UC Berkeley

In three essays, I present my findings in three interrelated aspects of online platforms. In the first chapter, I empirically study the economics of consumer limited attention in online markets and how sellers react to this bias. The second chapter focus on studying consumer search in online platforms under targeted search technology. And in the third chapter I study the platform's search and revenue design problem.

How do sellers react to consumer biases in online markets? In the first chapter I examine whether consumers are limited attentive to online ratings, and how sellers respond to it. Using 6.8 million transaction records on Taobao, I first demonstrate that consumers are limited attentive. The Taobao platform assigns each seller a summary symbol based on his/her rating score (net number of positive ratings) , and both the rating score and the symbol are made available to consumers. I show that the demand exhibits a discontinuous jump when a seller's rating score passes the threshold of a new summary symbol. More importantly, I find sellers appear to be responding by using different pricing strategies before and after reaching thresholds. As a seller's rating score gets closer to the next threshold, the seller's prices gets lower until the rating score reaches thresholds. Once the seller obtain a new summary symbol, the prices begin to increase. The kink in price levels is significant, suggesting sellers are responding to consumer limited attention.

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.

In the second chapter, I model targeted search in a differentiated market with many firms where consumers search sequentially for the best product match. 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. Using a unique dataset from Taobao, I find some suggestive evidence that is consistent with the model predictions.

Within this setup, I endogenize the search design by allowing the platform to choose the precision of targeted search and the revenue model contract in the third chapter. 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.