Consumer Search and Online Demand for Durable Goods

Using aggregate, product search data from Amazon.com, we jointly estimate consumer information search and online demand for durable goods. To estimate the 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. The model can accommodate highly complex demand patterns at the market level, and at the individual level the model has a number of attractive properties in estimation, including closed-form expressions for the probability distribution of alternative sets of searched goods and breaking the curse of dimensionality. Using numerical experiments, we verify the model's ability to identify the heterogeneous consumer tastes and the distribution of search cost from product search data. Empirically, the model is applied to the online market for camcorders 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 online search for camcorders at Amazon.com is typically limited to less than 10 choice options, and that this affects the estimates of own and cross elasticities. In a policy simulation, we also find that the majority of the households benefit from the Amazon.com's online recommendations via lower search costs. However, lowering search cost through product recommendations to popular product pages may cause worse choice outcomes or higher total search cost for households with atypical preferences.