This paper investigates the optimal information policy of an online platform (or multi-product firm) when ranking products in response to a consumer search query. The informativeness of rankings ranges from full information to full obfuscation, and consumers learn their match values with the products by engaging in costly sequential search. Invoking continuous match value distributions allows us to establish a novel result about consumer search. While consumers buy products with high match values and continue searching when they encounter low match values, they abort search without buying a product for intermediate ones. For a large class of distributions, the optimal strategy of a platform maximizing the probability of the consumer buying a product is to provide either full information or the smallest amount of information subject to the constraint that the consumer starts searching. As a result, platform and consumer welfare are either fully aligned or at odds with each other.
Janssen et al. (Sun,) studied this question.