Optimizing Click-Through in Online Rankings with Endogenous Search Refinement

摘要

Consumers engage in costly searches to evaluate the increasing number of product options available from online retailers. Presenting the best alternatives at the beginning reduces search costs associated with a consumer finding the right product. We use rich data on consumer click-stream behavior from a major web-based hotel comparison platform to estimate a model of search and click. We propose a method of determining the ranking of search results that maximizes consumers' click-through rates (CTRs) based on partial information available to the platform at the time of the consumer request, its assessment of consumers' preferences, and the expected consumer type based on request parameters from the current visit. Our method has two distinct advantages. First, we endogenize a consumer response to the ranking using search refinement tools, such as sorting and filtering of product options. Accounting for these search refinement actions is important since the ranking and consumer search actions together shape the consideration set from which clicks are made. Second, rankings are targeted to anonymous consumers by relating price sensitivity to request parameters, such as the length of stay, number of guests, and day of the week of the stay. We find that predicted CTRs under our proposed ranking are almost double those of the platform's default ranking.