Bayesian inference and consumer behavioral theory

With this article we hope to achieve two goals. The first is to encourage consumer behavioral researchers to consider Bayesian methods for analyzing experimental and survey data. As such, we provide what we hope will be a persuasive set of arguments for trying Bayes. The second goal is to survey the different uses to which the Bayesian posterior distribution can be put. We organize this survey in terms of loss functions and propose that such loss functions can be chosen so as to simply describe a consumer behavioral phenomenon, to highlight a managerial implication, or to emphasize a theoretical contribution.