Trade-Offs Between Ranking Objectives: Reduced-Form Evidence and Structural Estimation

Abstract

Online retailers and platforms typically present alternatives using ranked product lists. By adjusting the ranking, these platforms influence consumers’ choices and, in turn, conversions, platform revenues, and consumer welfare. In this paper, I study the trade-offs between ranking algorithms that target these different objectives. First, I highlight and provide reduced-form evidence for a key factor shaping these trade-offs: cross-product heterogeneity in position effects. To quantify the effects of different rankings, I then develop an empirical framework based on the search and discovery model of Greminger (2022). For this framework, I show that the ranking that maximizes conversions also maximizes consumer welfare, implying no trade-off between these two objectives. Moreover, I develop and test a heuristic ranking algorithm to maximize revenues. Finally, I estimate the model and compare the effects of the rankings developed for the different objectives. The results highlight the effectiveness of the different rankings and reveal that the proposed heuristic to maximize revenues also increases consumer welfare, suggesting that the trade-off between revenue maximization and consumer welfare also is limited.