You Can Lead a Horse to Water: Spatial Learning and Path Dependence in Consumer Search

Опубликовано 20 июля 2018, 19:11

We introduce a model of search by imperfectly informed consumers with unit demand. Consumers learn spatially: sampling the payoff to one product causes them to update their payoffs about all products that are nearby in some attribute space. Search is costly, and so consumers face a trade-off between ``exploring'' far apart regions of the attribute space and ``exploiting'' the areas they already know they like. We present evidence of spatial learning in data on online camera purchases, as consumers who sample unexpectedly low quality products tend to subsequently sample products that are far away in attribute space. We develop a flexible parametric specification of the model where consumer utility is sampled as a Gaussian process and use it to estimate demand in the camera data using Markov Chain Monte Carlo (MCMC) methods. We conclude with a counterfactual experiment in which we manipulate the initial product shown to a consumer, finding that a bad initial experience can lead to early termination of search. Product search rankings can therefore substantially affect consumer search paths and purchase decisions.

Speakers:
Greg Lewis

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