IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v61y2015i4p885-897.html
   My bibliography  Save this article

Attribute-Level Heterogeneity

Author

Listed:
  • 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)

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.

Suggested Citation

  • Peter Ebbes & John C. Liechty & Rajdeep Grewal, 2015. "Attribute-Level Heterogeneity," Management Science, INFORMS, vol. 61(4), pages 885-897, April.
  • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:4:p:885-897
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2014.1898
    Download Restriction: no

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:61:y:2015:i:4:p:885-897. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.