Predictive analytics comes to physical store visits

Predictive analytics is a buzzword being thrown around a lot these days. However, Euclid is actually doing something useful for retailers with predictive data on physical store visitation.

The company is capturing real-world store shopping patterns at scale and using machine learning to help retailers understand how often different groups of customers return. That, in turn, potentially enables them to do a better job engaging customers with segmented messaging.

Last week, Euclid released a new tool that analyzes billions of data points and is able to establish benchmarks for audience segments. “We look at historical behavior to understand patterns,” explained Brent Franson, Euclid CEO. “We determine how long [customers] stay and [how] often are they coming to the store.”

This allows traditional retailers to create distinct audience segments and predict their visitation frequency. Franson said the models are tested against actual in-store data and then refined over time. He says the company can now predict visitation with roughly 80 percent accuracy.

A primary use case is to find and re-engage customers who have lapsed or are about to lapse, to improve retention and lifetime value. For example, if a particular customer has come in monthly, and that pattern is broken, the customer may be in danger of lapsing. The retailer can then send out an offer or other incentive to visit the store.

By the same token, Franson told me, some audience segments are regular and don’t need to be given incentives to visit, potentially saving the retailer money in unnecessary offers or coupons. This capacity to understand and market differently to different audiences creates greater efficiency for retailers.

Euclid offers pre-packaged audience segments, but retailers can also create new segments using custom visitation data. Customer data is collected through user opt-ins to guest WiFi. However, individuals are not targeted, only audience segments.

“Retailers need to know the identity of all their customers to compete with Amazon,” said Franson. “You also need to have visibility into all their interactions with your brand.”

About The Author

Greg Sterling

Greg Sterling is a Contributing Editor at Search Engine Land. He writes a personal blog, Screenwerk, about connecting the dots between digital media and real-world consumer behavior. He is also VP of Strategy and Insights for the Local Search Association. Follow him on Twitter or find him at Google+.