Large-Scale Demand Estimation with Search Data

32 Pages Posted: 4 Aug 2018 Last revised: 26 Mar 2019

See all articles by Tomomichi Amano

Tomomichi Amano

Harvard University - Business School (HBS)

Andrew Rhodes

University of Toulouse 1 - Toulouse School of Economics (TSE)

Stephan Seiler

UCLA Anderson School of Management

Date Written: March 20, 2019

Abstract

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

Suggested Citation

Amano, Tomomichi and Rhodes, Andrew and Seiler, Stephan, Large-Scale Demand Estimation with Search Data (March 20, 2019). Stanford University Graduate School of Business Research Paper No. 18-36; Columbia Business School Research Paper No. 18-60. Available at SSRN: https://ssrn.com/abstract=3214812 or http://dx.doi.org/10.2139/ssrn.3214812

Tomomichi Amano

Harvard University - Business School (HBS) ( email )

Soldiers Field Road
Boston, MA 02163
United States

Andrew Rhodes

University of Toulouse 1 - Toulouse School of Economics (TSE) ( email )

Place Anatole-France
Toulouse Cedex, F-31042
France

Stephan Seiler (Contact Author)

UCLA Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095
United States

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