Retail is changing faster than almost any industry. Brands are investing in better shopping experiences both online and in stores (think shoppable Instagram posts, pop-up shops and checkout-free stores). Meanwhile, consumers use more channels than ever to discover and buy products, from social media to apps to voice devices. But while brands have access to more customer data than ever, many consistently fail to leverage the right data to reach customers.
When combined with demographic data, location data can yield powerful insights about who your consumers are and what they’re interested in, which can inform strategies about how to best reach them. This is more important than ever considering that consumer brands that create personalized experiences are seeing revenue increases of 6 to 10 percent — two to three times greater than brands that don’t.
To illustrate just how much retailers can glean from location data, we looked at Whole Foods data to get a better understanding of the chain’s “average shopper,” uncovering insights that could be used to develop personalization and targeting strategies.
Acquired by Amazon last June, Whole Foods is the grocery store known for selling natural foods and often gobbling up your “whole paycheck.” With that reputation, you might picture someone who is healthier and wealthier than average. On the contrary, you might also expect to see less affluent shoppers giving Whole Foods a shot in response to the highly anticipated price cuts promised by Amazon. Looking at real-world behavior patterns, we tested those assumptions.
The Whole Foods example shows how location data can provide the kind of granular insight brands need to know about their customers to design the shopping experiences they want.
We looked at the foot traffic patterns of more than one million individuals at Whole Foods stores across the U.S. between November 2017 and January 2018 and compared them to a group of baseline users who did not visit Whole Foods stores, but live in markets where the chain exists. All data comes from mobile devices, is fully anonymized, and is based on actual (not inferred) observations. We used two tools — Observation Graph and Geopulse Insights — to gather these insights.
To learn more about Observation Graph and Geopulse Insights, contact our team.