Bayesian Applications in Marketing∗

In this chapter, we review applications of Bayesian methods to marketing problems. Key aspects of marketing applications include the discreteness of response or outcome data and relatively large numbers of cross-sectional units, each with possibly low information content. Discrete response data require the development of non-standard likelihoods and low information content requires careful use of informative priors. One particularly important form of informative prior is embodied in hierarchical models. Given the importance of the prior, it is important to assure flexibility in the prior specification. Non-standard likelihoods and flexible priors make marketing a very challenging area for Bayesian applications. ∗Rossi would like to acknowledge the Kilts Center for Marketing, Booth School of Business, University of Chicago, for providing research funds. Allenby thanks the Fisher College of Business at Ohio State University for generous research support. All correspondence may be addressed to the authors at the UCLA, Anderson School of Management, 110 Westwood Plaza, Los Angeles, CA 90095; or via e-mail at [email protected].