Subscribe to the Blog Newsletter

How Facial Recognition Tech Will Lead to More In-Store Intelligence

Companies say converting more leads to customers will be their top priority over the next year, according to recent research. This is certainly a worthy goal, but it begs a natural next question — how do you keep customers once you have them?

This conundrum is one retailers have been trying to solve for decades. Thanks to new technologies, that’s becoming easier to do in 2017. Recently, Walmart announced a plan to bring Minority Report-style facial recognition technology from the big screen to retail stores to identify and intervene with unhappy customers at scale.

Where Facial Recognition Technology Provides the Most Value

Walmart may not have been top-of-mind when it comes to innovation in the past, but a number of significant tech innovation pushes this past year demonstrate that this legacy brick-and-mortar behemoth is committed to evolving with, and perhaps leading significant change.

Walmart’s stated goal in implementing facial recognition is to understand customer sentiment in real time so staff can provide support to alleviate situations that could damage a customer’s experience around a single transaction, as well as their longer-term loyalty.

But the potential benefits are much broader than simple triage. Here are three scenarios where facial recognition technology can earn retailers greater customer feedback in-store, as well as what retailers can do to productively implement that information.

Understanding the Journey

With facial recognition technology, retailers can examine touch points and flow on the journey purchase and determine how each is impacting the customer experience, including spend, whether positive or negative.

In-store shoppers have many interactions that collectively determine their overall experience. That’s why retailers must work to understand if every single touch point — interactions with sales associates, products, environment, technologies etc. — is working well, and what can be improved if it’s not.

For instance, if shoppers typically leave a retailer’s “Health and Beauty” section more frustrated than when they entered, this indicates issues with experiences specific to that department. Granular insights like these will help retailers make small improvements across their overall in-store customer experiences. Armed with this understanding, human workers can be trained to provide specific types of assistance at various touch points to improve or enrich that specific experience.

Personalizing the Experience

Facial recognition by itself has interesting and helpful applications. However, the real promise lies in using this data in concert with other data sources and analytics technologies to gain a comprehensive understanding of individual customers.

One of the most talked-about buzzwords of the last 18 months has been personalization. And while application of this concept has been used primarily by digital marketers to target offers and content, a study earlier this year confirmed that consumers value personalization during purchase and service interactions above marketing/advertising moments, which they ranked least important of the three.

A future scenario might be leveraging facial recognition to understand when a customer had entered a store, and then harnessing the plethora of other customer and contextual information to serve up a personalized and very meaningful experience, based on past interactions and nimble enough to read and analyze in-store behaviors and sentiment. This stream of real-time “customer experience intelligence” could power everything from targeted offers based on same-day comparison shopping from a customer’s mobile device, to individual customer dossiers to support more helpful associate-to-customer interactions.

Imagine a store manager receiving an alert that a VIP customer had entered the store, a record of her recent browsing history of both your website and your competitors’, her recent purchases, as well as social reviews and feedback she’s given about your brand — along with past and current sentiment. Instead of extending a generic greeting, the technology would augment the floor staff’s expertise to create a very different customer experience, indeed.

Anticipating their Needs

The ultimate promise of today’s emerging technologies and analytics are moving beyond responding to, and instead anticipating, customers’ needs, wants and opportunities for delight. With enough data and time, predictive algorithms can find patterns in past behaviors, and make an educated guess at what customers, and metrics, will do in the future. This allows retailers to avoid drastically bad experiences by preventing the conditions that cause them in the first place. It also allows brands to identify elements of the experience that drive the most positive business and relationship outcomes, and proactively build those into more places along the customer journey.

One national brand we worked with brought together individual store sales data and goals, with customer feedback and sentiment. We ran predictive models that identified which locations would miss sales goals, and exactly why — by location. Armed with this information, each store manager could focus their team on bolstering the experience in ways that both make customers happier, and get them to their monthly sales goals.

In the past, predictive models were run almost exclusively on structured data, and netted a respectable, but still wanting 60% to 70% accuracy rate. By incorporating unstructured human data from facial recognition software, social reviews and survey comments, accuracy can reach well into the 90% range.

Just like any new technology, facial recognition won’t be a silver bullet for understanding and interacting with today’s born-digital customers. However, applied thoughtfully, and in concert with a broader set of data and technologies, facial recognition is set to become a very powerful lens into one of the most elusive and important questions standing between buyers and sellers: Why. Why do they love this and shun that? Why didn’t they purchase? Why did they choose our competitor over our brand? Why do they come back over and over again? Why did they spend more this time than last? Every tool retailers can bring to the solving of this mystery is priceless.

Change Region

Selecting a different region will change the language and content of inmoment.com

North America
United States/Canada (English)
Europe
DACH (Deutsch) United Kingdom (English) France (français)
Asia Pacific
Australia (English) New Zealand (English) Singapore (English)