Data Mining at Your Grocery Store

       One industry in which data mining is already heavily used is the grocery and supermarket segment. Many people have reward cards from their favorite food emporium, from Food Lion's MVP card to Harris Teeter's VIC card, and many would likely be surprised to learn that their cards do much more than provide a modicum of savings on items throughout the store. These cards, generally swiped at the point-of-sale or cash register terminal, usually provide discounts on marked items as well as sometimes collecting “rewards points” that can be redeemed for coupons or store credit. But from the perspective of the supermarket's parent corporation, these cards are capable of providing vast amounts of data that can be used for marketing decisions, advertising campaigns, and inventory planning.

       Tesco is a British supermarket chain that was ahead of the curve when it came to utilizing technology to gather information about their customer base. They launched their reward card system, called the Clubcard, in 1995, and it soon proved extremely valuable. Originally, they were only able to use the information sent in by customers when they requested their card, such as address and household members, which they used for direct marketing campaigns. As technology improved and more advanced POS systems were installed, they were soon able to glean much more information about their customer's buying habits and preferences. First, they used this data to create more effective direct marketing, both encouraging increased consumption of favorite products as well as attempting to get consumers to switch to products with a higher profit margin. Next, Tesco saw an opportunity to market other products and services to their customers. People who paid with a credit card would be more likely to purchase a store credit card, or another credit card from a financial partner. Individuals who visited a store in a remote location were deemed less likely to use public transportation, and their information was sold to car insurance companies. Finally, Tesco combined customer shopping trends with weather data, allowing them to streamline staff for slower days and be more prepared for busier times as well. Tesco eventually overtook their competitors and became the market segment leader in the United Kingdom.

           Safeway is another major supermarket chain that has made extensive use of data mining a large part of their business processes. Through information culled from sales figures in their UK stores, Safeway discovered, “that only eight of the 27 different types of orange juice in the stores sold enough to be profitable”(Rae-Dupree 1). They responded by cutting their orange juice offerings dramatically, recognizing that there wasn't consumer demand for that much variety. Similar research indicated that, “only a dozen of more than 100 types of cheese were profitable.” However, more advanced data mining techniques showed that Safeway's most profitable customers overall tended to buy the types of cheese with the lowest profit margins. They decided to keep selling all types of cheese, realizing that they didn't want to potentially lose any of their most valued clientele.

         Closer to home, well-known grocer Harris Teeter recently partnered with a company called Tresata to “provide (Harris Teeter) with next-generation predictive analytics software”(Johnsen 1). Tresata uses the Hadoop platform to crunch massive amounts of data, promising “the rapid monetization of big data.” Without going into specifics, Harris Teeter claims that this partnership will allow them to “provide its customers better value across its online, mobile, social, and brick-and-mortar channels.” What does the future hold for Harris Teeter and other supermarkets as more and more data is generated and collected? Will social media and mobile technology provide further opportunities for companies to utilize data mining to their advantage? How will consumers respond to the continued use of big data to analyze their shopping habits?

Citations

“Dominating With Data, Data Mining Emerges As The Future Of Business Analysis." San Jose Mercury News (California). LexisNexis Academic. Web. Date Accessed: 2013/09/10.

Johnsen, Michael. “Harris Teeter Takes Step Toward Monetizing Big Data.” Drug Store News. (July 31, 2013). 2013/09/10. http://drugstorenews.com/article/harris-teeter-takes-step-toward-monetizing-big-data.

Rae-Dupree, Janet. “Data Business; Retailers Strike Gold With A Mine Of Information...” Computing. (October 28, 2004). LexisNexis Academic. Web. Date Accessed: 2013/09/10.