Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application

Olivier Toubia (), John Hauser and Rosanna Garcia ()
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Olivier Toubia: Columbia Business School, Columbia University, 522 Uris Hall, 3022 Broadway, New York, New York 10027
Rosanna Garcia: College of Business Administration, Northeastern University, 202 HA, Boston, Massachusetts 02115

Marketing Science, 2007, vol. 26, issue 5, 596-610

Abstract: Polyhedral methods for choice-based conjoint analysis provide a means to adapt choice-based questions at the individual-respondent level and provide an alternative means to estimate partworths when there are relatively few questions per respondent, as in a Web-based questionnaire. However, these methods are deterministic and are susceptible to the propagation of response errors. They also assume, implicitly, a uniform prior on the partworths. In this paper we provide a probabilistic interpretation of polyhedral methods and propose improvements that incorporate response error and/or informative priors into individual-level question selection and estimation. Monte Carlo simulations suggest that response-error modeling and informative priors improve polyhedral question-selection methods in the domains where they were previously weak. A field experiment with over 2,200 leading-edge wine consumers in the United States, Australia, and New Zealand suggests that the new question-selection methods show promise relative to existing methods.

Keywords: conjoint analysis; choice models; estimation and other statistical techniques; international marketing; marketing research; new-product research; product development; Bayesian methods (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:26:y:2007:i:5:p:596-610

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