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A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing

Guillermo Gallego¹; Anran Li²

¹ School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong 518116, China; · ² Department of Decisions, Operations and Technology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

Random Consideration Set Model We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176]—that captures the limited attention of consumers, assuming that purchases are based on fixed preference orderings with consideration sets formed from independent attentions. We provide a condition for uniquely identifying model parameters and design an efficient algorithm for model parameters estimation. We offer a greedy-like algorithm for assortment optimization, adaptable for optimal assortment subject to cardinality constraint or discovering efficient sets. We extend the model to consider random product preferences, with a 1/2 performance-guaranteed approximation algorithm. Using data from a major U.S. airline, we find that the RCS model outperforms the mixed multinomial logit model in approximately half of the markets, particularly with smaller, less varied data sets.

DOI

10.1287/opre.2019.0333

Volume

72 (6)

Pages

2358-2374

Language

en

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