A Comparative Empirical Study of Discrete Choice Models in Retail Operations

Gerardo Berbeglia (), Agustín Garassino () and Gustavo Vulcano
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Gerardo Berbeglia: Centre for Business Analytics, Melbourne Business School, The University of Melbourne, Carlton, Victoria 3053, Australia
Agustín Garassino: Department of Computer Science, University of Buenos Aires, Buenos Aires 1428, Argentina; School of Business, Universidad Torcuato Di Tella, Buenos Aires 1428, Argentina

Management Science, 2022, vol. 68, issue 6, 4005-4023

Abstract: Choice-based demand estimation is a fundamental task in retail operations and revenue management, providing necessary input data for inventory control, assortment, and price-optimization models. The task is particularly difficult in operational contexts where product availability varies over time and customers may substitute into the available options. In addition to the classical multinomial logit (MNL) model and extensions (e.g., nested logit, mixed logit, and latent-class MNL), new demand models have been proposed (e.g., the Markov chain model), and others have been recently revisited (e.g., the rank list-based and exponomial models). At the same time, new computational approaches were developed to ease the estimation function (e.g., column-generation and expectation-maximization (EM) algorithms). In this paper, we conduct a systematic, empirical study of different choice-based demand models and estimation algorithms, including both maximum-likelihood and least-squares criteria. Through an exhaustive set of numerical experiments on synthetic, semisynthetic, and real data, we provide comparative statistics of the predictive power and derived revenue performance of an ample collection of choice models and characterize operational environments suitable for different model/estimation implementations. We also provide a survey of all the discrete choice models evaluated and share all our estimation codes and data sets as part of the online appendix.

Keywords: demand estimation; consumer preferences; choice behavior; maximum likelihood estimation; least squares estimation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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