In its The Future of Retail series, Time Magazine discusses Big Data, brick-and-mortar retail, and RetailNext
Today the latest installment of Time Magazines, the Future of Retail series covers the topic of Big Data. After a brief not to the Big Data applications in electronic environments, the article moves to the problem of optimizing physical stores and Big Data’s role in that process:
But the vast majority of purchases – somewhere around 90% — still occur in a traditional retail setting. And brick and mortar retailers are looking towards big data to help them stay relevant.
One company that hopes to give traditional retailers the kind of analytic tools available to ecommerce firms is RetailNext. The firm has developed a computer program that uses a store’s security cameras to give managers all kinds of information about how consumers interact with the store. Using this program, RetailNext can show exactly how many customers are in a given store at a time, which parts of the store they explore, which specific items customers spend a lot of time perusing — and which they do not. RetailNext can combine this information with other variables like staffing levels, weather, product assortment and placement to determine what does and doesn’t boost sales. Luxury retailer Montblanc has used RetailNext’s services to improve its staffing levels and its product arrangement within its stores, increasing same-store sales 20% in the process. Retailers like American Apparel and Family Dollar have also successfully utilized RetailNext’s services to improve the layout of their stores and increase same-store sales.
In fact, it’s possible that traditional retailers could one day have a better understanding of their customers than ecommerce firms do. That’s at least what Tim Callan, Chief Marketing Officer at RetailNext, argues. He says that for customers in physical stores the “decision making capability is infinite, while there are only so many things they can do online.” In other words, given the right tools, a retailer can glean much more about a shopper from watching her peruse a traditional retail aisle than he can watching her click through links on a webpage. His firm is working on computer programs that can accurately distinguish — through video cameras — whether a shopper is male or female, and believes in the future this sort of technology could interpret customers’ facial expressions and other gestures that will help retailers understand why someone did or did not buy a certain product.
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This series continues with a description of the components that need to be in place to take full advantage of omnichannel analytics