Online Search and Optimal Product Rankings: An Empirical Framework

Abstract

We study the problem faced by an online retail platform choosing product rankings in order to maximize two distinct goals: consumer surplus and revenues/profits. To this end, we specify a version of the Weitzman sequential search model in which search reveals a consumer’s idiosyncratic taste for the product as well as vertical dimensions of its quality, and we derive convenient expressions for consumer surplus and revenues. To optimize consumer surplus, platforms should facilitate product discovery by promoting “diamonds in the rough,” that is, products with a large gap between the utility they deliver and what consumers expect based on the presearch information. By contrast, to maximize static revenues, the platform should favor high-margin products, potentially creating a tension between the two objectives. We develop computationally tractable algorithms for estimating consumer preferences and optimizing rankings, and we provide approximate optimality guarantees in the latter case. When we apply our approach to data from Expedia, our suggested consumer surplus–optimizing ranking achieves both higher consumer surplus and higher revenues relative to the Expedia ranking—delivering a Pareto improvement—and also dominates ranking the products in order of utility, which is intuitive but fails to leverage information on what consumers know presearch.