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Fast Polyhedral Adaptive Conjoint Estimation

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  • 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.

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File URL: http://dx.doi.org/10.1287/mksc.22.3.273.17743
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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 22 (2003)
Issue (Month): 3 ()
Pages: 273-303

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Handle: RePEc:inm:ormksc:v:22:y:2003:i:3:p:273-303

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Related research

Keywords: New Product Research; Measurement; Internet Marketing; Estimation and Other Statistical Techniques;

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Cited by:
  1. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
  2. Halme, Merja & Kallio, Markku, 2011. "Estimation methods for choice-based conjoint analysis of consumer preferences," European Journal of Operational Research, Elsevier, vol. 214(1), pages 160-167, October.
  3. Srinivasan, V. "Seenu" & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.
  4. Joel Steckel & Russell Winer & Randolph Bucklin & Benedict Dellaert & Xavier Drèze & Gerald Häubl & Sandy Jap & John Little & Tom Meyvis & Alan Montgomery & Arvind Rangaswamy, 2005. "Choice in Interactive Environments," Marketing Letters, Springer, vol. 16(3), pages 309-320, December.

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