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.” 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