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Bayesian Statistics and Marketing
About this book
Fine-tune your marketing research with this cutting-edge statistical toolkit
Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner.
Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity.
Readers of the second edition of Bayesian Statistics and Marketing will also find:
- Discussion of Bayesian methods in text analysis and Machine Learning
- Updates throughout reflecting the latest research and applications
- Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here
- Extensive case studies throughout to link theory and practice
Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- Dedication
- 1 Introduction
- 2 Bayesian Essentials
- 3 MCMC Methods
- 4 UnitāLevel Models and Discrete Demand
- 5 Hierarchical Models for Heterogeneous Units
- 6 Model Choice and Decision Theory
- 7 Simultaneity
- 8 A Bayesian Perspective on Machine Learning
- 9 Bayesian Analysis for Text Data
- 10 Case Study 1: Analysis of ChoiceāBased Conjoint Data Using A Hierarchical Logit Model
- 11 Case Study 2: WTP and Equilibrium Analysis with Conjoint Demand
- 12 Case Study 3: Scale Usage Heterogeneity
- 13 Case Study 4: Volumetric Conjoint
- 14 Case Study 5: Approximate Bayes and Personalized Pricing
- Appendix A: An Introduction to R and bayesm
- References
- Index
- End User License Agreement