An Approach to Improve the Predictive Power of Choice-Based Conjoint Analysis

Voleti, S and Srinivasan, V and Ghosh, P (2016) An Approach to Improve the Predictive Power of Choice-Based Conjoint Analysis. International Journal of Research in Marketing.

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Conjoint analysis continues to be popular with over 18,000 applications each year. Choice-based conjoint (CBC) analysis is currently the most often used method of conjoint analysis accounting for eight-tenths of all conjoint studies. The CBC employs a multinomial logit model with heterogeneous parameters across the population. The most commonly used models of heterogeneity are the Latent Class model, the single multivariate normal distribution, or a mixture of multivariate normal distributions. A more recent approach to capture heterogeneity is the Dirichlet Process Mixture (DPM) model and its predecessor Dirichlet Process Prior (DPP) model. The alternative models are empirically tested over eleven CBC data sets with varying characteristics. The DPM model provides the best predictive validity (percent of choices correctly predicted) for each of the eleven datasets studied, and provides a significant improvement over extant models of heterogeneity.

Affiliation: Indian School of Business
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ISB Creators
Voleti, S
Item Type: Article
Uncontrolled Keywords: Conjoint analysis; Choice-based conjoint analysis; Hierarchical Bayesian estimation; Dirichlet Process Prior; Dirichlet Process Mixture
Subjects: Business and Management
Depositing User: Veeramani R
Date Deposited: 14 Sep 2016 18:01
Last Modified: 14 Sep 2016 18:01
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