As Chain Trials Facial Recognition, Channel Assumptions FlipWritten by Evan Schuman
A major Russian convenience store chain, Ulybka Radugi, is now running a trial of facial recognition to choose digital in-store ads to be displayed and POS coupons to be offered. But as more chains start to seriously investigate the facial recognition potential, some of the fundamental CRM biometric assumptions are being challenged.
As a landmark facial recognition biometric study at Carnegie Mellon University established two years ago, retailers using existing security cameras can grab real-time images of shoppers and identify them within seconds using public databases. (The CMU study at the time limited itself to public Facebook images.)
There are three primary ways this can be used in retail. First option: it can be used to try and positively identify that customer by name, like the CMU study. It can then search for that name in its customer database.
Second option: it ignores a name for the time being, instead merely capturing the facial data points and noting what purchases the person attached to that face makes. Then, when the cameras catch that same face again (say, perhaps four days later), it will remember the prior purchases. It can either use that to send digital ads or coupons for that shopper or can merely note shopping patterns. (Note: 47 percent of people that we noticed buying Lime-flavored Diet Coke returned a few days later to buy Kleenex, red-colored hammers, Liquid Plumber and loaves of French bread. Please don’t ask us to explain it: we’re just closed-circuit cameras.)
The third option is the least interesting and it’s the approach that the Russian chain is trying: Using the images to guess gender and age-range and use that solely to send ads and promotions.
But such activities need not end with the same channel where they began. Once a shopper is identified in-store and is matched with a CRM profile—or they are identified anonymously in-store and a purchase profile of this unknown-person-with-this-specific-face is slowly built—that information can theoretically be married to data from that person’s desktop-shopping E-Commerce efforts or their tablet/smartphone’s M-Commerce efforts.
The question, then, is whether it has to start in-store. What if this hypothetical chain pushes some attractive incentives to get lots of customers and prospects to download its free mobile app? And buried in the terms & conditions is the right for the app to monitor images?
The next selfie or Snapchat that the shopper sends is captured and the facial data points are noted. The app itself may already have a name of the shopper (it probably does), but if not, the phone provides plenty of clues. And geolocation knows where the phone goes and certainly when it walks into one of the chain’s stores.
Here’s where it gets even freakier. Once the mobile app has identified the face of the shopper—and has linked it to whatever mobile shopper that customer has done—it can tell the in-store camera databases what to look for. When that shopper walks in, it can connect the mobile activity with any observed in-store activity.
And if the desktop device has a camera enabled for any purpose, there’s more potential. Invasive and creepy to the extreme, but potential nonetheless.