Major online platforms such as Amazon and eBay have invested significantly in search technologies to direct consumer searches to relevant products. These technologies lead to targeted search, implying consumers are visiting more relevant sellers first. For example, consumers may directly enter their desirable attributes into search queries, and the platform will retrieve relevant sellers accordingly. The platform may also let consumers refine the search outcomes by various criteria. This study characterizes the role of targeted search, and examines how targeted search affects market equilibrium and platform design.I model targeted search in a differentiated market with many firms where consumers search sequentially for the best product match. Within this setup, I endogenize the search design by allowing the platform to choose the precision of targeted search and the revenue model contract. One of the central results of the analysis is how targeted search affects equilibrium prices. I find its impact on price is not monotonic. When targeting is not too precise, targeted search lowers the equilibrium price. It makes sellers more similar and intensifies price competition, despite the fact that all consumers face sellers with better fit. However, once the targeting becomes sufficiently precise, the equilibrium price increases, because highly targeted search discourages active consumer search and gives sellers monopoly power. Furthermore, I consider two major platform revenue models, commission and promoted slots, with consumer search. The platform, by providing targeted search with precision up to the aforementioned limit, can extract more consumer surplus through higher commission rates, because targeted search improves consumer surplus by lowering search cost, increasing fit, and lowering price. With targeted search up to the limit, the platform can also extract more surplus from sellers by offering promoted slots, because sellers can use promoted slots to better target consumers. However, once targeted search becomes too precise, the market will face a price hike, hurting the platform revenue in both models. Therefore, I find that in both revenue models, the platform may want to limit the precision of targeted search even if improving it is costless, with or without consumer entry. Using a unique dataset from Taobao, I find suggestive evidences that are consistent with the model predictions
