Capitalising on the power of big data for retail


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The retail industry is undergoing a dramatic shift, as today’s technologically empowered consumers find new ways of transacting with retailers. Online shopping, Mobile commerce and Social media have provided the consumers with an interconnected network for sharing opinions, reviews with each other on brands and products - in turn creating vast volumes of Data. These and other changes in the retail industry are creating important opportunities for retailers. But to capitalize on those opportunities, retailers need ways to collect, manage and analyse a tremendous volume, variety and velocity of data. Simply put, adopt new approaches to keep customers engaged, maintain a competitive edge and maximize profitability.

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Capitalising on the power of big data for retail

  1. 1. IBM Software Big Data Retail Capitalizing on the power of big data for retail Adopt new approaches to keep customers engaged, maintain a competitive edge and maximize profitability
  2. 2. 2 Capitalizing on the power of big data for retail The retail industry is changing dramatically as consumers shop in new ways. With the growing popularity of online shopping and mobile commerce, consumers are using more retail channels than ever before to research products, compare prices, search for promotions, make purchases and provide feedback. Social media has become one of the key channels. Consumers are using social media—and the leading e-commerce platforms that integrate with social media—to find product recommendations, lavish praise, voice complaints, capitalize on product offers and engage in ongoing dialogs with their favorite brands. The multiplication of retail channels and the increasing use of social media are empowering consumers. With a wealth of information readily available online, consumers are now better able to compare products, services and prices—even as they shop in physical stores. When consumers interact with companies publically through social media, they have greater power to influence other customers or damage a brand. These and other changes in the retail industry are creating important opportunities for retailers. But to capitalize on those opportunities, retailers need ways to collect, manage and analyze a tremendous volume, variety and velocity of data. When point-of-sale (POS) systems were first commercialized, retailers were able to collect large amounts of potentially valuable information, but most of that information remained untapped. The emergence of social media and other consumer-oriented technologies is now introducing even more data to the retail ecosystem. Retailers must handle not only the growing volume of information but also an increasing variety—including both structured and unstructured data. They must also find ways to accommodate the changing nature of this data and the velocity at which is being produced and collected. If retailers succeed in addressing the challenges of “big data,” they can use this data to generate valuable insights for personalizing marketing and improving the effectiveness of marketing campaigns, optimizing assortment and merchandising decisions, and removing inefficiencies in distribution and operations. Adopting solutions designed to capitalize on this big data allows companies to navigate the shifting retail landscape and drive a positive transformation for the business. Imagining the possibilities How can solutions for big data help retailers? They can improve the effectiveness of traditional retail processes by generating new insights while creating new capabilities that drive better business outcomes. For example: • Personalized shopping experience: To help serve a customer shopping for a new TV, a retailer could analyze data from previous transactions, clickstreams, social media, geospatial services and other sources to understand the customer’s preferences and push a highly targeted, real-time promotion on to the customer’s smartphone as he or she shops in a store. Retailers can also examine broader customer search patterns, preferences and purchases to generate meaningful and interesting offers and suggest complementary products to provide greater value to the customer while boosting revenues. • Optimized merchandising: A retailer could better determine which products will sell best through each retail channel, at each store location and at what price. For example, a retailer could analyze fast-changing social media buzz about an upcoming superhero movie to gauge demand for particular action figures across multiple geographic locations. With insights into which specific product will sell best in each location, the retailer can ensure that stores in each area are well stocked with those products when the movie is released. Real-time competitive price comparisons can help the retailer set pricing or launch promotions that attract consumers away from rival retailers.
  3. 3. Retail 3 • Operational excellence: Analyzing communications, traffic patterns, weather data, political news and consumer demand signals could help a retailer manage retail distribution networks in real time to ensure timely delivery of products and achieve high-quality operational performance. Creating a personalized shopping experience Effectively analyzing the large volume and variety of customer data opens new opportunities to gain a deeper, more complete understanding of each customer and create a smarter shopping experience. What if you could: • Increase the precision of customer segmentation by analyzing customer transactions and shopping behavior patterns across all retail channels? • Enrich your understanding of customers by integrating multichannel data—from online transactions to social media and third-party data—to develop a 360-degree view of each individual and identify emerging trends? • Optimize customer interactions by knowing where a customer is and delivering relevant real-time offers based on that location? • Predict consumer shopping behavior and offer relevant, enticing products to influence customers to expand their shopping list? Marketing teams can use solutions for big data to collect and analyze customer information from a wider range of sources than before—including POS systems, online transactions, social media, loyalty programs, call center records and more. That information deepens their understanding of customer preferences, helps them more accurately identify shopping patterns and enables them to generate more precise customer segmentation. Marketers can then use new insights to deliver highly targeted, location-based promotions, in real time. Email Text analysis for pattern identification Customer Demographics, transactions and shopping patterns Drive marketing optimization Data • Customer micro-segmentation and full 360-degree view • Additional value and insight from sentiment analysis • More accurate satisfaction scoring • Demographics, transactions and shopping patterns • Timely delivery of offers to customers Call center Text and audio call records Video Surveillance, foot traffic in store POS Transaction logs Geospatial Where is the customer? Outcomes • Reduce marketing cost • Reduce churn • Increase visits and conversion • Increase customer loyalty Social media Customer sentiment Events Weather, local events Clickstream Online activities The result? Customers gain a richer, more personalized shopping experience with promotions and offers that are more likely to appeal to them. Retailers, meanwhile, are able to retain a competitive edge and boost revenues by maximizing cross- and up-sell opportunities, as well as consistently engaging customers across channels and reinforcing their brands at every turn. Figure 1. Retailers can draw on a wide variety of data—from transaction and clickstream data to social media and geospatial information—to enhance the effectiveness of marketing efforts and deliver real-time promotions.
  4. 4. 4 Capitalizing on the power of big data for retail Optimizing merchandising and supply chains Implementing a scalable big data platform can also help retailers build smarter supply chains and optimize merchandising across a multi-channel retail operation. What if you could: • Predict optimal pricing and maintain a price leadership position by analyzing price and demand elasticity? • Select the right merchandise for each channel and fine-tune local assortment planning by drawing on insights from social media, market reports, internal sales data and customer buying patterns? • Optimize inventory across multiple channels by using leading indicators such as customer sentiment and promotional buzz to anticipate future demand? • Fine-tune store planograms by analyzing customer buying patterns and purchasing trends? • Improve logistics by using real-time traffic, weather data and more to re-route shipments and avoid costly delays? Today many retailers monitor average prices by competitors on a weekly basis. With solutions for big data, they can conduct instant, real-time price comparisons of top competitors, tracking hourly price changes and synchronizing those changes with demand trends. Retailers can then use new insights to set their own pricing, initiate discounts and implement competitive real-time promotions to avoid losing sales—and gain agility. Figure 2. With better knowledge of competitive pricing and demand trends, retailers can initiate sales and promotions that help avoid losing business. Customer Demographics, transactions and shopping patterns Data • Ability to price by channel, region, time of day • Ability to move from store cluster assortments to individual store assortments • Integrated execution knowing customer’s preferred price point, profit targets, supply and timely offer delivery Product Availability, location, margins POS Transaction logs Geospatial Where is the customer? Outcomes • Increased revenue and margins • Improved marketing ROI • Fewer stock-out situations and markdowns • Optimized inventory • Increased customer satisfaction Social media Customer sentiment on pricing and demand Competitors Product availability, hourly price changes Events Weather, local events Execute dynamic pricing and create localized assortments
  5. 5. Retail 5 Enabling operational excellence In addition to improving marketing and merchandising efforts, solutions for big data can help retailers realize a variety of operational goals, from improving labor utilization to enhancing financial management. What if you could: • Optimize staffing levels by predicting changes in customer demand? • Better match employee skills with retail store needs and create the right incentives to drive strong sales performance? • Facilitate better-informed financial decision making by drawing on complete, trustworthy and timely data from a wide array of sources? • Improve fraud detection by analyzing large volumes of transactions? A flexible, comprehensive big data platform can play a key role in improving labor utilization and performance. Many large retailers rely on historical data to schedule their thousands of associates and assign those associates to the thousands or millions of tasks involved in providing a positive shopping experience. With solutions for big data, retailers draw on insights from price optimization, assortment planning and marketing to improve labor scheduling. They can incorporate employee performance analysis to optimize work assignments according to skill sets and manage incentives. Discovering the value of implementing big data solutions Leading retailers are already discovering the tremendous value of implementing solutions designed to analyze, organize and apply big data. Delivering a richer multichannel retail experience with new customer intelligence Bass Pro Shops—a leading retailer in fishing, hunting, camping and other recreational activities—capitalized on solutions for big data to create a richer multichannel retail experience. The company needed ways to increase retail shopping consistency across a full range of channels, including its retail store, boat dealership, Internet, catalog, wholesale, restaurant and resort channels. The existing enterprise data warehouse could not provide detailed analytics on individual customers or purchases across multiple channels. The company selected an IBM® Customer Intelligence Appliance, which provides a single view of each customer plus the capabilities for business intelligence and analytic reporting on customer behavior. The solution can generate reports in less than 10 seconds. Bass Pro Shops can now increase customer satisfaction and improve loyalty by providing a consistent experience no matter how customers choose to shop. New customer insights enable the organization to tailor offers and fine-tune each of the customer channels to maximize the appeal of products and drive more sales.
  6. 6. 6 Capitalizing on the power of big data for retail Enhancing analytics to improve merchandising decision making A large discount apparel and home fashion company capitalized on the potential of big data to optimize merchandising. The retailer needs timely insights on consumer demand and changing product prices over the course of a clothing season to purchase the right inventory for its stores. Unfortunately, the company’s existing analytics solution required an entire weekend to generate results, leading to missed supply chain and merchandising opportunities. The company implemented an IBM Customer Intelligence Appliance and deployed analytics capabilities to deliver key insights rapidly to buyers. Because the solution was a pre- integrated appliance, it was up and running in just weeks, without requiring excessive IT services. The solution’s performance enables the company to run queries 20 times faster than before, producing results to some queries in just seconds. Now 500 employees across the company use the analytics capabilities to quickly identify new opportunities and make key merchandising and supply chain decisions. Expanding customer analytics to optimize marketing, merchandising and operations For a global electronics retailer, solutions for big data helped expand its customer analytics efforts. The company needed to replace its 13-year-old CRM system, which offered only a store-centric view of customer patterns, required more than six weeks to build new models and generated reports too slowly to keep up with business demands. The retailer needed a solution that could analyze customer information across a widening array of customer data, including social media posts and clickstreams. The goal was to improve customer satisfaction and loyalty, allow marketers to create personalized offers, enable merchandisers to optimize assortment and pricing, and help managers to optimize the placement of in-store displays. The retailer replaced its existing CRM system with a new solution that combines an IBM Customer Intelligence Appliance with SAP software for analytics and reporting. The company now has a single view of each customer across channels, plus analytics capabilities to build segmentation models, score customers and run campaigns in hours. Figure 3. The IBM Big Data Platform offers an array of integrated capabilities to address the tremendous volume, variety and velocity of big data. IBM Big Data Platform Analytic applications Applications and development Visualization and discovery Systems management Accelerators Stream computingApache Hadoop system Data warehouse Data exploration Information integration and governance Cloud | Appliances | Mobile | Security BI/Reporting Exploration/ Visualization Functional Application Industry Application Predictive Analytics Content Analytics
  7. 7. Retail 7 Marketing and merchandising teams can draw on that single view of the customer to deliver more personalized offers and loyalty rewards, fine-tune merchandising for customer preferences and optimize the store layout. Predictive analytics capabilities enable the retailer to anticipate the next customer actions and improve interactions across channels and at each step of the customer lifecycle. Creating a data-driven retail enterprise Offering a broad portfolio of solutions and capabilities, the IBM Big Data Platform is helping retailers capitalize on the vast potential for big data in retail. The platform-based approach allows organizations to leverage their investments in technologies and skills by allowing them to start with capabilities for executing one particular use case and easily add others using the same platform. Pre-integrated capabilities help accelerate the time to value. Leading retailers can adopt IBM InfoSphere® BigInsights™ to collect, process, analyze and manage a large volume and variety of customer data from multiple sources. They could analyze everything from transactional data to unstructured social media data, learning more about customer preferences and future behaviors. Using IBM InfoSphere Data Explorer would enable these retailers to rapidly search massive volumes of historical or unstructured data. By implementing IBM InfoSphere Streams, retailers can continuously capture, analyze and cleanse data in motion to facilitate real-time decision making. A marketing team could gauge the success of a campaign by analyzing trending topics in social media. Merchandisers could analyze customer calls, e-mails and social media posts to assess rapidly changing demand for particular products by location. Using the IBM Customer Intelligence Appliance, retailers can integrate information from multiple retail channels and customer touch points to build a complete view of each customer. The more complete data set also enables retailers to produce more accurate models. Employing predictive analytics could help better anticipate future behaviors and optimize customer interactions. Keeping retail focused on the customer The multiplication of retail channels is empowering consumers, providing them with access to more information and new ways to research, compare, purchase and provide feedback on products. For retailers, the customer data produced through these multichannel interactions presents valuable opportunities to optimize marketing, merchandising and operations. The IBM Big Data Platform offers a comprehensive array of capabilities for addressing the growing volume, variety and velocity of available customer data. Whether they are enabling one, two or multiple retail processes by analyzing big data, retailers can implement IBM solutions that help protect existing investments and allow retailers to scale as needed. With IBM solutions for big data in place, retailers can build a foundation that supports a customer-centered, data-driven enterprise that helps them sustain a competitive edge. For more information To learn more about how IBM solutions help you capitalize on big data, visit: • •
  8. 8. © Copyright IBM Corporation 2013 IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America January 2013 IBM, the IBM logo,, BigInsights and InfoSphere are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at copytrade.shtml This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON- INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. Please Recycle IMW14679-USEN-00