Hierarchical Bayes Models: A Practitioners Guide

44 Pages Posted: 28 Jan 2005

See all articles by Greg M. Allenby

Greg M. Allenby

Ohio State University (OSU) - Department of Marketing and Logistics

Peter E. Rossi

University of California, Los Angeles (UCLA) - Anderson School of Management

Robert E. McCulloch

University of Chicago - Booth School of Business

Date Written: January 2005

Abstract

Hierarchical Bayes models free researchers from computational constraints and allow for the development of more realistic models of buyer behavior and decision making. Moreover, this freedom enables exploration of marketing problems that have proven elusive over the years, such as models for advertising ROI, sales force effectiveness, and similarly complex problems that often involve simultaneity. The promise of Bayesian statistical methods lies in the ability to deal with these complex problems, but the very complexity of the problems creates a significant challenge to both researchers and practitioners. We illustrate the promise of HB models and provide an introduction to their computation.

Keywords: Hiearchical models, bayes, survey research, conjoint

JEL Classification: M3, C0

Suggested Citation

Allenby, Greg M. and Rossi, Peter E. and McCulloch, Robert E., Hierarchical Bayes Models: A Practitioners Guide (January 2005). Available at SSRN: https://ssrn.com/abstract=655541 or http://dx.doi.org/10.2139/ssrn.655541

Greg M. Allenby

Ohio State University (OSU) - Department of Marketing and Logistics ( email )

Fisher Hall 524
2100 Neil Ave
Columbus, OH 43210
United States

Peter E. Rossi (Contact Author)

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States
773-294-8616 (Phone)

Robert E. McCulloch

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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