Bayesian Statistics and Marketing (Wiley Series in Probability and Statistics)

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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 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.
• 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.

Bayesian Statistics and Marketing (Wiley Series in Probability and Statistics) Review

I will disagree with Eric on this book being a must-have for any "applied quantitative" statistics or marketing Ph.D. student, and call it a must-see for people interested in Bayesian discrete-choice modeling. The five case studies are all examples of marketing research, but are relevant to a much broader audience - consider, for example, "scale usage heterogeneity", affecting analysis of rating-scale responses. The case-study chapters are the book's forte, but it also offers a proper and rigorous introduction to Bayesian modeling, including the expected topics such as simulation (MCMC, Gibbs sampler, etc.) and linear regression, but also chapters on HLM, endogeneity, and model selection. The authors discuss doing Bayesian computation with R package bayesm, but regrettably relegate R material to appendices instead of integrating it into the main narrative and making implementation transparent and reproducible.

PS. In the "if you like this, you may like that" vein, a similar but more general book is "Bayesian statistical methods" by Congdon. (Also published by Wiley, and with a nearly identical cover).

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