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.