Optimal Matchmaking Strategy in Two-sided Marketplaces

65 Pages Posted: 8 Mar 2020 Last revised: 20 Jul 2021

See all articles by Peng Shi

Peng Shi

University of Southern California - Marshall School of Business

Date Written: June 4, 2021

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 Services); some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork); 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. We find that the relative performance of these strategies is driven by whether the preferences of agents on each side of the market is easy to describe or satisfy. ``Easy to describe'' means that the preferences can be readily captured in a short questionnaire, and ``easy to satisfy'' means that an agent has high preferences for many potential partners. For markets with suitable characteristics, each of the above matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms.

Keywords: market design, online platforms, two-sided matching, communication complexity

JEL Classification: D47, D83, C78

Suggested Citation

Shi, Peng, Optimal Matchmaking Strategy in Two-sided Marketplaces (June 4, 2021). USC Marshall School of Business Research Paper, Available at SSRN: https://ssrn.com/abstract=3536086 or http://dx.doi.org/10.2139/ssrn.3536086

Peng Shi (Contact Author)

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

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