A Hierarchical Bayes Model for Assortment Choice

Abstract

In this research, the authors merge an established methodology—hierarchical Bayesian modeling—and an existing utility model— Farquhar and Rao's (1976) balance model—to describe individual choices among assortments of multiattributed items. This approach facilitates addressing three managerial questions of direct importance: (1) Which assortment of a given size has the most customers for whom it is the preferred assortment (and what fraction of customers)? (2) Which products should be added to a given assortment to maximize the fraction of customers who can find their most preferred assortment? and (3) For a given assortment, what type of customer is likely to purchase it? The model is applied to assortment choices constructed from a set of eight popular magazines with five measured attributes: business, current events, human interest stories, sports, and technology. The analysis indicates that consumers are heterogeneous: Some customers are price sensitive and unresponsive to the magazine attributes, and others are sensitive to the magazine features but do not necessarily want more of them. In addition, the authors observe that many subjects do not prefer varied assortments; rather, consumers focus on purchasing magazines with high levels of the attribute they want.