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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.
Introduction
Bayesian Essentials
MCMC Methods
Unit-Level Models and Discrete Demand
Hierarchical Models for Heterogeneous Units
Model Choice and Decision Theory
Simultaneity
A Bayesian Perspective on Machine Learning
Bayesian Analysis for Text Data
Case Study 1: Analysis of Choice-Based Conjoint Data Using A Hierarchical Logit Model
Case Study 2: WTP and Equilibrium Analysis with Conjoint Demand
Case Study 3: Scale Usage Heterogeneity
Case Study 4: Volumetric Conjoint
Case Study 5: Approximate Bayes and Personalized Pricing
Appendix A An Introduction to R and bayesm