Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study

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Abstract
Recently, both Hagerty and Kamakura have proposed insightful suggestions for improving the predictive accuracy of conjoint analysis via various types of averaging of individual responses. Hagerty uses Q-type factor analysis (i.e., optimal weighting) and Kamakura a hierarchical cluster analysis that optimizes predictive validity. Both approaches are compared with conventional conjoint and self-explicated utility models using real datasets. Neither the Hagerty nor the Kamakura suggestions lead to higher predictive validities than are obtained by conventional conjoint analysis applied to individual response data.