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Conjoint Measurement pp 231–258Cite as

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

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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).

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Author information

Authors and Affiliations

  1. Columbia University, New York, USA

    Olivier Toubia (Assistant Professor of Marketing)

  2. Technology Management and Decision Sciences, INSEAD, Fontainebleau Cedex, France

    Theodoros Evgeniou (Assistant Professor)

  3. M.I.T.’s Sloan School of Management, Cambridge, Massachusetts, USA

    John Hauser (Kirin Professor of Marketing and Head of the Marketing Group)

Authors
  1. Olivier Toubia
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  2. Theodoros Evgeniou
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  3. John Hauser
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Editor information

Editors and Affiliations

  1. Karlstad University, 651 88, Karlstad, Sweden

    Anders Gustafsson

  2. University of St. Gallen, Guisanstr. 1 a, 9010, St. Gallen, Switzerland

    Andreas Herrmann

  3. University of Mainz, Jakob-Welder-Weg 9, 55128, Mainz, Germany

    Frank Huber

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Toubia, O., Evgeniou, T., Hauser, J. (2007). Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design. In: Gustafsson, A., Herrmann, A., Huber, F. (eds) Conjoint Measurement. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71404-0_12

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