The devices we carry in our pockets continue to improve at recognizing and responding to our faces. From banking apps to lock-screen security systems, smartphones now support a whole host of features that use facial recognition algorithms to deliver personalized moments of interaction.
Consumer electronics retail represents the next frontier of facial recognition technology. Large department stores like Macy’s and Benetton are already reportedly using in-store cameras to aggregate data on millions of individual customers — while sites like Facebook are encouraging opt-ins to services that use face recognition to deliver personalized product recommendations.
The next logical step is to pair face recognition technology with digital displays to enhance and personalize the in-store shopping experience. Although big-box electronics retailers like Best Buy have denied that they use in-store facial recognition cameras, plenty of reasons exist for them to adopt these tools. Better customer recognition means more targeted in-store displays, more engaging store layouts and more personalized customer service interactions.
Here’s how brick-and-mortar consumer electronics retailers can leverage the power of facial recognition to personalize the shopping experience for their customers.
Mapping out the essentials
The first step is to determine the exact nature and amount of information that a store’s cameras will need to gather in order to generate personalized signage. It may not be necessary to capture actual images of customers’ faces in order to generate personalized signage and map out shopper behavior in a highly detailed way.
For example, some algorithms can detect and analyze specific facial characteristics without even needing to capture an image of a customer’s face. A camera using such an algorithm can use biometric and other data to determine that a given visitor is, for example, a 30-to-35-year-old female who is smiling. In many cases, this amount of data is all that’s needed to serve that shopper an ad for a smartphone that’s popular in her demographic.
Face feature data is often sufficient for feeding analytics that generate predictions about purchase patterns and other behaviors. For example, if a digital sign in the store serves two different ads for TVs to 100 men aged 45 to 50, that data can easily be correlated with point-of-sale stats to determine the results of the A/B test, in order to serve more engaging in-store ads to that customer segment next weekend.
For some electronics retailers, a single face feature recognition camera near the store entrance might be sufficient to capture the required data on a newly arrived customer and to connect that data with signage systems and customer databases. Other retailers, however, may find it necessary to install feature recognition cameras at points of sale, as well as at strategic locations throughout the store, in order to map out customers’ browsing patterns at a higher level of resolution.
Personalized ads are just the beginning
Although the most immediate and obvious application of face feature recognition is to inform more effective in-store ads, it can also provide a wealth of insights on customer behavior. When retailers combine these insights into a holistic view of their customer base, the resulting optics can inspire innovations at every level of the retail experience.
For example, face feature recognition systems can be used to map out an individual customer’s journey through the brick-and-mortar store, pinpointing walking patterns and dwell time at various locations. This information can be used to develop more targeted, impactful messages to deliver to that customer, driving purchase decisions and keeping certain products top-of-mind.
But in-store personalization is just the beginning. A retailer can then bring data captured by cameras into their customer analytics platform, integrate it with first-party website data about customers’ scrolling and click patterns, and generate a single cohesive map of a given customer’s online and in-store browsing behavior — connecting the dots on the customer journey across every touchpoint.
This unified holistic view can then be used to streamline and fine-tune sales floor layout, inventory, shelf displays, approaches by customer service representatives, and many other aspects of each micro-moment of interaction. And by tracking how long each shopper spends interacting with specific signage and product displays, retailers can assess customers’ responses to new products, in order to sharpen their sales tactics.
Although face recognition systems have generated their share of controversy — especially now that customer privacy and data protection are hot-button issues in US and EU regulatory circles — the benefits in terms of customer experience make these technologies well worth investigating.
In fact, facial detection systems can help close the competitive gap between brick-and-mortar electronics retailers and their online counterparts, by helping retailers A/B test all points of interaction as easily as if they were online ads. And most importantly of all, these technologies help retailers deliver memorable personalized shopping experiences, serving up those surprising moments of delight that drive impulse purchases and build long-term brand loyalty.