How Big Data and Analytics Are Transforming In-Store Experience for Retailers

  |  April 9, 2013   |  Comments

Create a unique shopping experience where customers derive both value and pleasure from visiting physical storefronts.

Last week Google indicated that it would provide same-day delivery service from local retail stores. Joining Amazon, eBay, and a host of other startups providing similar services, Google will attempt to eliminate the last compelling differentiator for a traditional brick-and-mortar store - instant gratification. With the ability to browse, order, and now obtain within 24 hours products from the comforts of your home, how does that impact traditional retail storefronts? Is this the death knell that we have long been hearing about?

Not really. Recent studies have shown that, for a majority of categories, consumers still prefer to use physical stores for both researching and purchasing products. And since most retailers now employ a multi-channel strategy, physical storefronts have to be an integral part of the overall shopping journey for their customers. The key is to create a unique shopping experience where customers derive both value and pleasure from visiting physical storefronts. We have seen leading digital e-tailers employ big data analytics to create a superlative experience for their users. We are now beginning to see retailers take a leaf from that playbook and emulate similar capabilities in their physical stores. They are:

1. Delivering experiences tailored to individual shoppers. Personalization of content based on customer behavior has been the hallmark of successful digital media e-tailers. Online stores seem to "know" their customers better - correctly identifying them by name, remembering their last few purchases, or dynamically customizing the storefront to showcase relevant products. Retailers are now enabling their physical storefronts to provide a similar level of personalization.

Consider the scenario where you walk into your favorite apparel store. Surveillance cameras mounted on the door immediately detect who you are and instantly beam identifying information to the in-store sales associates. An app on the sales associate's tablet then correlates all of your shopping characteristics such as loyalty, past purchases, cross-channel preferences, service incidents, and social expression and provides a consolidated view. With this information, a sales associate walks up to you as you are entering the store, greets you by name, and enquires about your most recent purchase. This level of personalized attention dramatically alters an individual's in-store shopping experience…and it's not that far-fetched. Breakthroughs in new facial recognition technology combined with real-time analytics are now enabling brick-and-mortar retail storefronts to provide greater levels of personalization.

  • Almax has created "smart mannequins" that have cameras for eyes and analyze shoppers' faces to detect age, gender, ethnicity, and a variety of other characteristics. A luxury goods retailer is currently piloting their technology to better assess their marketing messages, potentially discover new target groups, and tune their in-store displays.
  • NEC has built a similar system called NeoFace that can alert staff when a loyal customer or a big spender walks into a store.

2. Guiding shoppers to discover associated products. Automated guided selling was another aspect of the shopping experience that was unique to digital channels. We have seen intelligent algorithms make real-time product recommendations based on what was in a customer's shopping cart. Retail storefronts are now trying to emulate similar capability by using interactive display units and kiosks that can intelligently assess the customer and a product under consideration to make recommendations that enhance the shopping experience.

  • Kraft has partnered with Intel to create an in-store kiosk that leverages video analytics to assist shoppers with product recommendations based on their physical characteristics and past purchasing history.
  • IBM helped German retailer METRO boost customer satisfaction by 18 percent with intelligent dressing rooms that would detect the shopper's current apparel selection and make appropriate recommendations for accessories.

3. Tracking and analyzing shopper behavior across visits. One of the advantages of digital commerce was that all user activity was measurable. One could measure shopper behavior via clicks, page views, time spent per page, and the path traversed from landing to conversion. This enabled online retailers to optimize page design, placements, and tailor promotional messages. Big data analytics is now helping retail storefronts collect and analyze fine-grained shopper visit data.

  • RetailNext, provider of big data analytics solutions for physical storefronts, collects shopper data from a variety of sources such as surveillance video cameras, RFID tags, POS systems, etc. Retailers are currently using this system to collect about 10,000 data points per store visitor, translating to trillions of analytic data points. Retailers such as American Apparel use this technology to optimize store layouts, fixtures, staffing, and even product offering.
  • Euclid Analytics provides a similar solution, but relies exclusively on Wi-Fi pings emanating from shoppers' smartphones to track their in-store location. They then analyze this data to measure engagement rates, traffic conversion paths, and identify weak points in the shopping experience.

The benefits that big data and analytics provide are significant and furthermore blur the divide between online and physical worlds. However, while retailers are applying these practices to optimize in-store experience they must also address privacy and security concerns. The mechanisms employed for data collection in the physical world are different from what we are used to in the digital world and that raises genuine concerns among consumers. Physical retailers will have to be transparent in their data collection practices, respect individual's privacy concerns, and honor customer preferences.

Image on home page via Shutterstock.



Krishnan Parasuraman

Krishnan Parasuraman is responsible for driving revenue growth across IBM's digital media clients through the use of big data technologies. In his role, he works very closely with some of the largest marketing service providers and digital ad networks in an advisory capacity, driving big data solution architectures and best practices for management of Internet scale analytics. Krishnan drives the technology strategy for IBM's big data product portfolio and is an authority on the use of technologies, such as Hadoop and massively parallel data warehousing technologies, towards solving analytical problems in the online digital advertising, customer intelligence, and real-time marketing space. He speaks regularly at industry events, writes for trade publications, and is the author of the book, Harness the Power of Big Data.

Prior to his current role, Krishnan has worked in research, product development, consulting, and technology marketing roles across multiple disciplines within information management. He has enabled marketing and customer analytics solutions for large media and consumer-focused organizations like Apple, AOL, Yahoo, Microsoft. and Motorola. He holds an M.S. degree in computer science from the University of Georgia.

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