Predictive analytics comes to physical store visits
Euclid is tracking audience behavior patterns to help retailers bring shoppers back into stores more efficiently.
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.”
Marketing attribution and predictive analytics: A snapshot
What it is. Marketing attribution and predictive analytics platforms are software that employ sophisticated statistical modeling and machine learning to evaluate the impact of each marketing touch a buyer encounters along a purchase journey across all channels, with the goal of helping marketers allocate future spending. Platforms with predictive analytics capabilities also use data, statistical algorithms and machine learning to predict future outcomes based on historical data and scenario building.
Why it’s hot today. Many marketers know roughly half their media spend is wasted, but few are aware of which half that is. And with tight budgets due to the economic uncertainty brought about by the COVID-19 pandemic, companies are seeking to rid themselves of waste.
Attribution challenges. Buyers are using more channels and devices in their purchase journeys than ever before. The lack of attributive modeling and analytics makes it even more difficult to help them along the way.
Marketers continuing to use traditional channels find this challenge magnified. The advent of digital privacy regulations has also led to the disappearance of third-party cookies, one of marketers’ most useful data sources.
Marketing attribution and predictive analytics platforms can help marketers tackle these challenges. They give professionals more information about their buyers and help them get a better handle on the issue of budget waste.
Read Next: What do marketing attribution and predictive analytics tools do?
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
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