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. The use of informative priors including...

We discuss our experiences teaching Bayesian Statistics to students in doctoral programs in business. These students often have weak backgrounds in mathematical statistics and a predisposition against likelihood-based methods stemming from prior exposure to econometrics. This can be overcome by...

Two Bayesian optimal design criteria for hierarchical linear models are discussed - the psi_beta criterion for the estimation of individual-level parameters beta, and the psi_theta criterion for the estimation of hyper-parameters theta. While the psi_beta criterion involves only the...

Bayesian methods have become widespread in the marketing literature. We review the essence of the Bayesian approach and explain why it is particularly useful for marketing problems. While the appeal of the Bayesian approach has long been noted by researchers, recent developments in computational...

Mediation analysis is used to study the relationship between stimulus and response in the presence of intermediate, generative variables. The traditional approach to mediation analysis is based on the statistical significance of coefficients in aggregate regression models that assume homogeneity...

The primary emphasis of quantitative marketing is on the evaluation of firm policies for determination of marketing variables as well as the optimization of these policies. Thus, our methods of inference must apply to decision problems and to policy evaluation. In addition, the data we have...