EconPapers    
Economics at your fingertips  
 

Attribute-Level Heterogeneity

Peter Ebbes (), John C. Liechty () and Rajdeep Grewal ()
Additional contact information
Peter Ebbes: Department of Marketing, HEC Paris, 78351 Jouy-en-Josas, France
John C. Liechty: Department of Marketing, Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802
Rajdeep Grewal: Department of Marketing, Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802

Management Science, 2015, vol. 61, issue 4, 885-897

Abstract: Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research we study finite mixture specifications for modeling consumer heterogeneity where each regression coefficient has its own finite mixture---that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneity lies in its estimation. A proposed Bayesian estimation approach, based on recent advances in reversible-jump Markov chain Monte Carlo methods, can estimate parameters for the attribute-based finite mixture model, assuming that the number of components for each finite mixture is a discrete random variable. An attribute specification has several advantages over traditional, vector-based, finite mixture specifications; specifically, the attribute mixture model offers a more appropriate aggregation of information than does the vector specification facilitating estimation. In an extensive simulation study and an empirical application, we show that the attribute model can recover complex heterogeneity structures, making it dominant over traditional (vector) finite mixture regression models and a strong contender compared to mixture-of-normals models for modeling heterogeneity. This paper was accepted by Pradeep Chintagunta, marketing.

Keywords: heterogeneity; mixture models; hierarchical Bayes; conjoint analysis; reversible-jump MCMC; segmentation (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2014.1898 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:61:y:2015:i:4:p:885-897

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().

 
Page updated 2025-03-19
Handle: RePEc:inm:ormnsc:v:61:y:2015:i:4:p:885-897