Bayesian Statistics and Marketing

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John Wiley & Sons, May 14, 2012 - Mathematics - 368 pages
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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.

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1 Introduction
2 Bayesian Essentials
3 Markov Chain Monte Carlo Methods
4 UnitLevel Models and Discrete Demand
5 Hierarchical Models for Heterogeneous Units
6 Model Choice and Decision Theory
7 Simultaneity
Case Study 1 A Choice Model for Packaged Goods Dealing with Discrete Quantities and Quantity Discounts
Case Study 3 Overcoming Scale Usage Heterogeneity
Case Study 4 A Choice Model with Conjunctive Screening Rules
Case Study 5 Modeling Consumer Demand for Variety
Appendix A An Introduction to Hierarchical Bayes Modeling in R
Appendix B A Guide to Installation and Use of bayesm

Case Study 2 Modeling Interdependent Consumer Preferences

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