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.

[1]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[2]  Paul E. Green,et al.  A General Approach to Product Design Optimization via Conjoint Analysis , 1981 .

[3]  Imran S. Currim,et al.  Using Segmentation Approaches for Better Prediction and Understanding from Consumer Mode Choice Models , 1981 .

[4]  Paul E. Green,et al.  Hybrid Models for Conjoint Analysis: An Expository Review , 1984 .

[5]  Paul E. Green,et al.  Models and Heuristics for Product Line Selection , 1985 .

[6]  Philippe Cattin,et al.  Commercial Use of Conjoint Analysis: An Update , 1989 .

[7]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[8]  Paul E. Green,et al.  Segmenting Markets with Conjoint Analysis , 1991 .

[9]  Jacob Cohen.  Statistical Power Analysis , 1992 .

[10]  Michel Wedel,et al.  Latent class metric conjoint analysis , 1992 .

[11]  Hamparsum Bozdogan,et al.  Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity , 1994 .

[12]  J. Carroll,et al.  Guest Editorial: Psychometric Methods in Marketing Research: Part I, Conjoint Analysis , 1995 .

[13]  Greg M. Allenby,et al.  Using Extremes to Design Products and Segment Markets , 1995 .

[14]  J. Douglas Carroll,et al.  Psychometric Methods in Marketing Research: Part I, Conjoint Analysis , 1995 .

[15]  P. Lenk,et al.  Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs , 1996 .

[16]  M. Wedel,et al.  Metric Conjoint Segmentation Methods: A Monte Carlo Comparison , 1996 .

[17]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .

[18]  Greg M. Allenby,et al.  On the Heterogeneity of Demand , 1998 .

[19]  Peter E. Rossi,et al.  Marketing models of consumer heterogeneity , 1998 .

[20]  Michel Wedel,et al.  Discrete and Continuous Representations of Unobserved Heterogeneity in Choice Modeling , 1999 .

[21]  R. Kohli,et al.  Internet Recommendation Systems , 2000 .

[22]  C. Robert,et al.  Computational and Inferential Difficulties with Mixture Posterior Distributions , 2000 .