. When the user calls for information contained in the database, the respective data must be returned. Test your search result and category pages: These pages are often living in shades of your fashionable home page and product pages as well as checkout page, but they are essential for getting guests to the product pages! Don’t forget about category pages that are quite alike, if not equal to, search pages for many sites. Test and test yet again: It is extremely important to test your website and service platform from the viewpoint of a client, in order to ensure that everything runs appropriately. It’s difficult to figure out how many businesses make it evident that they do not test adequately. Do RBT in case of time crunch: The objective of risk-based testing, a.k.a RBT approach, is to test the critical areas of the application that can cause a major failure. This helps in reducing the impact and productivity of a testing strategy. Unlike traditional testing methodology, this approach helps decrease the number of test cases. To sum up: Your site has to be tested, fixed, retested and fully documented. Also, all the applications utilized in the website have to be tested for performance and scalability. The criteria for testing websites are Timeliness, Structural Quality, Content, Accuracy and Consistency, Response Time and Latency, and Performance. Some of the tests that need to be done on a website are Content Checking, Browser Compatibility, Transaction Testing, Configuration Testing, Performance & Scalability, and Security. Web testing is still evolving because web-based software is relatively new compared to other software. Software testing has been around for a long time. However, there are many companies making software for web testing. But the challenge is to choose the one that meets the needs and budget.

Choosing Single or Cross Platform and the Ideal Mobile Development Techniques

As a mobile app developer, sometimes you might have to take a decision on whether to build a native app or hybrid app. If you have the financial resources and time, it is always best to build native apps compatible with all mobile platforms. But there are many key considerations you need to think through, before taking that decision. Single Platform Vs. Cross-Platform Single Platform: If you have decided to build apps on a single platform, then you should keep in mind that they are appropriate for: Targeting specific audiences like iOS or Android users. Internal facing enterprise applications, where you know the platform on which your audience is on.   The advantages include the ease of designing, building and testing apps. App reach limitation and challenges with respect to multiple platform compatibility are some of the disadvantages. Cross-Platform: Cross-Platform mobile development should be the preferred approach if one wants to reach a larger mobile user base. According to the recent IDC’s Smartphone Share Report, building apps for iOS and Android unlocks a major percentage of the mobile market. Mobile app reach extension and smooth functioning of both internal and customer-facing apps are advantages. But these apps take longer to develop and is expensive to build. If the objective is to reach more users, the investment is worth it. Development Techniques: Native, Hybrid or Browser Once you have decided on the platform, there are three development techniques at your disposal––Native, Hybrid and Browser-based apps. Native Apps: Native apps are built using platform-specific SDKs and languages; iOS uses Objective C and Apple APIs, while Android uses Java and Google’s Android APIs. Native apps offer a host of advantages––faster functioning and richer user experience, availability of professional development & testing tools, full access to platform & device capabilities and monetization for developers. But the same also has some disadvantages: They are expensive to build and require separate developers for each platform. Also, each platform demands knowledge of different tools and languages. Lastly, there would be a need to redesign every time, since the code cannot be reused. Hybrid Apps: Hybrid apps run on the devices that are similar to one’s native apps run on. Hybrid apps are written with web technologies such as HTML, CSS & Javascript, and run inside a native container. The native container or shell acts as a proxy that allows Java script to access device APIs (not all of them) and sensors. Hybrid apps have some advantages: It is easy to find software developers with HTML, CSS, Javascript skills; Easy access to many device APIs that are not accessible in Mobile Web Applications; Provision to distribute and monetize via app stores, and Common code base for multiple platforms. The disadvantages include the need for native tools to package and distribute the apps, limited access to device APIs, lack of performance and difficulty of the HTML consortium to catch up with the frequent updates and releases of Apple and Google. Browser Apps: Browser apps/Mobile Web apps/HTML5 apps are built using HTML, CSS and Javascript and run on modern mobile browsers. These are best suited for internal enterprise apps targeting multiple platforms. These apps are least expensive and can be developed faster. Apart from multi-platform compatibility, browser apps are easy to deploy on the mobile device. Also, it is easy to find software developers with HTML, CSS and Javascript skills. Browser apps also have demerits: Even though they run on multiple platforms, they cannot be distributed via mobile app stores. So monetization has to be done separately. What’s more, they offer limited access to native APIs and sensors, and also lacks in smoothness. The possibility of animation is also ruled out. Hence if you are looking for performance, security, monetization, rich user experience and innovations, then the recommendation is to adopt a Native development technique. At the same time, a Hybrid app will enable lower cross-development costs, ease of development and the fastest way to reach the majority of mobile users.

SOA is BAD? A Business Perspective

SOA promises business agility, i.e. SOA is BAD: Business Agility through Decoupling. SOA promises business agility and enables us to expose business processes as services. You can have service as granular as sending an email to end customer and also as macro as Order Processing for an eCommerce application which is composed of smaller services such as user registration, order submission, reporting etc. Decoupling helps for localizing the changes, thereby reducing the cost and effort to implement the change. It allows quicker time to market by: Reuse instead of building from scratch, and Better quality by reusing tried and tested services In addition to cost benefits, another motivation behind moving to Service Oriented Architecture is to connect with other business units, partners and organizations which are using various web services as the only means to connect to the outer world. These are discussed in detail below: Problems targeted by SOA Agility: The Service Oriented Architecture provides agility to the business through shorter turnaround time for implementing a change or in coming up with a new set of functionalities. Companies which are in fast-changing markets, or in markets with fast-changing laws require frequent updates to their existing applications which may be tightly coupled with other services. Rather than implementing the change all over – in SOA, one can simply update the target service, and this will be reflected all the places where the service is used. Reusability of existing components: Organizations can make use of existing services while composing new applications and services. Complex composite services can be structured using simpler, granular services thus making reusability as one of the important means to achieve agility. For example, consider user authentication as a service, this single service can be used while composing complex services such as providing user access to external media and user writing some post/comment on some media site. Connect with customers: Let us take the example of an eCommerce organization, where customers usually get information on their orders by calling up customer support representatives. The same organization can enable tracking of the customer orders using service based solution where the same service can provide information to a customer representatives as well as end customers. This enables reduction of inward traffic to customer service centers and enables less number of customer representatives to service requests. Though these are just some of the benefits of the SOA from a business perspective, the SOA architecture offers far more agility and flexibility for business to overcome many unique application design and implementation. In my next post, we’ll see SOA from architecture perspective and the challenges working with SOA. Till then, stay tuned!

The Why, What and How of Digital Spying

When the unknown secret of a US program called PRISM first became public last month, a lot of people across the world were surprised by the extent to which they are vulnerable to online monitoring by government organizations. Though the ethical nature of this surveillance activity is debatable, one cannot deny the sheer power of BIG DATA in executing this kind of an activity at such a scale. Similar to security establishments in various countries there are another set of organizations that also try to stitch together online information from various sources and identify their ‘target’. These belong to a category of organizations known as Online Advertising Networks and Publishers. The idea to target audiences based on ‘User Onsite Behavior’ and ‘Network Activities’ is not new, but technology advances in the form of Big Data analysis in recent years provides an effective tool to target audiences in real-time. To explain this in simple terms; it means, if a user is seen on any of the news sites, business sites or male fashion sites, a reasonable guess, based on current sterrotypes would be to assume that the user is male. If the same user visits an eCommerce site the acquired information can be used to provide a personalized products view to the user, which increases the chances for online purchase. How does it work? Whenever a user visits certain websites or performs certain activities like clicking on various links, searching with special text, either the Site Publisher or Advertising Network sets certain information in the form of a cookie in the visitor’s browser. In some cases, the Site Publisher also passes the visit details on to a Web Analytics System (Adobe Analytics, Google Analytics etc.) for further analysis. This information is processed using BIG DATA tools and the outcome of the analysis is used to create a unique ‘profile’. The data is further used for defining audience segments. When visitors return to a specific site using the same web browser, those profiles can be used by publishers for personalized content and also by advertisers to position their online ads based on the resolved audience segment. Properly targeted ads and personalized content not only fetches more consumer interest but also helps publishers in charging a premium for these ads over random advertising. Over the last few years when print publishing was showing signs of decline in terms of readership, more and more publishers and advertisers are shifting their focus to the digital world for finding new sources for revenue.Over the last few years when print publishing was showing signs of decline in terms of readership, more and more publishers and advertisers are shifting their focus to the digital world for finding new sources for revenue.Since these technologies provide a way to target their audience, they will continue to engage in snooping to track their prey, whether it is ethical or unethical will always be a matter of debate.

Warranty Management Embedded With Business Intelligence and Analytics: A Boon for Warranty Stakeholders!

Today’s leading Software Consumers are becoming increasingly keen on Business Intelligence and Analytical tools that can help transform the raw data into actionable insights.  Data and insights are two very different things when it comes to Warranty Service or rather, any service. Companies do collect huge volumes of Warranty data, but they fail to convert it into insightful information that would allow timely and proactive actions from the manufacturer. With proper insights, potential Warranty problems can be handled at an early stage thereby resulting in reduced the warranty costs, improved product quality and higher customer satisfaction. With the presence of a warranty management solution, manufacturers are able to run their warranty operations but are unable to utilize the informative data available in hand because of the absence of analytics. This is where the Business Intelligence and Analytics step in, and take Warranty Management operations to the next level. Embedded BI or Analytics refer to capabilities such as enhanced Reporting, Dashboards, Data Discovery, or Data Management capabilities included as an integrated module or an extension of existing Software. With embedded Business Intelligence and Analytics, the stakeholders will get a holistic view of the entire Warranty Lifecycle. Some of the potential problems and cost drivers in warranty like fraudulent/duplicate claims, Rule Processing Engine, Failures, Field Modifications, and Supplier Management are better tackled by deploying Analytics. Studying the analytical data and having a snapshot of key Performance Indicators on these would allow the stakeholders to solve the problem with a preventive approach rather than a reactive one.  For instance, by digging or zeroing down to the list of failures that occur most frequently (Viz. Top 5 Failure Areas), would help the engineering team to solve issues during the early stages at the manufacturing plant, hence decreasing the warranty cost incurred. Warranty Engineers may refine the Processing Rules by analyzing the Top 10 Rule Failures leading to further decrease in manual reviews. With Analytics, the stakeholder will have better information on fraudulent/duplicate claims. Business Intelligence and enhanced Analytics on Warranties provide insights to functions across the organization like Sales, Marketing, Design and Engineering. ‘Patterns and Trends’, the striking feature of Analytics guides cross-functional departments in an organization in better forecasting, accruals and reserves. We can say that it not only enables manufacturers to take preventive measures but also helps in predicting risks and failures by analyzing history and trends. The Strength of Intelligent Warranty Data Warranty Management Data and its logical analysis play a crucial role across various business functions of an organization. It helps to understand product performance in the field, top warranty cost drivers, field service issues, as well as the serviceability and reliability of products. In the absence of a central Warranty Business Intelligence system, it becomes difficult to analyze the product effectively. Hence, improvement initiatives cannot be triggered on time. The figure above shows how the best in class organizations have been effectively using warranty data to improve cross-functional performances, but some of the manufacturing brands still have a long way to go in effectively measuring and analyzing warranty data and converting it into actionable insight World-class organizations are addressing the risks by implementing Business Intelligence and Data Warehousing systems. The takeaway is that an investment in Warranty Management embedded with Business Intelligence systems is a long-term beneficial investment in the form of Quality Products, Better Field Service and greater customer satisfaction.

When Reaching the End Means More Than Being First.

Over the last 5 years or so, a lot has happened in the IT industry that has disrupted the very way we use hardware and software, and to an extent, it has given us alternate ways to consume information. Every year, analyst firms and IT service providers look for technologies or trends that will drive the next wave of change (or disruption). This year too, things are no different–with some overlapping trends in the numerous predictions and insights that are coming our way. Overall, two areas stand out in having a profound impact on the way technology will be strategized and applied to consumers and the enterprise. First comes Mobility, and then comes the Disruptive Cloud. The interesting point to note is that both these forces are mutually reinforcing the other as they evolve. All the leading IT solution and service providers today, have increased their focus on the mobility segment– which includes the mobile, notebook, and everything in between. The Cloud, on the other hand, has enabled the realization of the unthinkable–to enable processing of data on devices, no matter what operating system or hardware it is running on. So anybody would agree with me when I say that the Cloud now controls the digital lives of people and extends anywhere from computing to communicating. If the recent product and service launches are to be analyzed, the signals are clear that the primary goal of IT solution providers is to create a powerful ecosystem from both the developer and consumer perspectives. The release of windows 8 is a perfect example in this regard,  as it is in line with the strategy being adopted by the big players in the IT domain. In a nutshell, Windows 8 is the older version in a new bottle with some features taken to the visual backend and up come those Apps! Apps that have evolved with new usability features and behavior. Though they require a multi-channel integration and interaction, the end product is so advanced that the experience can be customized to where a person is located and what they are doing. This is the same strategy being followed by Google, though not in very obvious terms, with Apps being designed for mobile and bigger devices. However, even though both the strategies might be the same in a way, the business sense is entirely different. Both Microsoft and Google are at the two ends of the OS dominance. One rules the desktop space while the other is at the mobile and both want more. For Microsoft it is about leveraging their desktop OS superiority to the not so successful mobile space while for Google there seems to be a simpler challenge – develop more for mobile and then leverage the same for larger devices. But hey, why are we not taking Apple into consideration here? Because, looks like they ‘bit the fruit’ first and did not feel anything! This strategy worked out perfectly for Apple with their scalable OS that works great on all devices. Though mobile and cloud came later, they were ready to embrace the change and leverage it to their strengths, even though they metaphorically, ‘arrived late to the party’! If you watched the WWDC2013 keynote, all this would make perfect sense as Apple lays out the plan for the next 10 years. The fight on the other hand for Microsoft and Google is not about reaching first or about dominance but it is about who reaches the other end first. And what will help them achieve this – mobility and cloud. Either way, ‘biting the fruit like Apple’ second causes lesser pain, doesn’t it?

Performance Monitoring for Best-in-Breed Mobile Apps

My smartphone is now an indispensable part of me! I reckon any technology enthusiast or a business decision maker would say the same. We all know that the smartphone space has seen exponential growth in the last few years. Companies today are increasingly relying on the mobile medium; not only as a powerful way to engage customers, but also as a tool to address their day-to-day business needs. Why do you feel this paradigm shift happened? Well, I guess it’s a no-brainer for the matured mobile user! It’s just the realization that mobile apps can add significant value to the success of a business. Hence, everyone (from individuals to organizations) is striving to deliver more value by providing new and innovative mobile apps. As mobile applications gain importance, application performance monitoring and its related tools are also passing through a phase of transformation. Today’s mobile teams are realizing that they can’t manage mobile apps with traditional web technologies. Mobile application performance monitoring tools are providing greater thrust towards application stability in the mobile app paradigm. Application performance monitoring/crash analytics delivers a capability to provide insights about mobile apps to investigate concerns such as: >    Is the OS update causing a problem? >    Is the device or network causing problems? >    Is the OS version and hardware causing issues? >    App health and availability – Do they matter? >    App improvement through performance boosting and code testing This allows the application performance monitoring tools to enhance quality of mobile applications by providing detailed analysis on: >    Crash Data >    Crash Trends >    Lines of code causing crash >    MAU / DAU Tracking >    Handled Exceptions >    OS version & device using app >    Error Monitoring – Diagnostics and Live Graphs >    Network Monitoring –  Performance of outside cloud services and network conditions Today’s application providers are integrating application performance monitoring tools to capture and analyze the application performance footprints more efficiently. Mobile application development that leverages application performance monitoring/crash analytics tools is now enabling the development of improved and stable apps. This impact is being realized by the increase in revenue through the apps. The notable names, as per VisionMobile 2013, in the Application Performance Monitoring space include BugSense, Crittercism and TestFlight. All these companies have their own USPs, and provide their services across varied mobile platforms. The various mobile platforms supported by each of these vendors are listed below. Though the mobile performance monitoring space is growing fast, the domain is still in its early stages of development. Many new ventures are coming up with their innovative solutions. Companies such as Google and Twitter are investing in these ventures to provide thrust to these much-needed application-monitoring solutions. The first half of 2013 has seen a lot of venture funding and acquisitions; a few are listed below: >    Crittercism received $12M from Google ventures, and more to help mobile developers monitor app, network performance – March-2013 >    Twitter scooped up its crash-reporting competitor Crashlytics in January 2013 >    Newcomers like Torbit and Bugsnag have been able to find traction almost from the get-go – 2013 >    Compuware launched free native mobile application performance monitoring service – April 2013 These recent developments in the mobile app space clearly indicate a major thrust in the field of mobile application performance monitoring tools. Though only a few companies have moved into the space to gain the first mover advantage, the area is certainly going to see lot of traction in the coming years. I would like to conclude by citing that the interest shown by both large as well as small companies, along with the increased funding, will certainly boost this fast-evolving  segment. FAQs – Tavant Solutions How does Tavant ensure optimal performance monitoring for lending mobile applications?Tavant implements comprehensive mobile app performance monitoring through real-time analytics, automated error detection, performance benchmarking, and user experience tracking. Their monitoring solutions provide detailed insights into app performance, user behavior, and system health to ensure optimal mobile lending experiences. What mobile app performance metrics does Tavant track for lending applications?Tavant tracks app load times, transaction completion rates, error frequencies, user engagement metrics, crash reports, and system resource utilization. Their monitoring platform provides dashboards and alerts that enable proactive performance optimization and rapid issue resolution for mobile lending apps. What are the key performance metrics for mobile lending apps?Key performance metrics include app load time, transaction success rates, user session duration, conversion rates, crash frequency, API response times, and user satisfaction scores. These metrics help ensure smooth, efficient mobile lending experiences that meet customer expectations. How do you monitor mobile app performance effectively?Effective mobile app performance monitoring involves real-time analytics tools, automated error tracking, user experience monitoring, performance benchmarking, and regular testing across different devices and network conditions. Continuous monitoring enables proactive optimization and issue prevention. Why is mobile app performance critical for lending?Mobile app performance is critical for lending because poor performance leads to application abandonment, customer frustration, lost revenue, and competitive disadvantage. Fast, reliable mobile experiences are essential for customer acquisition, retention, and successful loan origination in today’s mobile-first market.

Video Ad Serving Via VAST

Historically, different proprietary video players used by different publishers had quite a few technical complexities, and thereby limiting the reach of digital video advertising campaigns. To resolve this matter, the Interactive Advertising Bureau (IAB) introduced an XML-based specification known as VAST (Video Ad Serving Template). In my opinion, it’s a disruptive innovation that transformed the video advertising landscape. VAST facilitated interoperability by having a common in-stream advertising protocol for video players. The protocol is based on an universal XML schema for serving ads to digital video players. If your video player can accept the VAST template, then it can play any ad that follows the protocol, thus reducing expensive technical barriers and encouraging advertisers to increase their video ad spend. VAST Calls from Video Player: Normally the video player makes an ad request to the ad server, and the ad server directly serves the VAST response. Within the VAST response, there are various elements for Ad impressions and tracking events. The video player understands these events in a standardized way and reports such events back to the ad server. Now let’s look at another scenario; the above scenario involves only one ad server but the fact remains that there can be multiple ad servers involved in serving the video ad. In this case, the video player first interacts with the primary server, gets a wrapper response and then hits one or more secondary servers to get the actual VAST XML. Since all the servers here will be interested in receiving tracking information, the video player pings each of them with tracking requests. The diagram below illustrates how multiple ad servers serve Video Ads on the web pages. Here are the sample elements in a VAST wrapper XML: <VAST> <Ad> <Wrapper> … <VASTAdTagURI> </VASTAdTagURI> …</Wrapper> </Ad> </VAST> Different Types of Ads supported by VAST: Linear Ads are the ones that you see before the content video begins (pre-roll), in the middle of the video (mid-roll) and at the end (post-roll). Usually in YouTube ads, you would see that the user is given an option to skip an advertisement after a few seconds. With 3.0 VAST Protocol, support for the ‘skip offset attribute’ (usually the value is 00:00:05), skip event and progress event has been introduced. Master or Companion Ads provide a more engaging experience for the user. A Companion Ad is displayed at a specific location of the same web-page while the Master Ad is being played in the video player. Non-Linear Ads are the ads overlaid on the top of video content. Using the VPAID technology, a more engaging user experience can be built, where the main video will get paused when user engages with a Video Ad and resumes when user cancels the ad. The IAB has also come up with new Ad Units known as “Rising Stars”. Rising Stars have been a result of sustained efforts towards greater creativity in interactive advertising, and it encourages rich user engagement. More information on Video Rising Stars is outlined here. With VAST 3.0, support for Ad Pods has been introduced. In this case, a series of ads can be played like TV commercials. This is accomplished by assigning a “sequence” attribute to the Ad element within a VAST 3.0 XML. The ads in an Ad Pod need to be linear but the last ad can have a non-linear creative. The placement an Ad Pod within a Video is outside the scope of VAST 3.0, but this can be accomplished by using VMAP where ad breaks can be specified within the content timeline by the content owners. You can read more about VMAP at iab.net/vsuite/vmap.

Conditional Orders: A Must-have to Tame the Bull.

Being a trader who trades in the derivatives segment of the market, I try and do a lot of my analysis overnight. Before the market opens the next morning, I place triggers for my trade entries & exits. The idea of automating my orders works very well for me as I am working as a Business Analyst (BA) in a major IT firm. Over the past few months, due to a sudden increase in my work assignments, this arrangement has posed a few obstacles for me. Trading has taken a backseat as I am unable to monitor the market regularly and modify my triggers whenever it is needed. Many times my ‘trigger orders’ suffer due to noises in the market, low liquidity or large spreads. Hence I have paused my trading on days of high workload in my job. I had an opportunity to work as a BA on a conditional order system recently, that we were developing for a leading financial corporation in the US. While working on it, I could envision the value that the system could provide to retail traders like me. One of the features of the system enables traders to place trigger orders in option contracts based on the underlier’ s price. Since the triggers are placed on the underlier, problems of large spreads and low liquidity are being tackled effortlessly. Additionally, on important macroeconomic event days, another feature of the system – contingent orders; helps in placing all my orders based on the index’s reaction to the event. The conditional order system developed has Trailing Stop order, Bracketed order, One Triggers Other order, One Cancels Other order and Contingent order. During my initial days of trading as a day trader, I would have loved to have a trailing stop loss, which would have been a blessing for any intraday trader to lock his profits whenever realized and stop out in the case of adverse market event. The bracketed order would be ideal for a trader, who trades based on risk-reward ratios in any trade. A trader can define his exit on the profit side and also have a stop loss in case of a loss at the pre-order stage. He can also blend it with trailing stop-loss orders to place an advanced form of bracketed order by replacing regular stop loss order with trailing stop loss. One Triggers Other order and One Cancels Other orders take a trader a step closer to algo trading. These orders can be used in multiple areas like risk mitigation, margin adherence and locking profits. It’s one of the aspects to the system that I would love to experiment and fine-tune my existing strategies. The conditional order system is definitely a value add to any trader who trades in multiple stocks or commodities or any other asset and for a trader, whose primary job is not trading. With such a system being in place, a lot of problems of traders like me will be resolved. It will also help part time traders to focus on their primary jobs without having to sacrifice trading. With many brokerages in the US already embracing the idea, I have to admit that I have been eagerly waiting for my broker to implement the system.

The Advent of the Third Screen, or May I Wear My Watch Again?

I wear a watch. That fact alone places me in an older demographic since many teens and twenty-somethings have long relied on cell phones as their sole time-keeping device. As a consequence, many watchmakers have suffered in recent years. I said I wear a watch, but it’s actually been out-of-order for several weeks forcing me to reach into my pocket to tell time.  The experience has been frustrating and has made me appreciate anew the value of having a watch that I can glance at easily. To each his own, I suppose. There have been attempts to make a smarter watch. Casio Databank watches were the rage in the 80s. They could store phone numbers and reminders, but they couldn’t be connected to an external computer. Twenty years ago, the Timex Datalink allowed the transfer of phone numbers, appointments, anniversaries, to-do lists, reminders, and alarms from a PC to the watch encoded in the CRT’s emitted light. Ten years later, Microsoft introduced the SPOT Watch which could receive email, weather forecast, stocks info, and news via FM radio bands. The SPOT could be updated dynamically if the user was in range of a compatible FM signal. In the end, these devices didn’t last and were superseded by Palm PDAs, and, later, smart phones. There has been a lot of buzz lately about the possible resurgence of watches. There are persistent rumors about an Apple iWatch that could potentially make watches cooler again. Other possible makers of smart watches include Samsung, LG, and Microsoft. These new smart watches would interact with a cell phone via Bluetooth, and, possibly, via Wi-Fi and cellular networks. The latter is less likely because of the power requirements of a cellular connection. There are a number of smart watches already on the market including the Pebble which uses Bluetooth to connect to an iPhone or Android phone and displays the time, email headers, reminders and text message. Consider the nascent field of the second or companion screen. The idea is that your main screen (usually the TV or the computer monitor) can be augmented by having an app running on a smart phone or a tablet. What if in the near future, most computing was performed on mobile devices, which would then be augmented by smaller devices such as smart watches. In essence, a smart watch would be a third screen; a companion to a second screen mobile device. Such a pairing would redefine the client-server concept. This is more than just a story about watches. A whole range of products could act as a third screen including: Head-mounted display systems such as Google Glass. A car HUD (Head-up display) A smart refrigerator with a display panel, etc…   You could imagine scenarios where the computing devices around you would be aware of your presence via Bluetooth, Wi-Fi, NFC, etc…, and automatically become third screens to your cell phone. You might be sitting in front of a TV which would display information about an incoming call. A similar situation could occur if you sat down in front of a PC, even one that didn’t belong to you. In a role-reversal, these main screens would become third screens to your second-screen mobile devices. With the definition of a common interface, any device you encounter could become your display of choice at that moment. A bit far out? Perhaps. Nonetheless, third screens are coming, potentially opening up a whole new field for software developers. Now, may I wear my watch again?

Tested Tips for Successful eCommerce Testing

The history of online retail is littered with expensive failures, many of which could have been avoided by better testing before the site was opened to the customers. Customers are unlikely to have confidence in a website that goes through frequent downtimes, hangs during a transaction, or has usability issues. All these reasons make testing crucial for eCommerce environment and any failure can be expensive in terms of lost revenue and dissatisfied customers seeking alternative sites. Most software projects operate within tight budgets and timelines, so QA managers need a systematic and cost-effective approach to testing that maximizes test confidence. Below are the areas to focus on in eCommerce testing: 1. Browser Compatibility Remember this as a thumb rule: your application must perform consistently at-least in the top 3 browsers. 2. Browser Tones Testing should cover the main platforms (UNIX/Linux, Windows, Mac) and the expected language options. 3. Page Appearance The appearance of web pages in a browser forms the all-important interface between the buyer and the business. You can’t afford to get this one wrong. 4. Runtime Error Messages Consumers get frustrated when a browser throws up gibberish. Ensure that the application captures and handles all errors by generating an apt and user-friendly error page. 5. Numb/Broken hyperlinks. You don’t want links on your Website that lead to… nowhere. Try out automated tools such as Xenu and LinkChecker or websites such as brokenlinkcheck.com 6. Page download times. Many studies estimate that page load times of ten seconds or more, combined with ISP download times could cause up to 33% of customers to leave a site before they buy anything. Test download time under genuine test conditions, rather than testing it locally. 7. Transactions Transaction processing is a dominant element of eCommerce applications. Test integrity and security of transactions. 8. Shopping, Order processing, and Purchasing In my experience, functional testing consumes between 30% and 50% of the total testing effort. In most eCommerce systems, shopping and order processing form the core functionality. Although most engineers largely consider functional testing a manual process, tools (such as QTP and Selenium) can often help automate aspects of functional testing by automatically capturing and re-running user interactions. 9. Tax and shipping calculations You might have to handle multiple taxes and shipping rates. The problem becomes more interesting if you have customers outside the country. Testing is necessary to ensure that the customer is charged the correct tax and shipping amount. 10. Security Security (or a lack of it) is a barrier to eCommerce. With the rise in credit card scams and high-profile hackings, buyers avoid websites they perceive to be insecure. Penetration testing is necessary to find out vulnerabilities before anyone else can. We have looked at areas to focus on in eCommerce testing for the delivery and presentation of content: But the question is … where should we initiate testing? I will share additional insights on it in my next blog… stay tuned!

What’s so Big about Big Data?

About 90% of the data in today’s World has been created only in the last two years. As per Google, every 2 days we create as much information as we did till 2003. There are around 200 million Tweets every day. Facebook gets around 6 billion messages per day. When it comes to handling this kind of a data explosion, conventional RDBMS has its own limitations; that’s where Big Data comes into the picture. Big Data is about handling the 3Vs – Volume, Velocity, and Variety. Volume: Big Data can handle data in Petabytes or more; this is a difficult task in RDBMS. Velocity: Velocity describes the frequency at which data is generated, captured and shared. Big Data is capable of handling dynamic data from diverse sources—online systems, sensors, social media, web clickstream, and other channels. Variety: Big Data comprises all types of data—structured, semi-structured and unstructured data (such as text, sensor data, audio, video, click streams, log files and more). According to O’Reilly Media,., “Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the structures of your database architectures. To gain value from this data, you must choose an alternative way to process it.” The comparison chart shown below throws more light on the differences between RDBMS and Big Data. RDBMS Big Data Variety Places data inside well-defined structures or tables using meta data. But it can’t handle semi-structured and unstructured data—like photos, videos and posting messages on Social Media. Has the capability to handle a variety of data (structured, semi-structured and unstructured data) through different NoSQL databases like graph, document, key-value and column family databases. Volume Can handle data in MBs and GBs better than any Big Data system, but its performance goes down as the data size increases to TBs or PBs. The RDBMS system can be scaled up and not scaled out. Also, the cost of scaling up a system is high. Good in handling a large size of data. So it is very efficiently used by sites like Facebook, LinkedIn and Twitter, where the data size is huge. Big Data handles this task through scaling out on commodity hardware. Velocity Can handle small sets of data, but can’t manage the speed at which data arrives on sites like Facebook, Twitter, etc. So, the performance will be poor when the velocity is high. Can easily handle high velocity data like the millions/billions of messages arriving on social networking sites. It can handle the data through parallel processing, which is not possible in RDBMS. Apart from data storage and retrieval, Big Data has capabilities to process the data efficiently, e.g., we can divide the data to be processed into hundreds or thousands of commodity hardware, and the data can be processed independently on each machine. So the bottom line is that the omnipresent and ongoing buzz around Big Data is definitely not a passing fad. This claim can be substantiated with the fact that today’s technology-driven, net-enabled businesses are continuing to count on Big Data – big time!

Social TV: You’re not “Home Alone” Anymore!

There has been a lot of buzz and hype surrounding Social TV. But, TV was always social—wasn’t it? Remember the days when our family sat in front of the TV to watch a weekend movie or a Super Bowl? We used to root for our favorite actors and players. It was easier to express emotions and share opinions in real time. Today the TV still exists, but the audience is scattered. The same movies are shown on TV, and the Super Bowl is still being telecast right in the living room but, the viewing experience has changed! With the advent of Internet—and more so with the likes of Facebook and Twitter—the word “Social” has become much in ‘vogue’. And if you thought the internet has killed TV, there’s some good news for you – both have become very good friends. More and more people in America are watching television than ever before, and engaging in online activities at the same time. They update Facebook and chat about the program they are watching. Well, this is called “Social TV”. Are you not socializing while watching TV? So the essence of socializing is still the same; only the medium has changed. Social TV refers to the technologies, surrounding television, that promote communication and social interaction related to program content. Social TV can leverage diverse technologies—like text chat, voice communication, TV recommendations, ratings, context awareness, etc.—so that users can share, view and experience the same show, movie or game on TV with their friends. Social TV is also an opportunity for content producers and TV operators to offer new services and increase revenue by studying the consumers’ TV-related social behavior, devices and networks. The networks would have an improved ability to understand broader audience engagement and affinity for TV shows. In turn, this data could be harnessed to drive greater tune-ins, boost viewer loyalty, optimize marketing promotions, and increase ad revenue. On the other hand, advertisers could better evaluate the value created by social media around their commercial placements or product integrations. These findings could be used to gather information on the shows or genres that drive more social conversations and create a buzz about a brand. This in turn would help in improving the return on traditional ad buys. Social TV has actually created the need for greater user interactivity without going to an external source. This need has led to the emergence of the Second Screen—a term that refers to an additional electronic device (e.g. tablet, smartphone) that allows a television audience to interact with the content they are consuming.

Software Testing: To automate or not, that is the question

We all understand the importance and need of test automation. The first and most important step to successful automation is to determine what to automate (first). Which Test Cases to Automate? The ROI of automated testing is usually correlated with how many times a given test case will be repeated. Tests that are only performed a few times, are better left for manual testing. Good test cases for automation are those that are run frequently and require large amounts of data to perform the same action. Testers can get the most out of their testing efforts by automating: Test cases that should be re-executed in each new build or release (usually regression) Tests that are subject to human error Tests that require multiple data sets Frequently-used functionality that introduces high risk conditions Tests that run on several different hardware or software platforms and configurations High risk, business critical test cases Test cases that are very tedious or difficult to perform manually   The following categories of test cases are not suitable for automation: Test cases that are newly designed and not executed manually even once Test cases for which the requirements are changing frequently Test cases that are executed on an ad-hoc basis   Success in test automation requires careful planning and design, apart from a robust framework for a given test scope. In my next blog post, I will discuss some of the best practices of test automation. Stay tuned…

Simplifying the Way to Multi-Platform Mobile Apps

In the past we have seen an elevated need to define a mobile strategy for every organization. With myriad device types and operating systems, organizations face a much bigger challenge. It’s no longer enough to just have, say an iPhone app; you need to be able to support iPad, Android phones, the Amazon Kindle, larger Android tablets, Windows Phone, and BlackBerry as well. Developing an app that supports all of these platforms is a challenge, especially for a business with limited resources. Fortunately, we have cross-platform mobile frameworks like Appcelerator Titanium, Rhodes, and PhoneGap for help. Using these cross-platform tools, developers do not have to write the specific code or sequence for each platform, instead they can write the code once and then reuse them in subsequent projects on other platforms. Since most of these frameworks also support HTML5 and CSS3 alongside the calls to more native functions, they are easy for web developers to learn and use. Of course, a cross-platform framework development strategy isn’t always the ideal solution. Most cross platform frameworks expect developers to use their own custom development tools and suites. So developers do not have the freedom to choose their own IDE. Also most of these frameworks use their own subsets of JavaScript, which means that if you want to switch to another platform, that code you wrote before is likely not going to be reusable without a lot of work. In using any framework, one major factor that cross-platform developers must bear in mind is app design. A good cross-platform application looks at home on whatever platform it is used on. A bad cross-platform tries to look identical everywhere. For instance, if your Android app has navigation controls at the bottom of the screen, iPhone style, you’re doing it wrong. To summarize, cross platform tools do solve a lot of business problems, but also suffer from a few short comings. Architects and business owners need to evaluate their requirements and vision of the app closely before making a well informed decision.

The Need for Online Game Platforms

We’ve had the honor of helping a large video game company develop and enhance its online game platform. Across game titles and devices, it gives the company’s customers a unified experience for login, entitlements, personas, billing history, and much more. And it offers the potential to add even more information to this unified view in the future. While doing this work, I just assumed that all large game companies have similar systems in place because of the numerous benefits… Recently, though, I’ve had the opportunity to meet other game companies and have been surprised that they haven’t yet centralized such systems or done so to the same degree across their game portfolios. Some have done this with a few of their titles. Some have common multiplayer technology behind-the-scenes for matchmaking, leaderboards, and sharing game state, but they do not share the data across titles to let players share their overall achievements with their friends and fans. Or they don’t share billing and other account related data. They have indeed innovated in other dimensions (such as amazing player performance stats for a battle game), but they haven’t focused as much on connecting a single customer’s gaming world together. Yes, we have platforms like Sony PlayStation Network, Microsoft Xbox LIVE, and Apple Game Center that provide a uniform experience to players, but those are focused on games that run on those devices, yet many players play games on multiple devices from consoles to mobile to the web. Thus it seems important for big game publishers to do the same from their point of view. It’s clear that there are many challenges to offering such a centralized platform, but there are many benefits to both players and the game companies. I’ve already touched on the benefits to players. The benefits to companies include having a powerful unified view of each player that permits the company to cross-sell better or offer more targeted ads. Shared platforms lead to a large reduction in development cost since the common backend components can be built just once. Though not initially obvious, a centralized model also leads to more robust solutions for each title because the hard lessons learned in one place can be shared by all. Finally, given the litigious world we live in, a centralized model reduces legal risk because a seasoned central team can implement regulations more thoroughly. (Some might argue that recent data breaches make common platforms more risky, but I still feel that those security problems can be solved better with a shared team and system.) The first and biggest challenge seems to be one of organizational structure. Most large game companies have many studios that operate independently largely due to the fact that they were acquired at various points in time, and in various geographical locations. That independence arguably allows for faster game design innovation in each group, but it makes sharing technology challenging. Who should own and operate a common backend platform? How should that team, if formed, be funded? How should work for different teams be prioritized by the central team? How can the central team support each studio’s aggressive plans rather than hindering them? In the future postings, we’ll discuss how game companies are working through some of these challenges as well as the technical ones. We’ll also delve into other topics related to the game industry.

TFS Automation Solution – How fast and How far…

Traditional focus of Test Automation in many organizations has so far been based on the readily available, cost-effective tools rather than the ones that are best suited to meet the business requirements, have a shorter time-to-market and are compatible with the technology stack of the applications under test. The market is shifting as the organization seeks greater business value and agility in any solution they plan to invest on. Therefore, it is imperative for every organization to have a well-defined set of practices, processes and parameters to gauge the compatibility of the varied automated tools with the application under test. On that note, we recently conducted a comparative analysis of Coded UI with the currently prevalent Open Source and Commercial Testing Tools. Coded UI is an automation framework which enables a user to record a set of action, generates the code for the same and allows the user to playback the recorded actions. It also gives the flexibility to write the custom code using a hybrid automation approach (keyword and data-driven). Advantages of Coded UI: Excellent support for applications running on Microsoft Technologies (Silver Light, WPF). Record actions and generate code using Test Builder. Well integrated with TFS/Test manager Competitive cost advantage over QTP   Disadvantages: No support for Java/Flash applications. Record and playback is supported for IE only. (Firefox supports only playback). No in-built object spy– user needs to record the actions. Complex Object repository mapping. Require good knowledge in C# for custom coding.   The comparative evaluation was done by Tavant’s Automation CoE team. Some of the key comparison parameters that were taken into consideration are: Learning curve involved, Script Development Time, Ease of Use, Maintenance and Re-usability. Stay tuned to see a detailed evaluation report on how Coded UI fared against QTP and Selenium. However, I must add that the “fitment” or suitability of a specific tool or set of tools needs to be assessed based on the Enterprise-wide business priority as well as the overall objective that is intended for implementing test automation.

Personalizing News to Enhance Consumer Loyalty

There is an exponentially growing pool of news resources available on the Web. Additionally, with the rise of the blogosphere and personal publishing, it seems that there is an ever-increasing amount of content out there. Hence, people no longer go to a couple of sources for news of their interest. Rather, they now invariably end up going to a number of sources depending on the category of news they want to learn about. Moreover, the channel of consumption (pc, smart phone, tablet computer, etc.) of newsmight also influence their choice of sites. Given the plethora of news sites and blogs, consumers are finding it very cumbersome to effectively track news. Therefore, there is a growing need to automate the collection, organization, and syndication of news. This is where news aggregators come in. Aggregators are sites specifically designed to bring multiple news sources together and repackage them in a more convenient format for the readers.. Traditional news media was all about distributing a one-size-fits-all message to a wide audience; new media is more about personalization and customization. The most important aspect of news aggregation is personalization, which is no easy job. Here are four important steps to make personalization “work” for your website: 1. Know the reader:  You need to first understand the likes and interests of each and every reader visiting your site. Allow readers to log in using their Facebook profile and you have a readymade list of their interests. Integration with their Twitter feed can help you understand the topics / people they follow. Some other ways of getting information about the readers include, checking the profile information, examining click stream to identify the topics of their interest, etc. 2. Aggregate and Personalize: Next, you must have an easy-to-use filtering / sorting mechanism that remembers users’ preferences, identifies information of interest, and displays them in an orderly manner. News aggregators like Trove used by The Washington Post, scour multiple sources of content and list stories of interest based on user profiles and behavior. Trove also leverages Facebook Connect to identify and accordingly personalize news stories based on preferences as outlined by users’ Facebook profile. Also, personalization would be incomplete without providing for sharing. Give users an option to easily share, post, retweet news of interest among friends through social media. 3. Adapt and Continue to Learn: It is important to progressively learn more about your readers over time. Consider using a mobile app that not only provides users access to the latest news but also allows them to keep track of their favorite news topics. Information on content accessed by users on-the-go can help you refine their profiles and accordingly alter, for example, the information you display, how you display it, and in what order you place the stories. 4. Allow Discovery to Retain Freshness:  Personalization must not lead to extreme predictability and, therefore, boredom. Even as you personalize content, you must retain a sense of discovery. Give your readers a chance to “discover” topics that is outside of their usual realms of interest. For example, it is always a good option to retain the “Top Stories” section even on a customized home page. Irrespective of their preferences, users demand that they stay informed of all that is happening around them. Displaying stories that are “liked” by readers’ friends on Facebook is another means of allowing discovery. Readers might like to read a story that, although not of immediate interest to them, is liked and recommended by their friends. Personalization is a tough job and we are just scratching the surface here. But it is time to make a start quickly or be left behind in the race for winning customer loyalty. Tavant has been working with several leading media companies to make this beginning in a strong way. Want to know how we are making personalization work for our customers? Click here and read on.

Online Commerce Concerns for Game Studios

As the Gaming industry has evolved in recent years, video games have become one of the most sought after forms of entertainment, surpassing even traditional entertainment forms such as music and movies in terms of revenues. And rightly so, since game studios have left no stone unturned in enriching game content, enhancing user experience, and improving online commerce to surpass game sales year after year. For studios to develop and enhance the online gaming experience, numerous factors need to be addressed, such as user login, game entitlements, managing avatars, online friends, etc. As the business approach and technology vary from one company to another, so do the complexities and nuances of their gaming systems. Our previous post covered the benefits of online commerce for both players and companies. In this post, let’s discuss some typical concerns that an enterprise needs to address while building a centralized gaming model. Supported Business Models: A studio can support multiple business models like direct purchase from sales portals, subscription services and virtual currencies like rewards points. The decision to adopt a business model depends on its business feasibility and market penetration potential. Implementation Approach: The implementation approach depends on the preferred business model, and it plays a key role in building the e-commerce infrastructure. The selection of appropriate technologies and a very meticulous system design are key for a successful centralized gaming system. Online entitlement of a game, in general, requires multiple levels of validations on the data entered by the user. This will internally require various supporting systems such as: Identity and Profile Management System: New age games have greater access to consumer information and hence, managing user data becomes a critical aspect of online game account management. Some studios prefer in-house solutions to meet their needs, as they believe a customized solution supports more rapid innovation and is better adaptable to emerging industry requirements. Billing System: The growing popularity of digital distribution has impelled gaming studios to look for stable and highly secure billing systems. To enhance the user experience, billing systems with virtually zero transaction time are being built. Also, to counter the growing risk of online data theft, a mere “https” is no longer good enough, calling for more robust and highly secure systems. With smartphones taking over the market, game companies have a pressing need to support mobile billing too. Furthermore, a billing system is dynamic in nature as it depends on the marketing and sales strategy of the company. Therefore, it should support free demo games, limited-time discounted games, deactivating/activating subscriptions, gifting, and bundling, while supporting the regular priced games too. A billing system is also expected to provide a vast range of features including but not limited to: – Storage and processing of account information from credit cards and debit cards – Support for prepaid electronic cards like Wallie and Paysafe – Support for payment systems such as PayPal, Giropay and Sofort – Redemption of reward points, etc. In-game Persona Management: Given rapid technological advances, posting personal content in games and interacting with other players in MMOs is now common, as well as allowing users to maintain multiple personas or avatars across multiple games. A persona management system here acts as a backbone, supporting the game content, and gives users the flexibility to traverse across games and levels from the same central account, thereby enhancing their multiplayer experience. Game Activation and Key Management System: For digital goods, the game activation process can be broadly classified into digital download entitlement and online game entitlement. The former allows a user to download a digital product, while the latter gives him access to go online. With rapid innovation in gaming content and competitive market conditions, game activation and key management system have become the most dynamic aspect of the e-commerce ecosystem. Growing piracy in the gaming domain has given companies a run for their money. I have often noticed users trying out various combinations of serial numbers, trying their luck to get game content for free. Then, there are others who use online tools to try breaking into a game. With a highly secure and fraud-proof game activation and key management system, such attempts can be foiled. It is even possible to track such users and take preventive actions against any piracy attempts. Legacy Architecture: This is one of the biggest bottlenecks in the upgrade path for IT infrastructure. The issue usually comes in when a system is in production and supports the titles already on sale, but is hard to enhance or scale. Organizations feel concerned about the ROI of such systems and avoid new investments. The decision to retire such obsolete systems is never easy, but with a phased implementation of SOA, teams can get a new system in place without causing much inconvenience to their customers. Others: Apart from the aforementioned issues, there are other non-technical requirements for legal, tax, audit, and partnerships, which may vary across organizations and geographies.

Usability: A Business Case

What is Usability? Usability has quite a simple definition — it means people’s ability to use a product easily and efficiently to accomplish their tasks. Usability or user engineering is a significant advancement in the process of developing products, such that they satisfy and delight users as well as stakeholders who invest in bringing them to the market. A usable product must be: • Easy to learn • Efficient to use • Easy to remember • Enjoyable to use • Visually pleasing Additionally, it should provide quick recovery from errors. Value Proposition Usability requires an innate understanding of value propositions — values sought by users, how a product will provide those values, and values sought by the business delivering the product. The goal is to achieve a balanced design that provides value for the business, stakeholders and users alike. Benefits of Usability Usability engineering offers significant benefits in terms of cost, product quality and customer satisfaction: • It improves development productivity through more efficient design and fewer code revisions. • It helps eliminate over-design by emphasizing the functionality required to meet the needs of real users. • It helps in early detection of design problems in the development process, saving both time and money. • It results in greater cost savings through reduced support costs, lesser training requirements and higher user productivity. • It enhances satisfaction of customers and improves reputation of the product as well as the organization that developed it. Preferred Design Approach Many top engineers and designers recognize that usability engineering is the preferred approach to designing products. This is for the following reasons: • Since technological products are designed for use by humans, it makes sense to clearly define their needs before building products for them. • It has become clear through research and observation that product designers and developers cannot effectively speak for users. Designers have different backgrounds, levels of experience, goals and motivations from those of users. Therefore, designers should not guess or make assumptions about users’ needs and wants. • It has often been seen that preference does not match performance. In other words, what users say they need and want is often substantially disconnected from what they actually need and want, when faced with using a product to perform a task. Therefore, the only effective way to determine what is best for users is to observe them performing tasks with the product of interest. Cost Justifying Usability Enhancing the usability of software-based products (and services) is smart business. Usability improves productivity, enhances customer satisfaction, builds customer loyalty, and inevitably results in tangible cost savings and profitability. Since user-interface (UI) development is an important part of product development, and the cost for UI development is included in product development costs, it pays to do it right at the very first attempt. Some important statistics for cost justifying usability: • It is about 400 times more expensive to fix problems in the maintenance phase of a project than in the design phase. E.g., if it cost $10 to make a program change during development, it would probably cost $400 to do it after the system is in field. • Around 63% software projects exceed their cost estimates. The top four reasons for this being: o Frequent change requests from users o Overlooked tasks o Users’ lack of understanding of their own requirements o Insufficient communication between users and analysts, leading to lack of understanding • Only 33% of the maintenance effort is spent for debugging and therefore, 67% effort is needed for changing the system. To learn about more such statistics, please click here. Return on Investment Most software and website development managers treat the money and effort spent on usability as unnecessary. In the first 10 per cent of the design process, key system design decisions are made, which can determine the rest 90 per cent of a product’s cost and performance. Therefore, usability techniques come in handy to keep a product aligned with company goals [1]. Contrary to popular belief, usability offers significant returns on investment (ROI) to products developed for either internal use or sale [2]. The ROI can be internal as well as external. Internal ROI: • Improved user productivity • Reduced user errors • Shrunk training costs • Savings from making changes earlier in the design lifecycle • Lesser user support External ROI: • Higher sales • Reduced customer support costs • Savings gained from making changes earlier in the design lifecycle • Slashed costs of training (in case training is offered by an external vendor) For more statistics, please read the white paper titled ‘Return on Investment for Usable User-Interface Design: Examples and Statistics,’ written by Aaron Marcus here. How to Convince Clients to Pay for Usability Do your clients assert that there is no reason to test an application’s design, since you were hired because of your proficiency in creating good web applications and the design was expected to be flawless? It is widely believed that asking a design to go through user test might challenge the design firm’s skills and expertise. It might make people question, “are designers so uncertain and unsure about their own work that they need to test it?” In reality, using sound methodology is the cornerstone of professionalism, as is knowing how to manage a project by planning for the necessary steps. Consider this: If developers were hired to code a piece of custom software and they claimed that there was no need to debug the code, they will be considered as crazy. In software development, we know from experience that all code has some or the other bugs. It’s impossible to write perfect software right at the very first attempt; the only way to deliver high-quality programs is to use a sound development process with explicit steps for several types of testing. Modern user interfaces are just as complex as software in terms of the number of different variables considered. Moreover, many years of usability engineering experience has proved that

Outsourced Software Testing – A Paradigm Shift

In their quest to develop intuitive and easy to use software with near-zero defects, many software companies are looking at outsourcing software testing. With this trend catching up in the industry, the focus of offshore service providers has also moved from software development to software testing. Ubiquity of offsite software testing companies has only encouraged this already growing trend. Over the last decade, the way organizations buy and use technology has changed radically. As of 2008, the annual spend on IT services globally was reported to be more than $500 billion, with Forrester Research citing continued growth in infrastructure and applications outsourcing as well as offshore and project consulting[1]. This growth has been driven by the realization that in a globalized economy, outsourcing can offer significant gains in efficiency, productivity, quality and revenues. However, despite the potential benefits of outsourcing, very few organizations outsource testing of existing or new software applications. According to a survey of IT leaders conducted by Forrester in 2008[2], only 16% of organizations outsourced software testing. This despite the fact that the outsourced testing market is actually poised for rapid growth; the growth rate for outsourced testing services is 50 per cent annually, or even higher in some cases. Today’s businesses rely on cutting-edge tools and technologies that can be rolled out quickly and can perform consistently time after time for end-users. There is a growing realization that quality software testing is a specialized and professional skill, and not merely an afterthought activity slotted in at the end of the development lifecycle. A single application failure at a crucial point in a process or transaction can be both expensive and complex to repair; therefore, it’s vital that upfront testing and identification of defects is as good as it can possibly be. But how can one decide when to outsource testing? Well, ask yourself the following questions and if your answer to these is “Yes,” then you certainly need to look at outsourcing the testing function: 1. Do I have frequent releases / minor releases, which need to be tested for both the release scope as well as for regression? 2. Do I wish there was scope to reduce the development and test cycle time? 3. Do I wish there were fewer showstopper defects in production? 4. Is the time to market a crucial factor for my organization’s success? 5. Is the application required to conform to performance or security guidelines? 6. Do I need to have my software tested on multiple platforms, versions of OS, usability? 7. Can I enhance my ROI using Automated Testing? 8. Am I building domain competence in testing? However, it should not be assumed that outsourcing the testing function for different projects will deliver same benefits. But to ensure the best possible results from outsourcing of software testing, a few elements are crucial, such as: • Staffing and specializations of the test function • A business-driven, process-oriented, structured testing methodology • Use of appropriate tools and techniques • Engagement model • Quantitative performance measures • Domain expertise – both business and technical • Independence of testing team from development team • Continuous implementation of best practices With these elements in place, one can be assured that testing truly delivers business value. Some of the key value adds and benefits of outsourced testing are: • Lower costs • Higher profitability • Enhanced efficiency in testing • Reduced time-to-market • Focus on core business • Exhaustive coverage and predictable quality of deliverables • Skill generation and training • Scalability, flexibility and specialization for staffing • Technological advancement At Tavant, we realize that inadequate software testing can prove to be damaging for the quality of your software as well as the costs associated with it. More so, because quality of software is the key to enhance business value and deliver customer delight. We can help you realize 35% or more savings in your testing costs through: • Enterprise-focused Center of Excellence (CoE) for testing (ZeroD) • Flexible Engagement Model • Expertise in Distributed Agile QA • Use of specialist independent testing services • Business-driven, structured testing methodology • Reusable automation test framework • Predictable and repeatable test process When successfully executed, outsourcing of software testing can reduce the cost of the testing function by 30-35% or more as well as lessen the time-to-market, while improving the overall software quality. It gains even more importance because most businesses heavily rely on software applications for their day-to-day operations. Given this situation, it can be said that impeccable software plays a pivotal role in the success of most businesses, irrespective of their areas of operation. References: [1] The State Of Enterprise IT Services: 2008, Forrester Research, September 2008. [2] Capture Value From The Growing Diversity In Outsourced Applications Testing Services, Forrester Research, October 2008.

Is ‘Complete Mobile Freedom’ a Fable?

We are in the middle of a ‘mobile’ revolution. OK may be not bang in the middle – but the revolution is still pretty ferocious. Mobile handsets are becoming increasingly more powerful for the same price. There exists a generation of ‘on-the-go’ businessmen and executives who want to be as productive on the move as they would be at their desks. They need to be able to work with their handsets as easily as they do with their conventional computers on their desks. But why does the idea of ‘Complete Mobile Freedom’ still seem far-fetched? Is running native apps the only way to get you mobile? Why does one still fear the loss of a Xtop machine? [Xtop = Laptop or Desktop] Today’s devices and their browsers are very reliable. They can do a fantastic job of not just rendering images and text, but also implementing SSL security for remote connections. In a nutshell – today’s mobile browser has become powerful and sophisticated. With this premise, think of applications that use real-time data and events – like media applications and trading applications that use streaming real-time data to keep the user in touch with second-by-second updates. Such applications are already there on most mobile phones! What is so interesting about that fact is that such applications are a perfect match for people ‘on-the-go’. So, how easy is it to make such applications work in the mobile environment, and what are the key challenges? We at Tavant have been part of this revolution, and our experiences show that there are two aspects to this environment that can be quite challenging. The first is the fact that cell phone hardware and software are far from standardized, with each OEM having their own specifications and features. This means that native applications developed for one manufacturer would not work for another. Sometimes, the same app may not even work across different models of the same manufacturer. The second challenge is with the intermittent nature of the communication. Mobile networks are expected to be weak or unavailable in certain areas and especially inside buildings and elevators. Thus the virtually unbroken network connectivity of a wired connection cannot be replicated here. Applications developed for the mobile have to keep this important aspect in mind. How can we design mobile apps in view of the challenges mentioned above? One of the ways to work around the portability problem is to prefer web applications for mobile browsers, and use native apps only when specific features of the native system are critical to the application – a good example would be touch and sound features for a mobile game app. Since the browser behavior is more standard across manufacturers and models, web applications are more easy to port (though adjustments are still needed for screen form factor) than native applications. Also, the web applications should be designed so that the rendering of the pages is separate from the data being displayed. This way, stylesheets can be used to mould the view to each model and screen-size, increasing portability. The problem of intermittent connectivity would most adversely affect the applications that deal with real-time data. The best way to work around this is to design the applications to use asynchronous communications and incremental updates for any data fed back to the mobile. Though loss of connectivity would still prevent immediate responses, at least transactions in-flight would not be affected, and the screens would update themselves faster as soon as connectivity is re-established. For streaming data, it is advisable to use the “Long Poll” method instead of the “Persistent Connection” method since the latter is a major drain on battery life. These are just some of the many considerations and decisions that are needed to be able to design productive and intuitive mobile apps. The mobile app universe is seeing rapid growth, and the learning and resultant standardization will definitely go a long way in conquering many of the challenges in this area.

A Coward Approach to Project Management – A New Paradigm

Cowards die many times before their death. They have played out all possible scenarios that could lead to a catastrophe. Today’s managers need to think like cowards for their projects. They need to look at all possible scenarios that could lead the project to failure. Project management is not a war but it is surviving your project and making it see the light of the day, with available resources and minimum damage. Risk is best defined as the potential to suffer a loss. Risk management is described as a collection of methods and techniques which are designed to ensure that a project, company, or an organization is guarded against these risks to the maximum. While it is impossible to be prepared for every single problem that may come, there is a group of key risks that most organizations should protect themselves against. At Tavant, risk management is one of the most crucial aspects of project management. Projects are vulnerable to many threats – both external and internal. The objective of risk management is to identify these threats and work out a strategy to eliminate, minimize, avoid, or transfer. A well laid out risk management plan not only helps deliver project on time, but also impacts the budget and quality in a positive way, and not to forget the team is happier and motivated as they don’t have to get into fire-fighting mode. Risk assessment entails mathematics, philosophy, individual personality, and perception. When you begin dealing with risk assessment, we consider three things: (a) impossible, (b) possible, and (c) real. The issue of risk assessment involves things which are very possible. The possible can be thought of as a thing which can occur but has not yet occurred, and what is the probability of it occurring. This assessment is qualitative risk assessment. The quantitative risk analysis is designed to get numerical data which is related to the probability that a particular risk and its consequences will occur. Risk realized is called an issue; and what better way to handle an issue, if your risk planning already has a strategy to handle the potential risk (which now has become an issue). You can never be too careful. However, projects, companies, and organizations need to define the threshold of their risk appetite. There is nothing wrong in being pessimistic and paranoid about things going wrong in a project, but just being pessimistic is not sufficient. It is imperative that you layout a plan and strategy to counter them all and run the project optimistically. The reality is what you will see on daily basis.

Cloud in the Mind!

Long distance relationships are hard – or are they just mind over matter? Today’s economy wants to tighten its belt, and within this philosophy lies a fertile ground for cloud computing. Let’s not argue – the cloud has arrived and is here to stay. However, the economies of scale that a cloud provides are both a friend and a foe for the enterprise. As much economic benefit cloud provides, it also extends a proportional sense of ‘loss of control’. If not in reality – this is more of a psychological problem. Ask a CIO who’s yet to get on a cloud – long distance relationships with data are intimidating. But here’s the cool part – the distance from data is dependent on how quickly you can access it and how secure you can make it. If neither of the 2 parameters change, you would never know if the data was moved from your premise to mine. So – why is cloud a big deal? Straight up – cost savings and economies of scale. IT plays a huge role in an enterprise. And IT management is proportionately budgeted for. With cloud, IT services can be offered at a fraction of the cost relieving massive challenges on resources and infrastructure. With unified access, data can be made more reliable, available, and secure. Which is eventually the whole point of IT and its management anyways isn’t it? And that’s what gives the cloud its silver lining. For the ones who still don’t buy it and want numbers, check this out – Gartner predicts that by end of 2013, cloud services market will be worth over $150 billion annually. But I’m sure you’re still wondering – what if… and why not…? Well you can argue all you like and try to convince yourself that the cloud will go away. But with the kind of benefits that the cloud provides today, it’s impossible for the enterprise to ignore its financial implications. In fact a study on cloud risks carried out by European Network and Information Security Agency (ENISA) recently, names compliance and malicious intent as two of the highest risks through cloud. These risks are anyways high up on a CIO’s risk list, even with an on-premise infrastructure. But the study has also raised high concerns on legal risks for data. The need for standardized jurisdiction coupled with an obsession on information management quality is a must. Sure over time the cloud will mature and service providers will take on more aggressive positions within your IT environment. A lot of bumps will smoothen out and early adopters would explicitly benefit on their balance sheets. It would be like the ERP story all over again. However, the question that you need to answer is – do you really want to manage IT? Do you really need to manage IT? Do you want to commit yourselves to the periodicity of systems, software, and resources? The cloud is here to stay – move in early. There’s no phrase called ‘second mover advantage’ and there’s a good reason for it. Don’t say I didn’t tell you …

Automate. Accelerate. Proliferate.

Test automation has emerged as a buzz word in the last few years and software testing teams are scampering to find high ROI tools that can automate the various levels of test automation. Though manual testing is comparatively straightforward, advanced script-based testing requires experience and skill that are difficult to come-by. So the world moves towards test automation. The idea is to automate testing and reduce costs and time-to-market for software. However, the nuances of test automation are still evolving and most users are overwhelmed by complex tools and long learning curves. Unfortunately this cannot be evaded. After all – quality assurance is all about assuring quality. High license fees, poor access to automation engineers, cost of delivery are preventing testers from providing competitive advantages to their applications. Delayed launches, under-tested products, non-optimized functionalities, etc. can become financially demanding for your business. Add to this a competitive market place and there’s almost no room for error. Thankfully, some of us at Tavant found simple solutions to these complex problems. By creating a unified and platform agnostic solution called Tavant Watir Automation Framework (TWAF), manual testers can now build and execute complex code-based test cases by simply using language based commands. TWAF leverages the open source framework WATIR and follows scripting based on Ruby. Unlike complicated Java scripting, Ruby is less complex and significantly reduces the learning time for a manual tester. Developed over the WATIR framework is Tavant’s customizable engine that allows simplified English based test cases. The layer has a validation engine that prompts users for incorrectly spelled objects. The simple English language based query system allows manual test engineers to build advanced test cases which can then be automated. Current releases can be quickly tested and screened to ensure optimum performance. One of the best aspects of TWAF is that it is platform independent. This means that manual testers can use any operating system and still enjoy flawless test automation – even for the code.

Lending Web Channels

The relevance of the web to today’s business enterprise is now an assumed necessity. Yet, in the context of rapidly evolving technologies, tools and behaviors, Web 2.0 and social media are redefining the web from a purpose-built channel to a core-to-business platform. It is not the dot com of your business that matters, but the capabilities, the depth and breadth of the web-as-business that matters.It is within this rapidly evolving context that businesses are forging not only better incremental gains and saving but also creating a sustainable competitive advantage. Traditional advertising is evolving into full voiced communication, branding into identity, and order taking and sales into relationships. This new definition of web-as-a-channel is cutting right across the firm to build a tighter, firmer, and more aware organization that is tuned to the customers’ needs.Let’s look at the key value propositions of direct channel portals Optimized resources – With a virtual office the need to physically allocate more resources in the branches is reduced significantly. In fact, lending institutions can create easy-to-follow and comprehend walk-throughs on their portal that can enhance usability and provide a rich portal experience without additional overheads on resources and staff. Data integrity – Since data remains collated within a singular location, its integrity is undeniable at all times. Sabotage is out of the question and even a sudden surge in demand will not throw the system off track. Multi-point sale – The portal’s real estate is more effective than the next door branch. Couple this with well planned marketing; multi-point sale doesn’t remain a myth. Visitors can choose what they want, self service their needs through intelligently designed UI, and compare different products. Simplified integration – Be it upstream or downstream systems, direct channel portals can seamlessly integrate with existing systems and thereby provide a singular data stream with zero intrusion. This allows possibility of expansion and segregated products for different types of media without risk to the data’s integrity.   Today’s competitive business landscape demands faster time-to-market for a new product. Customers demand seamless servicing and will not accept slow or failed communication.. FAQs – Tavant Solutions What web channel solutions does Tavant provide for lending institutions?Tavant offers omnichannel lending platforms including responsive web portals, mobile applications, social media integration, and API-driven third-party channel support that create seamless customer experiences across all digital touchpoints. How does Tavant optimize web channel performance for lenders?Tavant provides channel analytics, conversion optimization tools, A/B testing frameworks, and performance monitoring that help lenders maximize the effectiveness of their web channels and improve customer acquisition rates. What are lending web channels?Lending web channels include company websites, mobile apps, social media platforms, comparison sites, and third-party marketplaces where customers can discover, apply for, and manage loans through digital interfaces. How do multiple web channels benefit lenders?Multiple web channels increase customer reach, provide more touchpoints for engagement, allow for targeted marketing, improve conversion rates, and enable customers to interact through their preferred digital platforms. What features should lending websites have?Essential features include loan calculators, instant pre-qualification, secure applications, rate comparisons, educational content, customer portals, mobile optimization, and integration with backend lending systems.

Social Networking – connect with your audience!

Popular social networking sites such as Facebook and YouTube have demonstrated how much people across the world love to share what’s happening in their lives and share what they’ve created. There are thousands of examples of other companies innovating with this model, but to what extent have you thought about how to use some of these ideas in your own business? Consider this example. Electronic Arts launched The Sims 3 game in June along with a community site: www.TheSims3.com.The Sims 3 game lets players create all kinds of objects for use by their virtual characters called Sims – items such as clothing, accessories, furniture, households, and lots. It lets them create custom Sims too with different personality traits. The Sims franchise has been one of EA’s (and the gaming world’s) most successful businesses. The response to the recent game launch was amazing – EA sold 1.4 million copies in just the first week. The activity on TheSims3.com is equally impressive. Fans have been using all of the new features for expressing their creativity in great numbers. They’ve created hundreds of thousands of items for others to download into their games. They’ve created tens of thousands of videos and stories using recorded clips and images of their gameplay. Furthermore, players have been sharing their work outside of TheSims3.com community on social networking sites like Facebook thanks to the seamless integration the new site offers. This not only helps players scratch their itch to share their work with their friends, but it also helps EA gain greater exposure by leading people who don’t own the game to the game site. TheSims3.com helps EA’s business in another, more direct, way. It offers a store that sells premium, EA-created items through micro-transactions – the pricing is low enough to remove the hesitation to spend. Looking at popular web destinations today, we still see lots of opportunities for businesses to engage their audiences better by adding features similar to those that EA added. Are you running a sports fansite? Why not let fans create videos or comic strips based on game footage, perhaps even mixed with their own personal video clips and images? Yes, I’m sure there are copyright issues that you’ll have to address, but there must be at least some content you’re willing to license to your users as building blocks for their creativity. Do you operate a destination where people can watch movies or TV shows? Fan sites for music groups? The same concept could work there as well.