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Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design

In: Conjoint Measurement

Author

Listed:
  • Olivier Toubia

    (Columbia University)

  • Theodoros Evgeniou

    (INSEAD)

  • John Hauser

    (M.I.T.’s Sloan School of Management)

Abstract

Soon after the introduction of conjoint analysis into marketing by Green and Rao (1972), Srinivasan and Shocker (1973a, 1973b) introduced a conjoint analysis estimation method, Linmap, based on linear programming. Linmap has been applied successfully in many situations and has proven to be a viable alternative to statistical estimation (Jain, et. al. 1979, Wittink and Cattin 1981). Recent modification to deal with “strict pairs” has improved the estimation accuracy with the result that, on occasion, the modified Linmap predicts holdout data better than statistical estimation based on hierarchical Bayes methods (Srinivasan 1998, Hauser, et. al. 2006).

Suggested Citation

  • Olivier Toubia & Theodoros Evgeniou & John Hauser, 2007. "Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design," Springer Books, in: Anders Gustafsson & Andreas Herrmann & Frank Huber (ed.), Conjoint Measurement, edition 0, chapter 12, pages 231-258, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-71404-0_12
    DOI: 10.1007/978-3-540-71404-0_12
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    Citations

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    Cited by:

    1. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    2. Hema Yoganarasimhan, 2020. "Search Personalization Using Machine Learning," Management Science, INFORMS, vol. 66(3), pages 1045-1070, March.
    3. 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.

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