Big Data's Next Target: Brick-And-Mortar Retailers
RetailNext brings big data analytics to physical stores.
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Ecommerce sites have myriad tools to study their customers' buying habits and behaviors. But physical stores often are stuck in the pre-Internet era of video cameras and front-door traffic counters. RetailNext, a Silicon Valley company founded in 2007, is hoping to change that by bringing big data analytics to brick-and-mortar retailers.
Online stores are "able to optimize their stores to increase sales and cut out unnecessary costs, and make customer satisfaction better--all kinds of wonderful things by using metrics to guide the way their online stores work," said RetailNext chief marketing officer Tim Callan in a phone interview with InformationWeek.
"But the overwhelming majority of commerce is still taking place in physical locations," said Callan. He points to U.S. Department of Commerce data that shows more than 95% of American consumer purchases in 2011 occurred in brick-and-mortar stores.
RetailNext was founded to bring online-style, data-driven intelligence to physical stores. Its big data analytics products debuted in 2009, and today are used by more than 60 retail chains, including American Apparel, Brookstone, and Mont Blanc. They tie together a variety of input devices--everything from in-store video cameras, Wi-Fi routers, employee badges, point-of-sale systems, weather feeds, and even airport boarding pass information--allowing retailers to examine store data in various ways.
A RetailNext analytics system consists of an on-premise Linux server, or "appliance." The box is preconfigured by RetailNext, freeing retailers from running an elaborate setup procedure.
"They don't have to do anything. They don't even need a keyboard or a monitor. They take the server, plug it in, and they're good," said Callan.
The server gathers information from the devices connected to it, which most often includes video cameras and cash registers. Due to bandwidth limitations that vary by retail location, the server doesn't process full-motion video. Video feeds do show customers' movements and traffic flow inside a store, however.
Video and other data are sent from the store appliance to one or more central servers, depending on the number of stores involved. The information is then processed and analyzed, and retailers can access reports via a Web console or mobile app, or receive periodic reports via email.
Big data analytics come into play when retailers synthesize information from multiple devices to explore store operations in greater depth.
"We have customers who want to know where their employees are going in their stores. So what they do is assign the employees Wi-Fi trackers, which are little matchbox-sized boxes. The employees are instructed to carry these in their pockets while they're on the clock," said Callan.
"A camera can tell where humans are moving through the environment, but it doesn't know that Person A is an employee and that Person B is not," Callan said. "But if you put a Wi-Fi tracker on someone, now you can see that this (person) moving through the environment is actually one of your employees.
And then you can track and monitor them."
Stores use data generated by the RetailNext system for a variety of uses, such as deciding where to position store displays to maximize customer interaction.
Sporting goods retailer Gander Mountain, for example, found that by moving key displays away from high-traffic areas, shoppers were more likely to stop and examine the merchandise.
When these displays were in busy areas, "folks were more reluctant to stop because there were other people behind them, walking fast," said Callan.
Callan predicts that future analytics systems will be able to read facial expressions to tell if customers are angry or frustrated.
"We believe that as this technology gets built out, brick-and-mortar retailers, instead of having less information, are going to have much more useful information than online retailers have been able to enjoy," said Callan.
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