Large-Scale Demand Estimation with Search Data
32 Pages Posted: 4 Aug 2018 Last revised: 26 Mar 2019
Date Written: March 20, 2019
In many online markets, traditional methods of demand estimation are difficult to implement because assortments are very large and individual products are sold infrequently. At the same time, data on consumer search (i.e., browsing) behavior are often available and are much more abundant than purchase data. We propose a demand model that caters to this type of setting. Our approach is computationally light and allows for flexible cross-price elasticities that are informed by search patterns. We apply the model to a data set containing search and purchase information from a retailer stocking almost 600 products, recover the elasticity matrix, and solve for optimal prices for the entire assortment.
Keywords: High-Dimensional Data, Demand Estimation, Consideration Sets, Consumer Search
JEL Classification: C55, D12, D83, M31
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