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

Olivier Toubia, Theodoros Evgeniou and John Hauser
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Olivier Toubia: Columbia University
Theodoros Evgeniou: INSEAD
John Hauser: M.I.T.’s Sloan School of Management

Chapter 12 in Conjoint Measurement, 2007, pp 231-258 from Springer

Abstract: 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).

Keywords: Support Vector Machine; Loss Function; Conjoint Analysis; Response Error; Complexity Control (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-3-540-71404-0_12

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