Structural Workshop Paper - Discrete-Choice Models of Consumer Demand in Marketing

Marketing researchers have used models of consumer demand to forecast future sales; to describe and test theories of behavior; and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain an econometric system that describes consumers' choices over available options, and to thus characterize product demand. The basic framework has been augmented signi cantly to account for quantity choices; to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multi-category purchases); and to allow for asymmetric switching between brands across di erent price tiers. These extensions have enabled researchers to bring the analysis to bear on several related marketing phenomena of interest. This paper has three main objectives. The rst objective is to articulate the main goals of demand analysis forecasting, measurement and testing and to highlight several considerations associated with these goals. Our second objective is describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility speci cations of demand systems, and review extensions that have appeared in the Marketing literature. The third objective is to explore a few emerging directions in demand analysis including considering demand-side dynamics; combining purchase data with primary information; and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see potential for new directions in future research. ∗Professor of Marketing, Chicago Booth, Email: [email protected] †Associate Professor of Marketing, Stanford GSB, Email: [email protected] ‡This paper was written for a special issue at Marketing Science. We thank the organizers of the Columbia-DukeUCLA Workshop on Quantitative Marketing and Structural Econometrics, Duke University, August 2010. We thank our students, colleagues and collaborators for numerous discussions, and the review team, Bryan Bollinger, Eric Bradlow, J.P. Dube, Yesim Orhun, Anita Rao, Scott Shriver, Inseong Song and the review team for comments on this draft. The usual disclaimer applies.