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Likelihood estimation of consumer preferences in choice-based conjoint analysis

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  • Halme, Merja
  • Kallio, Markku

Abstract

In marketing research the measurement of individual preferences and assessment of utility functions have long traditions. Conjoint analysis, and particularly choice-based conjoint analysis (CBC), is frequently employed for such measurement. The world today appears increasingly customer or user oriented wherefore research intensity in conjoint analysis is rapidly increasing in various fields, OR/MS being no exception. Although several optimization based approaches have been suggested since the introduction of the Hierarchical Bayes (HB) method for estimating CBC utility functions, recent comparisons indicate that challenging HB is hard. Based on likelihood maximization we propose a method called LM and compare its performance with HB using twelve field data sets. Performance comparisons are based on holdout validation, i.e. predictive performance. Average performance of LM indicates an improvement over HB and the difference is statistically significant. We also use simulation based data sets to compare the performance for parameter recovery. In terms of both predictive performance and RMSE a smaller number of questions in CBC appears to favor LM over HB.

Suggested Citation

  • Halme, Merja & Kallio, Markku, 2014. "Likelihood estimation of consumer preferences in choice-based conjoint analysis," European Journal of Operational Research, Elsevier, vol. 239(2), pages 556-564.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:2:p:556-564
    DOI: 10.1016/j.ejor.2014.05.044
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    References listed on IDEAS

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    1. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    2. Asmussen, Søren & Glynn, Peter W., 2011. "A new proof of convergence of MCMC via the ergodic theorem," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1482-1485, October.
    3. Natter, Martin & Feurstein, Markus, 2002. "Real world performance of choice-based conjoint models," European Journal of Operational Research, Elsevier, vol. 137(2), pages 448-458, March.
    4. 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.
    5. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
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    Cited by:

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