Online platforms (and multi-product firms) generate lists of products (or services) in response to consumer search queries. In ranking these products, platforms draw on their vast amount of information about past consumer search behavior and their purchase history. In “Optimally Informative Rankings and Consumer Search” (joint with Maarten Janssen, Marcel Preuss and Cole Williams), we investigate how much of their pertinent information platforms convey through their rankings of products.
When consumers engage in costly search and expect to find some products they like better and others they like less, they are updating their expectations about the remaining alternatives whenever they inspect a product. As a result, three scenarios ensue: 1) If they like a product very much, they immediately buy. 2) If they strongly dislike a product, they become more optimistic about the remaining alternatives and continue searching. 3) If however, the consumer is fairly indifferent about a product, they may neither buy it nor continue to search as their experience does not induce sufficient optimism about the remaining products. In turn, they abort search altogether without buying a product.
Scenario 3) is a novel finding about consumer search with learning and has meaningful implications for platforms and their regulators. Understanding optimal consumer search behavior, we derive the optimal platform ranking of two products, one of which promises a higher value for the consumer (in expectation) and one a lower value. The more informative the platform’s ranking, i.e., the higher the probability it puts the higher-value product in the first position of their ranking, the higher the likelihood the consumer (inspects and) buys the first product. However, if the consumer does not like the first product, they are less likely to inspect the second the more informative the ranking, i.e., the less likely the product in the second position is the higher-value product.
It turns out that the platform optimally chooses to obfuscate their ranking to increase the probability of the consumer to inspect both products when consumer search cost (i.e., their implicit cost of inspecting another item) is low, but to provide a fully informative ranking to increase the probability the consumer buys the first product if the consumer’s search cost is high. Interestingly, the platform provides either less information than would be necessary to induce search of the second product, or more information than necessary to ensure the consumer’s participation in search in the first place. An intriguing result of our findings is that platform and consumer welfare are aligned only if search cost is high (in which case the platform maximizes social welfare) but at odds with each other when search cost is low.
A STABLE MATCH 