Online Demand Under Limited Consumer Search

Using aggregate product search data from Amazon.com, we jointly estimate consumer information search and online demand for durable goods. To estimate demand and search primitives, we introduce an optimal sequential search process into a model of choice and treat the observed market-level product search data as aggregations of individual-level optimal search sequences. The model builds on the dynamic programming framework by Weitzman (1979) and combines it with a choice model. At the individual level, the model has several attractive properties including closed-form expressions for the probability distribution of alternative search sets and breaking the curse of dimensionality. Using numerical experiments, we verify the model's ability to identify consumer tastes and search cost from product search data. Empirically, the model is applied to the camcorder online market and is used to answer manufacturer questions about market structure and competition, and to address policy maker issues about the effect of recommendation tools on consumer surplus outcomes. We find that consumer search for camcorders is typically limited to about 10 choice options, and that this affects the estimates of own and cross-elasticities. We also find that the vast majority of the households benefit from the Amazon.com's product recommendations via lower search costs