27 Sep 2018Working Paper Summaries

Large-Scale Demand Estimation with Search Data

by Tomomichi Amano, Andrew Rhodes, and Stephan Seiler

Online retailers face the challenge of leveraging the rich data they collect on their websites to uncover insights about consumer behavior. This study proposes a practical and tractable model of economic behavior that can reveal helpful patterns of cross-product substitution. The model can be used to simulate optimal prices.

Author Abstract

Many online markets are characterized by sellers that stock large numbers of products and sell each product infrequently. At the same time, consumer browsing information is typically tracked by online retailers and is much more abundant than purchase data. We propose a demand model that caters to this type of setting. Our approach, which is based on search and purchase data, is computationally light and allows for flexible substitution patterns. We apply the model to a data set containing browsing and purchase information from a retailer stocking over 500 products, recover the elasticity matrix, and solve for optimal prices for the entire assortment.