Customer Loyalty and the Persistence of Revenues and Earnings

We use big data on customer shopping patterns to explain variation in the persistence of a company’s revenues and earnings. Using GPS location data from customers’ mobile devices that encompasses nearly 2.9 billion visits to over 1 million retail locations belonging to 285 U.S. companies, we develop new measures of customer loyalty and find that revenues and earnings are more persistent when customers are more loyal. Specifically, revenues and earnings are more persistent when customers (a) have more regular shopping patterns, (b) are repeat rather than one-time customers, (c) shop during the week rather than on weekends, and (d) spend more time in the store. We also explore implications of customer loyalty for managers, investors, and analysts. We find that capital investment increases with customer loyalty, consistent with customer loyalty being associated with less uncertain returns to capital investment. Further, we find that investors and analysts do not immediately incorporate customer loyalty into trading decisions and forecasts. Our results illustrate the value of customer data for understanding the conditions under which revenues and earnings are sustainable