In 2012, InformationWeek published a story that suggested physical retailers may be big data’s next big target. Three years later, big data is still a concept that most brick-and-mortar businesses still assume is not for them. But, as the article pointed out, the vast majority of commerce still takes place in physical stores. So it stands to reason that real-world business owners could use big data to minimize costs, increase sales and manage customer databases. Here is look at how big data is penetrating the physical retail world.
Big Data Simplified: The 42 Model
San Francisco-based big-data technology firm 42 was formed a year after the InformationWeek article was first published. The mission of 42 is to bring a “simplified” version of big data to the retail world. According to TechCrunch, 42 takes raw, point-of-sale data and converts it into actionable research. They accomplish this by making sense of data that retailers already have, but don’t necessarily understand.
Their service enables owners of physical businesses to create reports for presentations or internal analysis, as well as to track revenue performance. The company provides custom recommendations and helps businesses identify their top customers, while analyzing which customers should get the bulk of the business’s focus. Their big data analyses can also help with merchandising and regional analysis, as well as with managing supply chains, from warehousing to the creation of shipping labels. To get started, retailers simply upload their raw data in any format and 42 takes it from there.
Mining Mobile Devices for In-Store Big Data Analysis
The missing link between big data and brick-and-mortar retailers may have been the mobile revolution. That link, of course, is no longer missing.
“Clean data” is data that was harvested from the phones, tablets and other mobile devices of shoppers in stores. The data is considered clean because it contains no user-specific information. Using proximity marketing techniques, clean data is pulled from wifi and Bluetooth signals to figure out where a customer went in a store, how long they stayed there and whether or not they accept digital offers.
Because the signals are anonymous and no personal information is gathered, the technique ensures customer privacy while collecting, analyzing and acting on big data in real time.
Another analysis shows that since there is no way for physical retailers to compete with the data-collection ability of e-commerce sites, retailers are using shoppers’ own smartphones to log them into a database as soon as they enter the store. This strategy correlates a shopper’s mobile signal with the radio frequency attached to a product they are examining. This information is beamed to a salesperson on the floor, who is armed with a tablet containing all the pertinent information information that was gleaned from real-time big data.
For years, big data providers have been looking for ways to crack the riddle of brick-and-mortar retailers. One model is to simply upload a business’s raw data to a server for analysis from a company like 42. But mobile devices are at the heart of a collection-and-analysis approach that works in real time. There is no doubt that e-commerce has the edge when it comes to data collection, but that doesn’t mean there isn’t a place for big data in the real world.
