Enhancing the In-Store Experience through Facial Recognition Software

The best way to understand what eyeQ is trying to do is to com­pare it to a conventional digital display. Unless it’s a specialized store, that display can’t show shoppers a significant portion of a store’s assortment, so it might instead show a variety: lots of fast fades and dissolves, and maybe some panning video of the aisles or the escalators. The idea is to keep it moving, so if a shopper stands there long enough he or she will see something of interest

The digital sign in eyeQ’s system is connected to a cam­era, a lot of proprietary facial recognition software, and—via IBM’s cloud service—Watson. When a shopper stops to look at an eyeQ digital sign, the sign looks back at him or her. Then, based on facial features and appearance, eyeQ tailors its con­tent to the viewer’s age and gender. Men between the ages of 35 and 45 might see suits, top-end cameras, or fly fishing rods; women between the ages of 18 and 30 might see jewelry or clothes.

“When somebody approaches the screen,” says Doug Bain, the chief revenue officer of eyeQ Insights, “the screen is already aware of the baseline information—the age and gender of the person—and is ready to make product recom­mendations.” There’s more, though: If the shopper gives the system his or her Twitter user name, Watson can capture the most recent 200 tweets, run them through its natural language processing capabilities, and slot him or her into one of a selec­tion of basic personality types. Not only can the system change the products being recommended but it can also change the whole experience—background colors, video, music, whatever it’s got.

The most recent version of eyeQ’s system can even register emotion. “It’s categorizing the person’s expression as happy, sad, angry, surprised—and to what degree,” says Bain. “Is the expression changing dramatically when he or she gets to a certain point in the video, or a certain page, or when he or she touches the screen? It’s another data point to determine the effectiveness of the content.”

As it develops, eyeQ has been working closely with retail marketing agency TPN. Manolo Almagro, TPN’s senior man­aging director for digital and retail technologies, notes that the system, even without a consumer opting in or providing her or his own information, can identify a repeat visitor by the unique media access control address her or his mobile device sends out to find available Wi-Fi. “We don’t know the individual’s name,” he says, “but we know it’s the same 40-year-old woman who was in on Tuesday.”

Almagro adds that further development of the tech­nology requires some care. “I know you’re interested in a certain type of product, I know your age and gender, and I know your personality type. This is a level of depth we’ve never had before, and we have to ask ourselves, ‘How much personalization is too much?’ I want to give enough to make the experience more convenient for you, but not so much that it becomes creepy. That’s a kind of fine line we have to walk.”

At the moment, one retailer has a prototype in place, and several consumer package goods companies are experi­menting with prototypes in the retail space. Shortly, eyeQ expects to have a prototype implementation robust enough to start to generate some real feedback. Bain says early signs are encouraging. “We have data that demonstrate that increased engagement with the system does indeed mean increased sales.”

Source: Barry Berman, Joel R Evans, Patrali Chatterjee (2017), Retail Management: A Strategic Approach, Pearson; 13th edition.