Stated choice methods: analysis and application
Stated choice methods: analysis and application
A Hierarchical Bayes Model of Primary and Secondary Demand
Marketing Science
Modeling Consumer Demand for Variety
Marketing Science
Observed and Unobserved Preference Heterogeneity in Brand-Choice Models
Marketing Science
When Do Price Thresholds Matter in Retail Categories?
Marketing Science
Structural Modeling in Marketing: Review and Assessment
Marketing Science
Promotion Effect on Endogenous Consumption
Marketing Science
Accounting for Primary and Secondary Demand Effects with Aggregate Data
Marketing Science
Database Paper---The IRI Marketing Data Set
Marketing Science
Consumer-Driven Demand and Operations Management Models: A Systematic Study of Information-Technology-Enabled Sales Mechanisms
A Model for Trade-Up and Change in Considered Brands
Marketing Science
Tipping and Concentration in Markets with Indirect Network Effects
Marketing Science
The Sound of Silence: Observational Learning in the U.S. Kidney Market
Marketing Science
Retail Competition and the Dynamics of Demand for Tied Goods
Marketing Science
Opinion Leadership and Social Contagion in New Product Diffusion
Marketing Science
Multiple-Constraint Choice Models with Corner and Interior Solutions
Marketing Science
Disentangling Preferences and Learning in Brand Choice Models
Marketing Science
Marketing Science
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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 significantly to account for quantity choices, to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multicategory purchases), and to allow for asymmetric switching between brands across different 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 first 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 to describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and we 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 the potential for new directions in future research.