IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v22y2003i3p273-303.html
   My bibliography  Save this article

Fast Polyhedral Adaptive Conjoint Estimation

Author

Listed:
  • Olivier Toubia

    () (Sloan School of Management, Massachusetts Institute of Technology, E56-305, 38 Memorial Drive, Cambridge, Massachusetts 02142)

  • Duncan I. Simester

    () (Sloan School of Management, Massachusetts Institute of Technology, E56-305, 38 Memorial Drive, Cambridge, Massachusetts 02142)

  • John R. Hauser

    () (Sloan School of Management, Massachusetts Institute of Technology, E56-305, 38 Memorial Drive, Cambridge, Massachusetts 02142)

  • Ely Dahan

    () (Anderson School, University of California at Los Angeles, 110 Westwood Plaza, B-514, Los Angeles, California 90095)

Abstract

We propose and test new adaptive question design and estimation algorithms for partial profile conjoint analysis. Polyhedral question design focuses questions to reduce a feasible set of parameters as rapidly as possible. Analytic center estimation uses a centrality criterion based on consistency with respondents' answers. Both algorithms run with no noticeable delay between questions. We evaluate the proposed methods relative to established benchmarks for question design (random selection, D-efficient designs, adaptive conjoint analysis) and estimation (hierarchical Bayes). Monte Carlo simulations vary respondent heterogeneity and response errors. For low numbers of questions, polyhedral question design does best (or is tied for best) for all tested domains. For high numbers of questions, efficient fixed designs do better in some domains. Analytic center estimation shows promise for high heterogeneity and for low response errors; hierarchical Bayes for low heterogeneity and high response errors. Other simulations evaluate hybrid methods, which include self-explicated data. A field test (330 respondents) compared methods on both internal validity (holdout tasks) and external validity (actual choice of a laptop bag worth approximately $100). The field test is consistent with the simulation results and offers strong support for polyhedral question design. In addition, marketplace sales were consistent with conjoint-analysis predictions.

Suggested Citation

  • Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
  • Handle: RePEc:inm:ormksc:v:22:y:2003:i:3:p:273-303
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.22.3.273.17743
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, vol. 17(1), pages 29-44.
    2. Toubia, Olivier & Hauser, John & Simester, Duncan, 2003. "Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis," Working papers 4285-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    3. Freund, Robert Michael. & Roundy, Robin. & Todd, Michael J., 1947-, 1985. "Identifying the set of always-active constraints in a system of linear inequalities by a single linear program," Working papers 1674-85., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Arora, Neeraj & Huber, Joel, 2001. " Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Oxford University Press, vol. 28(2), pages 273-283, September.
    5. John R. Hauser & Steven P. Gaskin, 1984. "Application of the “Defender” Consumer Model," Marketing Science, INFORMS, vol. 3(4), pages 327-351.
    6. Elie Ofek & V. Srinivasan, 2002. "How Much Does the Market Value an Improvement in a Product Attribute?," Marketing Science, INFORMS, vol. 21(4), pages 398-411, June.
    7. John R. Hauser & Steven M. Shugan, 1980. "Intensity Measures of Consumer Preference," Operations Research, INFORMS, vol. 28(2), pages 278-320, April.
    8. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    9. Green, Paul E & Helsen, Kristiaan & Shandler, Bruce, 1988. " Conjoint Internal Validity under Alternative Profile Presentations," Journal of Consumer Research, Oxford University Press, vol. 15(3), pages 392-397, December.
    10. David Reibstein & John E. G. Bateson & William Boulding, 1988. "Conjoint Analysis Reliability: Empirical Findings," Marketing Science, INFORMS, vol. 7(3), pages 271-286.
    11. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    12. Green, Paul E & Srinivasan, V, 1978. " Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Oxford University Press, vol. 5(2), pages 103-123, Se.
    13. Sha Yang & Gerg M. Allenby & Geraldine Fennel, 2002. "Modeling Variation in Brand Preference: The Roles of Objective Environment and Motivating Conditions," Marketing Science, INFORMS, vol. 21(1), pages 14-31, May.
    14. Moore, William L. & Semenik, Richard J., 1988. "Measuring preferences with hybrid conjoint analysis: The impact of a different number of attributes in the master design," Journal of Business Research, Elsevier, vol. 16(3), pages 261-274, May.
    15. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
    16. V. Srinivasan & Allan Shocker, 1973. "Linear programming techniques for multidimensional analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 38(3), pages 337-369, September.
    Full references (including those not matched with items on IDEAS)

    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:ormksc:v:22:y:2003:i:3:p:273-303. 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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.