Consumer Search and Filtering on Online Retail Platforms

Abstract

This article examines how the consumer’s search cost and filtering on a retail platform affect the platform, the third-party sellers, and the consumers. We show that, given the platform’s percentage referral fee, a lower search cost can either increase or lower the platform’s profit. By contrast, if the platform optimally adjusts its referral fee, a lower search cost will increase the platform’s profit. As the consumer’s search cost decreases, if the platform’s demand elasticity increases significantly, the platform should reduce its fee, potentially resulting in an all-win outcome for the platform, the sellers and the consumer; otherwise, a lower search cost will increase the platform’s optimal fee percentage, potentially leading to higher equilibrium price. Furthermore, we find that the availability of filtering on the platform will in expectation make consumers search fewer products but buy products with higher match values, and can either increase or decrease the equilibrium retail price. When filtering reveals only a small amount of the products’ match-value variations, it will benefit the platform, the sellers, and the consumers.