E-commerce platforms, such as Amazon, Alibaba and Flipkart, that match sellers and consumers at an unprecedented scale, operate their internal search engines to help buyers find relevant products from a large number of sellers, and also allow sellers to advertise to consumers for positions in the search listing. Determining an optimal ranking of products in response to a search query is a challenging problem for the platform because sellers have certain private information about their products that the platform does not have. Using a theoretical model, we show that sellers’ bids in ad auctions, through which sponsored slots are typically allocated, can reveal (some of) this private information to the platform (“information effect”), which it can optimally combine with information that it has about consumers to improve the placement of organic results, a practice we call “strategic listing”. However, by introducing an externality between the sponsored and organic sides, strategic listing also leads to more competition in the ad auction (“competition effect”), thus reducing the incentive of sellers to participate on the platform. Overall, while both the platform and the consumers benefit from strategic listing, as it becomes more difficult to incentivize sellers’ participation, the platform should reduce both the dependence of the organic results on the information learned from sponsored ad bidding and its commission rate. Our results shed light on the variation in practices across different platforms.
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