2019
Working Paper
HBS Working Paper Series

Large-Scale Demand Estimation with Search Data

By: Tomomichi Amano, Andrew Rhodes and Stephan Seiler

Format:Print
| Language:English
| Pages:34

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

Citation

Amano, Tomomichi, Andrew Rhodes, and Stephan Seiler. "Large-Scale Demand Estimation with Search Data." Harvard Business School Working Paper, No. 19-022, September 2018. (Revised June 2019. Stanford University Research Paper, No. 18-36, 8-20 2018.)