The Exponomial Choice Model: A New Alternative for Assortment and Price Optimization

• 成果类型: Article; Proceedings Paper
• 署名作者: Alptekinoglu, Aydin; Semple, John H.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Southern Methodist University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2015.1459
• 发表日期: 2016
• 页码: 79-93
• 关键词: willingness-to-pay; nested logit model; dynamic-assortment; revenue management; Consumer choice; COORDINATION; mechanisms; inventory

摘要

We investigate the use of a canonical version of a discrete choice model due to Daganzo (1979) [Daganzo C (1979) Multinomial Probit: The Theory and Its Application to Demand Forecasting (Academic Press, New York).] in optimal pricing and assortment planning. In contrast to multinomial and nested logit (the prevailing choice models used for optimizing prices and assortments), this model assumes a negatively skewed distribution of consumer utilities, an assumption we motivate by conceptual arguments as well as published work. The choice probabilities in this model can be derived in closed form as an exponomial (a linear function of exponential terms). The pricing and assortment planning insights we obtain from the exponomial choice (EC) model differ from the literature in two important ways. First, the EC model allows variable markups in optimal prices that increase with expected utilities. Second, when prices are exogenous, the optimal assortment may exhibit leapfrogging in prices, i.e., a product can be skipped in favor of a lower-priced one depending on the utility positions of neighboring products. These two plausible pricing and assortment patterns are ruled out by multinomial logit (and by nested logit within each nest). We provide structural results on optimal pricing for monopoly and oligopoly cases, and on the optimal assortments for both exogenous and endogenous prices. We also demonstrate how the EC model can be easily estimated-by establishing that the log-likelihood function is concave in model parameters and detailing an estimation example using real data.