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Bayesian Analysis in MarketingA breakthrough in customer analytics

Launched January 2010 Updated March 2010 23 lectures
Prof. Greg M. Allenby
Fisher College of Business, Ohio State University, USA
Prof. Peter E. Rossi
Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics, University of Chicago, USA
Summary

Modern Bayesian analysis has rapidly diffused into the field of marketing over the last 15 years, and is a widely accepted tool for empirical research in academic and practitioner communities. Bayesian analysis can be successfully applied to a broader array of problems, and affords greater insights from data than other... read moremethods of analysis. Bayesian analysis has been used in the development of models of consideration set formation, models of heterogeneous advertising effects, and models of optimal marketing mix allocation involving simultaneous sets of equations. The behavior associated with these models is difficult to study using conventional methods. Bayesian analysis deals directly with complex aspects of realistic models, while delivering actionable insights from the data.

The biggest inroad made by Bayesian analysis in the practitioner marketing world has been in the area of conjoint analysis. Conjoint analysis is a technique for understanding the drivers of value of product offerings. Offerings are thought to be comprised of benefit bundles delivered through product attributes and attribute levels, and conjoint analysis is a statistical technique for estimating the portion of utility that can be assigned to each of the attributes. Utility portions are referred to as ‘part-worths’, and the challenge addressed by Bayesian analysis is the ability to estimate these part-worths at the respondent level. Once respondent level estimates are available for analysis, segmentation analysis and source of volume calculations from new product entry are made possible. The availability of individual level estimates in conjoint analysis is made possible only through the use of Bayes Theorem.

The goal of this series is to introduce the science and practice of Bayesian analysis in marketing. We begin by reviewing the fundamentals of Bayesian analysis, including its computational arm known as Markov chain Monte Carlo (MCMC) estimation. MCMC estimation is a numerical method by which we obtain random samples from the posterior distribution of a model. We show that this method of estimation is particularly well suited for analyzing hierarchical model structures, which are frequently encountered in marketing. We then explore a series of applications that demonstrate the usefulness of Bayesian analysis in marketing.

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Fundamentals (8 Lectures)  

Applications (15 Lectures)  

The value of HB in conjoint/choice analysis
43 min
  • Mr. Bryan K. Orme
  • President, Sawtooth Software, Inc., USA
The SoV Probit
41 min
  • Dr. Jeff Brazell
  • President and CEO, The Modellers Group, USA
Trade-up and trade-down: models for capturing change in consideration set
56 min
  • Mr. Mark Garratt
  • Partner and Co-founder, In4mation Insights, USA
Bayesian modeling of social network data
37 min
  • Prof. Asim Ansari
  • Professor of Marketing, University of Columbia, USA
Joint choice decisions
52 min
  • Prof. Neeraj Arora
  • Professor of Marketing, University of Wisconsin-Madison, USA
Estimating an item's category role
47 min
  • Dr. Peter Boatwright
  • Associate Professor of Marketing, Carnegie Mellon University, USA
Bayesian stochastic dynamic models for internet auctions
39 min
  • Prof. Eric T. Bradlow
  • Professor of Marketing, Statistics and Education, University of Pennsylvania, USA
Hierarchical effects of advertising
42 min
  • Prof. Sandeep R. Chandukala
  • Assistant Professor of Marketing, Indiana University, USA
Investigating the strategic influence of customer satisfaction on firm financial performance
41 min
  • Dr. Jeffrey Dotson
  • Assistant Professor of Marketing, Vanderbilt University, USA
A Bayesian approach to attribute based consideration sets
45 min
  • Prof. Timothy J. Gilbride
  • Assistant Professor of Marketing, University of Notre Dame, USA
Variety: models of multiple-discreteness
27 min
  • Prof. Jaehwan Kim
  • Associate Professor of Marketing, Korea University Business School, S. Korea
Models for upper levels of a hierarchy
33 min
  • Dr. Qing Liu
  • Assistant Professor, University of Wisconsin-Madison, USA
Making better pricing decisions with informative priors
75 min
  • Dr. Alan L. Montgomery
  • Associate Professor of Marketing, Carnegie Mellon University, USA
Marketing mix modeling
36 min
  • Prof. Thomas Otter
  • Professor of Marketing, Goethe University Frankfurt, Germany
Reporting bias in survey data
45 min
  • Dr. Sha Yang
  • Associate Professor of Marketing, New York University, USA