Progressive Grocer

SEP 2016

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September 2016 | progressivegrocer.com | 153 Analytics can help grocers see where technology can improve business efficiencies and reduce costs. It can help them identify the in-store experiences that will be enhanced by technology." —Michael Gorshe, Accenture Still Catching Up? Not everyone agrees with such a widespread and sophisticated view of in-store analytics in the grocery channel. "In-store analytics is still in a fairly primitive stage in the grocery industry," counters Rajeev Sharma, founder and CEO of VideoMining, a State College, Pa.-based provider of in-store be- havior analytics. "For the most part, the industry still uses manual and subjective 'shopper research' techniques for insights using self-reported data from interviews, shop-alongs and manual observa- tion. New technologies such as video analytics and mobile phone tracking have recently been intro- duced to provide a scalable and reliable basis for in-store analytics." David Ciancio, senior consultant for cus- tomer strategy at Cincinnati-based consultancy DunnhumbyUSA, offers a harsher assessment of in-store analytics in the grocery class of trade. He claims the industry has been relatively late to adopt analytics except with regard to the supply chain, despite the fact that grocers were early pioneers in data collection via UPCs and loyalty cards. "Many grocers are using some level of analytics for pricing and promotions, but almost none are using analytics to really improve the customers' shopping experience," he asserts, ticking off his criticisms: Many promotions are still confusing to customers and unprofitable for the merchants. Value perception is a challenge for most grocers, despite the analytics. Assortment strategies are unclear to customers. Marketing is imprecise, wasteful and not truly personal. "e evidence is clear that few operators are differentiating based on analytics, and even fewer are winning true customer loyalty," Ciancio says. "Overall, grocers have yet to fully exploit all with U.S. headquarters in New York, lists several ways that Big Data technologies allow retailers to leverage their own internal data in a way that was nearly impossible before: Grocers can cluster stores according to shop- per spending patterns by analyzing point-of-sale (POS) data at a much higher level of granularity than with syndicated data. Grocers can track consumer sentiment and preferences from online chatter in real time, instead of having to rely on 2-month-old panel data. Store audits can now be collected robotically, decreasing the cost of data collection and providing a treasure trove of insights about category adjacen- cies, display effectiveness and shelf configuration. Advances in data science make it possible to connect shopper, product and store information for a truly comprehensive view of the business. Grocers can now tell better than ever before what consumer profile is most likely to shop a given basket or to respond to a given assortment. "ere has always been some form of in-store analytics originating from forecasting how much stock they needed to have in their store each week," explains InContext's Scamehorn. "But the tools to do that have evolved with frequent-shop- per databases and Big Data applications to attempt to uncover less obvious relationships among the products being sold. is area is growing very quickly. It is becoming something that all retailers are embracing at varying levels of sophistication."

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