Bayesian Statistics and Marketing

＂Bayesian statistics can help to provide unique insights into important issues in many fields. Bayesian statistics is not new, but traditionally it has not been widely taught as it is computationally challenging and there were criticisms surrounding the subjectivity of Bayesian methods. As computing power has increased, computational and modelling breakthroughs have made the implementation of Bayesian methods ideal for addressing many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction and pricing to customized targeting and micro-pricing in online environments, using a wide range of different data sources.＂--

Peter Rossi is James Collins Distinguished University Professor of Marketing, Economics, and Statistics at the Anderson School of Management, UCLA, USA. He is the author of the popular R package, bayesm, and he has researched and published extensively on pricing and promotion, target marketing, and other related subjects.

Greg Allenby is Helen C. Kurtz Professor of Marketing as well as Professor of Statistics at the Fisher College of Business, Ohio State University, USA. He is a Fellow of the Informs Society for Marketing Science and the American Statistical Association, and he has published widely on the development and application of quantitative methods in marketing.

Sanjog Misra is Charles H. Kellstadt Professor of Marketing in the Booth School of Business, University of Chicago, USA. He has served as the co-editor of numerous high-impact journals, including Quantiative Marketing and Economics, and his research focuses on the use of machine learning and deep learning to study consumer and firm decisions