Hierarchical Bayes versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery

A study conducted by Vriens, Wedel, and Wilms (1996) and published in Journal of Marketing Research found that finite mixture (FM) conjoint models had the best overall performance of nine conjoint segmentation methods in terms of fit, prediction, and parameter recovery. Since that study, hierarchical Bayes (HB) conjoint analysis methods have been proposed to estimate individual-level partworths and have received much attention in the marketing research literature. However, no study has compared the relative effectiveness of FM and HB conjoint analysis models in terms of fit, prediction, and parameter recovery. To conduct such a comparison, the authors employ the simulation methodology proposed by Vriens, Wedel, and Wilms with some modification. The authors estimate traditional individual-level conjoint models as well. The authors show that FM and HB models are equally effective in recovering individual-level parameters and predicting ratings of holdout profiles. Two surprising findings are that (1) HB performs well even when partworths come from a mixture of distributions and (2) FM produces good parameter estimates, even at the individual level. The authors show that both models are quite robust to violations of underlying assumptions and that traditional individual-level models overfit the data.