Online Demand Under Limited Consumer Search.

Marketing Science (Impact Factor: 2.36). 11/2010; 29:1001-1023. DOI: 10.2139/ssrn.1340267
Source: DBLP

ABSTRACT Using aggregate product search data from, 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's product recommendations via lower search costs.

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    Thirty Third International Conference on Information Systems (ICIS 2012); 01/2012
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    ABSTRACT: We develop a probit-based choice model under optimal sequential search and apply the model to study aggregate demand of consumer durable goods. In our joint model of search and choice, we fully characterize optimal sequential search and derive a semi-closed form ex-pression for the probability of choice that obeys the full set of restrictions imposed by optimal sequential search. Our joint model leads to a partial simulation-based estimation that avoids demanding high-dimensional, simulated integrations in evaluating choice probabilities and that is particularly attractive when the consumer search set is large. We demonstrate the applicabil-ity of the proposed model using aggregate search and choice data from the camcorder product category at We show that the joint use of search and choice data provides bet-ter predictions than using search data alone and leads to more realistic estimates of consumer substitution patterns.
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