Optimal Matchmaking Strategy in Two-sided Marketplaces

65 Pages Posted: 8 Mar 2020 Last revised: 13 Apr 2022

See all articles by Peng Shi

Peng Shi

University of Southern California - Marshall School of Business

Date Written: April 13, 2022

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); 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 the above 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 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 (April 13, 2022). 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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