Guiding Consumers through Lemons and Peaches: A Dynamic Model of Search over Multiple Characteristics

67 Pages Posted: 18 May 2018 Last revised: 10 Mar 2019

See all articles by Pedro Gardete

Pedro Gardete

Stanford Graduate School of Business

Megan Hunter Antill

Stanford Graduate School of Business

Date Written: March 2019

Abstract

The increasing amount of data available to consumers has most likely aided in decision-making. However, it has also created an opportunity for sellers to design the information landscape that consumers navigate. This paper develops a novel fully dynamic search model for alternatives with multiple characteristics, and reports estimation results for an online used car seller. The model allows characterizing search over alternatives with multiple characteristics that may be distributed arbitrarily. It also allows for a rich set of consumer search behaviors, including piecemeal search within and arbitrary paths across alternatives. We estimate the model using clickstream data on the website of a used car seller. The dataset tracks incremental search actions as well as test-drive reservations. The estimated fundamentals are then used to consider the effects of different information design policies. We find that the choice of the characteristics to be made available to consumers upfront may have conversion implications ranging from -0.39% to +1.65%. The perfect information scenario increases conversion rates by 10.4%. Finally, we compare our model with the knowledge gradient model of learning, and show that taking forward-looking behavior into account explains the moments of the data better, and that the models’ likelihoods are significantly different.

Keywords: consumer search, information design, correlated attributes, knowledge gradient

JEL Classification: D12, D82, D83

Suggested Citation

Gardete, Pedro and Hunter Antill, Megan, Guiding Consumers through Lemons and Peaches: A Dynamic Model of Search over Multiple Characteristics (March 2019). Stanford University Graduate School of Business Research Paper No. 3669. Available at SSRN: https://ssrn.com/abstract=3180811 or http://dx.doi.org/10.2139/ssrn.3180811

Pedro Gardete (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Megan Hunter Antill

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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