Consumer Search and Filtering on Online Retail Platforms

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. The authors show that, given the platform’s percentage referral fee, a lower search cost can either increase or decrease 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 consumers; otherwise, a lower search cost will increase the platform’s optimal fee percentage, potentially leading to higher equilibrium retail prices. Furthermore, the availability of filtering on the platform will in expectation induce consumers to search fewer products but buy products with higher match values, and filtering can either increase or decrease equilibrium retail prices. When filtering reveals only a small amount of the products’ match-value variations, it will benefit the platform, the sellers, and the consumers. This article shows that the effects of filtering and those of a decrease in search cost are qualitatively different.