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. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3536086
Peng Shi is an Assistant Professor of Data Sciences and Operations in the USC Marshall School of Business. His current research focuses on optimization in matching markets, with applications in school choice, public housing, organ allocation, and online marketplaces. His research has won multiple awards, including the MSOM Responsible Research in OM Award, the MSOM Service Management SIG Best Paper Award, the ACM SIGecom Doctoral Dissertation Award, the INFORMS Public Sector Operations Best Paper Competition, and the INFORMS Doing Good with Good OR Student Paper Competition. Prior to joining USC, he completed a PhD in operations research at MIT, and was a post-doctoral researcher at Microsoft Research.