Modeling decision behavior among multiple choices has been an active research area for several decades. In this tutorial, we review the classic discrete choice models that are widely used in studying purchase behavior for consumers faced with multiple substitutable products. In addition, many other choice models have also been proposed to capture new features that arise in choice process, such as network effects, consideration set, sequential choice and bounded rationality. We provide an overview for a variety of operations management problems under discrete choice models. Pricing is a widely-used marketing strategy to attract consumers and win market competition. In pricing problems, firms determine prices for all their products to maximize the aggregate expected revenue or profit. We characterize the structure of the optimal prices under various choice models. Assortment management is viewed as another effective retailing strategy. In the assortment problems, sellers are not allowed to change retail prices; for example, some product must be sold at the manufacturer suggested retail price. However, a seller can decide which products should be carried in its store or presented to the arriving consumers. We find the optimal solution to the assortment optimization problems under mild conditions for some discrete choice models, and present efficient approximation algorithms for other problems that are NP-hard. To implement the discrete choice models in practice, a critical step is to calibrate the models using real data. We provide the general estimation procedure for discrete choice models using sales data of different structure, and discuss how to develop algorithms to deal with the issues on choice modeling or data availability.
Author: Ruxian Wang