Leveraging Loyalty Programs Using Competitor Based Targeting

Abstract

Loyalty programs (LPs) are widely used by firms but not well understood. These programs provide discounts and perks to loyal customers and are costly to administer, but produce uncertain changes in spending patterns. We use a large and detailed dataset on customer shopping behavior at one of the largest U.S. retailers before and after joining a loyalty program to evaluate how behavior changes. We combine this with detailed spatial data on customer and store locations, including the locations of competing firms. We find significant changes in behavior associated with joining the LP with a large amount of heterogeneity across customers. We find that location relative to competitors is the factor most strongly associated with increases in spending following joining the LP, suggesting that the LP’s quantity discounts work primarily through business stealing and not through other demand expansion. We next estimate a set of predictive models to test what variables best predict how spending will change after joining the LP. We use high-dimensional data on spatial relationships between customers, the focal firm’s stores, and competing stores as well as customers’ historical spending patterns. These models are used to test whether past sales data reflecting customer’s vertical value to the firm or spatial data reflecting customer’s horizontal vulnerability are more predictive of post-LP spending increases. We show how LASSO regularization estimated on complex spatial relationships are more predictive than are models using past sales data or simpler spatial models. Finally, we show how firms can use customer and competitor location data to substantially increase LP performance through spatially driven segmentation.

Keywords: loyalty programs, predictive analytics, spatial models, retail competition, LASSO estimation, price discrimination