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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Yes, you can access Bayesian Statistics and Marketing by Peter E. Rossi,Greg M. Allenby,Sanjog Misra in PDF and/or ePUB format, as well as other popular books in Business & Marketing Research. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Wiley

Year

2024

ISBN

9781394219124

Edition

Topic

Business

Subtopic

Marketing Research

Index

Business

Cover
Table of Contents
Title Page
Copyright
Dedication
1 Introduction
2 Bayesian Essentials
3 MCMC Methods
4 Unit‐Level 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 Choice‐Based 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