IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Fast Polyhedral Adaptive Conjoint Estimation

Listed author(s):
  • Toubia, Olivier
  • Simester, Duncan
  • Hauser, John
  • Dahan, Ely

Web-based customer panels and web-based multimedia capabilities offer the potential to get information from customers rapidly and iteratively based on virtual product profiles. However, web-based respondents are impatient and wear out more quickly. At the same time, in commercial applications, conjoint analysis is being used to screen large numbers of product features. Both of these trends are leading to a demand for conjoint analysis methods that provide reasonable estimates with fewer questions in problems involving many parameters. In this paper we propose and test new adaptive conjoint analysis methods that attempt to reduce respondent burden while simultaneously improving accuracy. We draw on recent "interior-point" developments in mathematical programming which enable us to quickly select those questions that narrow the range of feasible partworths as fast as possible. We then use recent centrality concepts (the analytic center) to estimate partworths. These methods are efficient, run with no noticeable delay in web-based questionnaires, and have the potential to provide estimates of the partworths with fewer questions than extant methods. After introducing these "polyhedral algorithms" we implement one such algorithm and test it with Monte Carlo simulation against benchmarks such as efficient (fixed) designs and Adaptive Conjoint Analysis (ACA). While no method dominates in all situations, the polyhedral algorithm appears to hold significant potential when (a) profile comparisons are more accurate than the self-explicated importance measures used in ACA, (b) when respondent wear out is a concern, and (c) when the product development and marketing teams wish to screen many features quickly. We also test a hybrid method that combines polyhedral question selection with ACA estimation and show that it, too, has the potential to improve predictions in many contexts. The algorithm we test helps to illustrate how polyhedral methods can be combined effectively and synergistically with the wide variety of existing conjoint analysis methods. We close with suggestions on how polyhedral algorithms can be used in other preference measurement contexts (e.g., choice-based conjoint analysis) and other marketing problems.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by Massachusetts Institute of Technology (MIT), Sloan School of Management in its series Working papers with number 4171-01.

in new window

Date of creation: 03 Feb 2003
Handle: RePEc:mit:sloanp:1811
Contact details of provider: Postal:

Phone: 617-253-2659
Web page:

More information through EDIRC


References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. David Reibstein & John E. G. Bateson & William Boulding, 1988. "Conjoint Analysis Reliability: Empirical Findings," Marketing Science, INFORMS, vol. 7(3), pages 271-286.
  9. 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.
  10. 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.
  11. John R. Hauser & Steven P. Gaskin, 1984. "Application of the “Defender” Consumer Model," Marketing Science, INFORMS, vol. 3(4), pages 327-351.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:mit:sloanp:1811. 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: (Christian Zimmermann)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.