TITLE

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

AUTHOR(S)
Toubia, Olivier; Hauser, John; Garcia, Rosanna
PUB. DATE
September 2007
SOURCE
Marketing Science;Sep/Oct2007, Vol. 26 Issue 5, p596
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
27713796

 

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