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.
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.
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.
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.
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].
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.
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! 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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.
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.
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.