Bayesian Statistics and Marketing, 2nd Edition

Bayesian Statistics and Marketing, 2nd Edition

by Peter E. Rossi, Greg M. Allenby, Sanjog Misra
Released July 2024
Publisher(s): Wiley
ISBN: 9781394219117

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Book description

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.

Table of contents

  1. Cover
  2. Table of Contents
  3. Title Page
  4. Copyright
  5. Dedication
  6. 1 Introduction
    1. 1.1 A BASIC PARADIGM FOR MARKETING PROBLEMS
    2. 1.2 A SIMPLE EXAMPLE
    3. 1.3 BENEFITS AND COSTS OF THE BAYESIAN APPROACH
    4. 1.4 AN OVERVIEW OF METHODOLOGICAL MATERIAL AND CASE STUDIES
    5. 1.5 APPROXIMATE BAYES METHODS AND THIS BOOK
    6. 1.6 COMPUTING AND THIS BOOK
    7. ACKNOWLEDGMENTS
    8. NOTE
  7. 2 Bayesian Essentials
    1. 2.1 ESSENTIAL CONCEPTS FROM DISTRIBUTION THEORY
    2. 2.2 THE GOAL OF INFERENCE AND BAYES THEOREM
    3. 2.3 CONDITIONING AND THE LIKELIHOOD PRINCIPLE
    4. 2.4 PREDICTION AND BAYES
    5. 2.5 SUMMARIZING THE POSTERIOR
    6. 2.6 DECISION THEORY, RISK, AND THE SAMPLING PROPERTIES OF BAYES ESTIMATORS
    7. 2.7 IDENTIFICATION AND BAYESIAN INFERENCE
    8. 2.8 CONJUGACY, SUFFICIENCY, AND EXPONENTIAL FAMILIES
    9. 2.9 REGRESSION AND MULTIVARIATE ANALYSIS EXAMPLES
    10. 2.10 INTEGRATION AND ASYMPTOTIC METHODS
    11. 2.11 IMPORTANCE SAMPLING
    12. 2.12 SIMULATION PRIMER FOR BAYESIAN PROBLEMS
    13. 2.13 SIMULATION FROM POSTERIOR OF MULTIVARIATE REGRESSION MODEL
    14. NOTES
  8. 3 MCMC Methods
    1. 3.1 MCMC METHODS
    2. 3.2 A SIMPLE EXAMPLE: BIVARIATE NORMAL GIBBS SAMPLER
    3. 3.3 SOME MARKOV CHAIN THEORY
    4. 3.4 GIBBS SAMPLER
    5. 3.5 GIBBS SAMPLER FOR THE SUR REGRESSION MODEL
    6. 3.6 CONDITIONAL DISTRIBUTIONS AND DIRECTED GRAPHS
    7. 3.7 HIERARCHICAL LINEAR MODELS
    8. 3.8 DATA AUGMENTATION AND A PROBIT EXAMPLE
    9. 3.9 MIXTURES OF NORMALS
    10. 3.10 METROPOLIS ALGORITHMS
    11. 3.11 METROPOLIS ALGORITHMS ILLUSTRATED WITH THE MULTINOMIAL LOGIT MODEL
    12. 3.12 HYBRID MCMC METHODS
    13. 3.13 DIAGNOSTICS
    14. NOTES
  9. 4 Unit‐Level Models and Discrete Demand
    1. 4.1 LATENT VARIABLE MODELS
    2. 4.2 MULTINOMIAL PROBIT MODEL
    3. 4.3 MULTIVARIATE PROBIT MODEL
    4. 4.4 DEMAND THEORY AND MODELS INVOLVING DISCRETE CHOICE
    5. NOTES
  10. 5 Hierarchical Models for Heterogeneous Units
    1. 5.1 HETEROGENEITY AND PRIORS
    2. 5.2 HIERARCHICAL MODELS
    3. 5.3 INFERENCE FOR HIERARCHICAL MODELS
    4. 5.4 A HIERARCHICAL MULTINOMIAL LOGIT EXAMPLE
    5. 5.5 USING MIXTURES OF NORMALS
    6. 5.6 FURTHER ELABORATIONS OF THE NORMAL MODEL OF HETEROGENEITY
    7. 5.7 DIAGNOSTIC CHECKS OF THE FIRST STAGE PRIOR
    8. 5.8 FINDINGS AND INFLUENCE ON MARKETING PRACTICE
    9. NOTES
  11. 6 Model Choice and Decision Theory
    1. 6.1 MODEL SELECTION
    2. 6.2 BAYES FACTORS IN THE CONJUGATE SETTING
    3. 6.3 ASYMPTOTIC METHODS FOR COMPUTING BAYES FACTORS
    4. 6.4 COMPUTING BAYES FACTORS USING IMPORTANCE SAMPLING
    5. 6.5 BAYES FACTORS USING MCMC DRAWS FROM THE POSTERIOR
    6. 6.6 BRIDGE SAMPLING METHODS
    7. 6.7 POSTERIOR MODEL PROBABILITIES WITH UNIDENTIFIED PARAMETERS
    8. 6.8 CHIB'S METHOD
    9. 6.9 AN EXAMPLE OF BAYES FACTOR COMPUTATION: DIAGONAL MNP MODELS
    10. 6.10 MARKETING DECISIONS AND BAYESIAN DECISION THEORY
    11. 6.11 AN EXAMPLE OF BAYESIAN DECISION THEORY: VALUING HOUSEHOLD PURCHASE INFORMATION
    12. NOTES
  12. 7 Simultaneity
    1. 7.1 A BAYESIAN APPROACH TO INSTRUMENTAL VARIABLES
    2. 7.2 STRUCTURAL MODELS AND ENDOGENEITY/SIMULTANEITY
    3. 7.3 NON‐RANDOM MARKETING MIX VARIABLES
    4. NOTES
  13. 8 A Bayesian Perspective on Machine Learning
    1. 8.1 INTRODUCTION
    2. 8.2 REGULARIZATION
    3. 8.3 BAGGING
    4. 8.4 BOOSTING
    5. 8.5 DEEP LEARNING
    6. 8.6 APPLICATIONS
    7. NOTE
  14. 9 Bayesian Analysis for Text Data
    1. 9.1 INTRODUCTION
    2. 9.2 CONSUMER DEMAND
    3. 9.3 INTEGRATED MODELS
    4. 9.4 DISCUSSION
    5. NOTES
  15. 10 Case Study 1: Analysis of Choice‐Based Conjoint Data Using A Hierarchical Logit Model
    1. 10.1 CHOICE‐BASED CONJOINT
    2. 10.2 A RANDOM COEFFICIENT LOGIT
    3. 10.3 SIGN CONSTRAINTS AND PRIORS
    4. 10.4 THE CAMERA DATA
    5. 10.5 RUNNING THE MODEL
    6. 10.6 DESCRIBING THE DRAWS OF RESPONDENT PARTWORTHS
    7. 10.7 PREDICTIVE POSTERIORS
    8. 10.8 COMPARISON OF STAN AND SAWTOOTH SOFTWARE TO BAYESM ROUTINES
    9. NOTES
  16. 11 Case Study 2: WTP and Equilibrium Analysis with Conjoint Demand
    1. 11.1 THE DEMAND FOR PRODUCT FEATURES
    2. 11.2 CONJOINT SURVEYS AND DEMAND ESTIMATION
    3. 11.3 WTP PROPERLY DEFINED
    4. 11.4 NASH EQUILIBRIUM PRICES – COMPUTATION AND ASSUMPTIONS
    5. 11.5 CAMERA EXAMPLE
    6. NOTES
  17. 12 Case Study 3: Scale Usage Heterogeneity
    1. 12.1 BACKGROUND
    2. 12.2 MODEL
    3. 12.3 PRIORS AND MCMC ALGORITHM
    4. 12.4 DATA
    5. 12.5 DISCUSSION
    6. 12.6 R IMPLEMENTATION
    7. NOTE
  18. 13 Case Study 4: Volumetric Conjoint
    1. 13.1 INTRODUCTION
    2. 13.2 MODEL DEVELOPMENT
    3. 13.3 ESTIMATION
    4. 13.4 EMPIRICAL ANALYSIS
    5. 13.5 DISCUSSION
    6. 13.6 USING THE CODE
    7. 13.7 CONCLUDING REMARKS
    8. NOTE
  19. 14 Case Study 5: Approximate Bayes and Personalized Pricing
    1. 14.1 HETEROGENEITY AND HETEROGENEOUS TREATMENT EFFECTS
    2. 14.2 THE FRAMEWORK
    3. 14.3 CONTEXT AND DATA
    4. 14.4 DOES THE BAYESIAN BOOTSTRAP WORK?
    5. 14.5 A BAYESIAN BOOTSTRAP PROCEDURE FOR THE HTE LOGIT
    6. 14.6 PERSONALIZED PRICING
    7. NOTES
  20. Appendix A: An Introduction to R and bayesm
    1. A.1 SETTING UP THE R ENVIRONMENT AND BAYESM
    2. A.2 THE R LANGUAGE
    3. A.3 USING BAYESM
    4. A.4 OBTAINING HELP WITH BAYESM
    5. A.5 TIPS ON USING MCMC METHODS
    6. A.6 EXTENDING AND ADAPTING OUR CODE
  21. References
  22. Index
  23. End User License Agreement

Product information

  • Title: Bayesian Statistics and Marketing, 2nd Edition
  • Author(s): Peter E. Rossi, Greg M. Allenby, Sanjog Misra
  • Release date: July 2024
  • Publisher(s): Wiley
  • ISBN: 9781394219117