Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach

Optimizing Revenue in Two-Sided Online Markets through Strategic Quality Selection and Information Disclosure Ensuring profitable operations in two-sided online marketplaces demands an astute analysis of seller quality and strategic information sharing with buyers. A recent paper, titled “Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach” by B. Light, R. Johari, and G. Weintraub delves into the nuances of this operation. The authors explore the challenge that platforms encounter in deciding which sellers to allow and how much quality information to share with buyers in order to enhance platform revenue. Utilizing two distinct two-sided market models, the paper unveils conditions under which adopting straightforward information structures, such as excluding certain sellers or not differentiating among participating sellers, proves to be a revenue-maximizing strategy. This study utilizes a constrained price discrimination problem to reveal specific strategies platforms can use to adjust information structures in diverse market scenarios, providing insights for digital platforms aiming to navigate the marketplace more effectively.