Optimal Matchmaking Strategy in Two-Sided Marketplaces

Abstract

Online platforms that match customers with suitable service providers utilize a wide variety of matchmaking strategies; some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Finder), some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork), and others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these strategies in terms of their efficiency of matchmaking as proxied by the amount of communication needed to facilitate a good market outcome. The paper finds that the relative performance of these matchmaking strategies is driven by whether the preferences of agents on each side of the market are easy to describe. Here, “easy to describe” means that the preferences can be inferred with sufficient accuracy based on responses to standardized questionnaires. For markets with suitable characteristics, each of these matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4444 .