Predictive Analytics Solve Retail’s Trickiest Problem: Customer Intent Posted on : Feb 17 - 2018

The notion of predictive data analytics as a game changer for retail is nothing new. We’ve heard this now for the past few years, with industry experts noting customer identification and personalization as the most obvious benefits.

It’s certainly a compelling prospect.

But when it comes to achieving that single view of customers – which is what would effectively drive that useful personalization – the rubber hasn’t really met the road. At least, not for most retailers that aren’t Amazon, not yet. As retailers look to implement the omnichannel experience, they’re still very much in the experimental phase, working to integrate online and offline information with the goal of really understanding their customers’ journey.

What we most urgently need, however, is a window into customer intent. What does the customer want? When are they likely to buy? What gets them into a store? Retailers have many questions but few answers. Offline data, specifically location data, gets us closer to resolving the most pressing of these conundrums primarily because it provides major contextual signals. Knowing, for instance, that a customer has visited a store is a significant signal indicating intent. If we can use this data, along with its online counterpart, we can accelerate our ability to deliver the right message at just the right time to the right person.

That’s what predictive analytics is really about: helping retailers forecast intent. If we can make smart, data-backed predictions on what customers are likely to do, and when, retail marketers can then deliver a true curated shopping experience.

Some predictive models are trained using visit data from trillions of events across millions of visits from thousands of locations. This kind of model makes predictions based on key data points about visitors including number of visits, days since the last visit, visit duration, number of locations visited, and more. Historical visit and behavioral data then helps the model refine its accuracy and deliver better predictions of future visits. This class of methodology delivers an actionable, analytical, and novel approach for retail marketers to identify and classify visitors, including understanding how often different groups of customers return.

Online marketers have used predictive analytics to predict where and why users click, but clicks and anonymous visitors don’t translate well into solid predictions on precisely who will visit a store and when. Predictive analytics that rely on location data, as well as online and other offline data, can really move the needle on this. If we know who customers are, and we leverage a large data footprint, we can help retailers specifically predict which shoppers will be visiting their commercial locations within a specific time period. View More