Pricing for Heterogeneous Products: Analytics for Ticket Reselling

51 Pages Posted: 26 Aug 2019 Last revised: 20 Nov 2020

See all articles by Michael Alley

Michael Alley

StubHub, Inc

Max Biggs

University of Virginia - Darden School of Business

Rim Hariss

McGill University - Desautels Faculty of Management

Charles Herrmann

Massachusetts Institute of Technology (MIT), Operations Research Center, Students

Michael Li

Massachusetts Institute of Technology (MIT), Operations Research Center, Students

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: March 26, 2019

Abstract

We develop a framework for estimating price sensitivity in applications such as ticket reselling. This framework allows us to estimate heterogeneous price sensitivity that we subsequently embed in a price optimization model for ticket reselling. Due to the heterogeneous nature of tickets, the unique market conditions at the time each ticket is listed, and the sparsity of available tickets, demand estimation needs to be done at the individual ticket level. We introduce a double/orthogonalized machine learning method for a classification setting that allows us to isolate the causal effects of price on the outcome by removing the conditional effect of the ticket and market features. Furthermore, we introduce a novel loss function which can be easily incorporated into off the shelf machine learning algorithms, including gradient boosted trees and neural networks. We also show how in the presence of hidden confounding variables instrumental variables can be incorporated. Using a wide range of synthetic data sets, we show this approach beats state-of-the-art machine learning approaches for estimating price sensitivity in our setting, and prove analytical properties for this estimator. We then develop an optimization model for selling tickets on a secondary market, in which we incorporate the heterogeneous price sensitivity model. Leveraging data from a major ticket reseller, we show significant potential for impact in practice. More broadly, this paper develops a novel methodology for estimating heterogeneous treatment effects in classification settings that can be applied to other settings such as personalized pricing and estimating intervention effects in healthcare applications.

Keywords: Pricing, Revenue Management, Machine Learning, Business Analytics, Causal Inference

Suggested Citation

Alley, Michael and Biggs, Max and Hariss, Rim and Herrmann, Charles and Li, Michael and Perakis, Georgia, Pricing for Heterogeneous Products: Analytics for Ticket Reselling (March 26, 2019). Available at SSRN: https://ssrn.com/abstract=3360622 or http://dx.doi.org/10.2139/ssrn.3360622

Michael Alley

StubHub, Inc ( email )

199 Fremont Street
San Francisco, CA
United States

Max Biggs

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Rim Hariss (Contact Author)

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Charles Herrmann

Massachusetts Institute of Technology (MIT), Operations Research Center, Students ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Michael Li

Massachusetts Institute of Technology (MIT), Operations Research Center, Students ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-565
Cambridge, MA 02142
United States

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