As marketers are discovering, mobile location data can be used for many purposes. Among them, according to inMarket, is predicting retail store closures.
The company’s SDK (software development kit) is integrated into 700 apps, and it sees more than 50 million active mobile devices in the US. One of the data sets the company captures is the frequency of retail store visits. This can be translated into a loyalty metric and benchmarked by retail segment.
For a new retail report, the company tracked non-grocery store visits between March and May and assigned each store a score based on visitation frequency: “The average loyalty score for all non-grocery retailers in Spring 2017 was 1.45.”
Top and bottom 10 retailer rankings by customer loyalty (visits)
Those on the bottom 10 list on the right have announced layoffs and store closures. Those on the top 10 list, on the left, are expanding. InMarket says that the data are predictive of future layoffs and closures. Accordingly, Nine West and Disney should be next.
Customer loyalty (visitation frequency) is obviously a function of a number of variables. However, this kind of data can be used by investors to predict quarterly earnings or by retail corporate as an early warning signal of trouble. Indeed, store-visits data has utility beyond attribution.
It can also be used to measure and benchmark performance of individual stores across chains to find outliers and determine which stores to emulate or close. Foursquare, PlaceIQ and others have also promoted the benefits of location data for a broad array of uses beyond attribution.
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Author: Greg Sterling
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