The past decade has seen a dramatic increase in the use of Bayesianmethods in marketing due, in part, to computational and modellingbreakthroughs, making its implementation ideal for many marketingproblems. Bayesian analyses can now be conducted over a wide rangeof marketing problems, from new product introduction to pricing,and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basicadvantages of the Bayesian approach, detailing the nature of thecomputational revolution. Examples contained include household andconsumer panel data on product purchases and survey data, demandmodels based on micro-economic theory and random effect models usedto pool data among respondents. The book also discusses the theoryand practical use of MCMC methods.Written by the leading experts in the field, this uniquebook:Presents a unified treatment of Bayesian methods in marketing,with common notation and algorithms for estimating the models.Provides a self-contained introduction to Bayesianmethods.Includes case studies drawn from the authors’ recentresearch to illustrate how Bayesian methods can be extended toapply to many important marketing problems.Is accompanied by an R package, bayesm, which implementsall of the models and methods in the book and includes manydatasets. In addition the book’s website hosts datasets and Rcode for the case studies.Bayesian Statistics and Marketing provides a platform forresearchers in marketing to analyse their data withstate-of-the-art methods and develop new models of consumerbehaviour. It provides a unified reference for cutting-edgemarketing researchers, as well as an invaluable guide to thisgrowing area for both graduate students and professors, alike.