"..an asset for business schools and marketing researchers." (Technometrics, May 2007) "'Bayesian Statistics and Marketing' comes from three pioneers in the field of market research and fills a hole in the existing literature on the topic." (Journal of the American Statistical Association, December 2006) "...extremely useful to both researchers and practitioners who are interested in understanding the power of these methods for solving important marketing problems." (Journal of Marketing, October 2006) "This book deserves to be widely adopted by business schools, and widely read by more numerate marketing practitioners." (Short Book Reviews, April 2006) " ... valuable to marketing researchers and others working on related applications, especially if they use advanced logistic and probit models." (JRSSA, Vol. 169, No. 4, October 2006) " ... an excellent book for researchers in applied Bayesian statistics." (Journal of Applied Statistics, Vol. 33: 9, 1034, November 2006) '...an important study tool for potential practitioners or all those researchers who study Bayesian methods through "learning by doing" (Statistical Papers,48,2007)
The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. "Bayesian Statistics and Marketing" describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained in this book include household and consumer panel data on product purchases and survey data, demand models based on micro economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.
Written by the leading experts in the field, this unique book: presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models; provides a self contained introduction to Bayesian methods; includes case studies drawn from the authors' recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems; and is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition, the book's website hosts datasets and R code for the case studies. "Bayesian Statistics and Marketing" provides a platform for researchers in marketing to analyse their data with state of the art methods and develop new models of consumer behaviour. It provides a unified reference for cutting edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.