Customer Loyalty and the Persistence of Revenues and Earnings
49 Pages Posted: 28 Jan 2021
Date Written: December 7, 2020
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 2.5 billion visits to over 1 million retail locations belonging to 288 U.S. companies, we 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. However, despite the higher persistence of revenues and earnings when customer loyalty is higher, revenue and earnings response coefficients are not higher, which suggests that investors do not immediately and fully incorporate the implications of customer loyalty into prices. We also show that analysts’ forecasts do not fully account for customer loyalty, leading to predictable forecast errors, particularly when companies do not provide guidance. Our results illustrate the value of customer data to firms, investors, and analysts in understanding the conditions under which revenues and earnings are sustainable.
Keywords: customer loyalty, revenues, earnings persistence, big data
JEL Classification: M41, M31, G14
Suggested Citation: Suggested Citation