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date: 28 May 2020

Abstract and Keywords

This article describes various discrete choice models of consumers who may be heterogeneous both in terms of their preferences and in their sensitivities to marketing variables such as price. It addresses a distinct set of challenges that are being posed through the use of hierarchical priors. It considers standard statistical approach that generates discreteness by applying a censoring function to underlying continuous latent variables. This approach generates models that can be employed in situations where more descriptive models are required. Nonparametric and flexible parametric models involving Dirichlet processes and other mixtures are also accepted and favored in marketing. This article outlines several utility specifications that incorporate discreteness and other important aspects of consumer decisions. Computational issues are important when dealing with large marketing data sets and this article discusses on how to implement posterior simulation methods in marketing models.

Keywords: discrete choice models, hierarchical priors, statistical approach, censoring function, Dirichlet processes

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