Customer‐centric category selection for mobile and print promotions in loyalty reward programs

Abstract

In their loyalty reward programs, large retailers adopt category‐specific targeted promotions as an effective means to increase shoppers' basket values. However, neither the literature nor observed marketing practice provides category selection methods that maximize the promotion's profitability. Hence, we provide a predictive customer‐centric category selection approach based on the return on marketing investment measure that accommodates both fixed and variable promotion costs, captures cherry‐picking effects, and encompasses the retailer's entire category assortment. We use a real‐world promotional data set from a leading German retailer to show that our approach predicts customer responses to these promotions with high out‐of‐sample accuracy tested over time and also across promotion frequencies. We find that the most promising categories in mobile promotions maximize cross‐category profits to curtail cherry‐picking and boost the sales of nonpromoted items—that is, the profitability‐driving part of the profit uplift. In contrast, the different cost structure of print promotions requires that categories achieve a high (but not too high) redemption rate, as cross‐category profit declines, in order to recoup customer contacting costs. Our benchmark analysis reveals that current marketing practice fails to hit the profitability functions' “sweet spot” and can even work against the retailer by producing negative returns.

Introduction of the special issue on Analytics and Operations of Online Retailing

“…von Mutius and Huchzermeier (2021) study the problem of optimizing category selection for mobile and print promotions using a data set from a leading German retailer. They show that there is a difference in planning for these two promotions, and that the current marketing practice can sometimes backfire.…”