Fast Polyhedral Adaptive Conjoint Estimation
We propose and test new "polyhedral" question design and estimation methods that use recent developments in mathematical programming. The methods are designed to offer accurate estimates after relatively few questions in problems involving many parameters. With polyhedral question design, each respondent's questions are adapted based upon prior answers by that respondent to reduce a feasible set of parameters as rapidly as possible. Polyhedral estimation provides estimates based on a centrality criterion (the "analytic center" of the feasible parameter set). The methods require computer support but can operate in both Internet and other computer-aided environments with no noticeable delay between questions. We evaluate the proposed methods using two approaches. First, we use Monte Carlo simulations to compare the methods against established benchmarks in a variety of domains. In the simulations we compare polyhedral question design to three benchmarks: random selection, efficient Fixed designs, and Adaptive Conjoint Analysis (ACA). We compare polyhedral estimation to Hierarchical Bayes estimation for each question design method. The simulations evaluate the methods across different levels of respondent heterogeneity, response accuracy, and numbers of questions. For low numbers of questions, polyhedral question design does best (or is tied for best) for all domains. For high numbers of questions, efficient Fixed designs do better in some domains. The best estimation method depends on respondent heterogeneity and response accuracy. Polyhedral (analytic center) estimation shows particular promise for high heterogeneity and/or for low response errors. The second evaluation employs a large-scale field test. The field test involved 330 respondents, who were randomly assigned to a question-design method and asked to complete a web-based conjoint exercise. Following the conjoint exercise, respondents were given $100 and allowed to make a purchase from a Pareto choice set of five new-to-the-market laptop computer bags. The respondents received their chosen bag together with the difference in cash between the price of their chosen bag and the $100. We compare the question-design and estimation methods on both internal validity (holdout tasks) and external validity (actual choice of a laptop bag). The field test findings are consistent with the simulation results and offer strong support for the polyhedral question design method. The preferred estimation method varied based on the question design method, although Hierarchical Bayes estimation consistently per-formed well in this domain. The findings reveal a remarkable level of consistency across the validation tasks. They suggest that the proposed methods are sufficiently promising to justify further development. At the time of the test, the bags were prototypes. Based, in part, on the results of this study the bags were launched successfully and are now commercially available. Sales of the features of the laptop bags were consistent with conjoint-analysis predictions.
|Date of creation:||03 Feb 2003|
|Date of revision:|
|Contact details of provider:|| Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA|
Web page: http://mitsloan.mit.edu/
More information through EDIRC
|Order Information:|| Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA|
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.:
- 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 273-83, September.
- 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 103-23, Se.
- David Reibstein & John E. G. Bateson & William Boulding, 1988. "Conjoint Analysis Reliability: Empirical Findings," Marketing Science, INFORMS, vol. 7(3), pages 271-286.
- 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.
- 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.
- 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.
- John R. Hauser & Steven P. Gaskin, 1984. "Application of the “Defender” Consumer Model," Marketing Science, INFORMS, vol. 3(4), pages 327-351.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 392-97, December.
- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
When requesting a correction, please mention this item's handle: RePEc:mit:sloanp:1810. 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 references are entirely missing, you can add them using this form.