Metric Conjoint Segmentation Methods: A Monte Carlo Comparison

The authors compare nine metric conjoint segmentation methods. Four methods concern two-stage procedures in which the estimation of conjoint models and the partitioning of the sample are performed separately; in five, the estimation and segmentation stages are integrated. The methods are compared conceptually and empirically in a Monte Carlo study. The empirical comparison pertains to measures that assess parameter recovery, goodness-of-fit, and predictive accuracy. Most of the integrated conjoint segmentation methods outperform the two-stage clustering procedures under the conditions specified, in which a latent class procedure performs best. However, differences in predictive accuracy were small. The effects of degrees of freedom for error and the number of respondents were considerably smaller than those of number of segments, error variance, and within-segment heterogeneity.

[1]  S. Addelman.  Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments , 1962 .

[2]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[3]  Russell I. Haley.  Benefit Segmentation: A Decision-oriented Research Tool , 1968 .

[4]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[5]  Yoram Wind,et al.  Subjective evaluation models and conjoint measurement , 1972 .

[6]  Paul E. Green,et al.  Consumer Segmentation Via Latent Class Analysis , 1976 .

[7]  C. F. Banfield,et al.  Algorithm AS 113: A Transfer for Non-Hierarchical Classification , 1977 .

[8]  Paul E. Green,et al.  A new approach to market segmentation , 1977 .

[9]  P. Green,et al.  Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .

[10]  Yoram Wind,et al.  Market-Based Guidelines for Design of Industrial Products , 1978 .

[11]  Yoram Wind,et al.  Issues and Advances in Segmentation Research , 1978 .

[12]  Yoram Wind,et al.  Market-Based Guidelines for Design of Industrial Products , 1978 .

[13]  W. DeSarbo,et al.  Componential Segmentation in the Analysis of Consumer Trade-Offs , 1979 .

[14]  B. Everitt.  Unresolved Problems in Cluster Analysis , 1979 .

[15]  Roger N. Shepard,et al.  Additive clustering: Representation of similarities as combinations of discrete overlapping properties. , 1979 .

[16]  David Stewart,et al.  The Application and Misapplication of Factor Analysis in Marketing Research , 1981 .

[17]  Philippe Cattin,et al.  Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study , 1981 .

[18]  What Product Benefits to Offer to Whom , 1981 .

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

[20]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[21]  Michael R. Hagerty,et al.  Improving the Predictive Power of Conjoint Analysis: The use of Factor Analysis and Cluster Analysis , 1985 .

[22]  H. Hruschka.  Market definition and segmentation using fuzzy clustering methods , 1986 .

[23]  V. Srinivasan,et al.  A Simultaneous Approach to Market Segmentation and Market Structuring , 1987 .

[24]  Kohsuke Ogawa,et al.  An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis , 1987 .

[25]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[26]  W. Kamakura.  A Least Squares Procedure for Benefit Segmentation with Conjoint Experiments , 1988 .

[27]  W. DeSarbo,et al.  A maximum likelihood methodology for clusterwise linear regression , 1988 .

[28]  I. P. Akaah.  Cluster analysis versus Q-type factor analysis as a disaggregation method in hybrid conjoint modeling: An empirical investigation , 1988 .

[29]  M. Wedel,et al.  A fuzzy clusterwise regression approach to benefit segmentation , 1989 .

[30]  M. Wedel,et al.  Consumer benefit segmentation using clusterwise linear regression , 1989 .

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

[32]  Paul E. Green,et al.  Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study , 1989 .

[33]  Wayne S. DeSarbo,et al.  A simulated annealing methodology for clusterwise linear regression , 1989 .

[34]  Gary J. Russell,et al.  A Probabilistic Choice Model for Market Segmentation and Elasticity Structure , 1989 .

[35]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[36]  Sanjay Mishra,et al.  A Monte Carlo investigation of conjoint analysis index-of-fit: Goodness of fit, significance and power , 1990 .

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

[38]  M. Wedel,et al.  A Clusterwise Regression Method for Simultaneous Fuzzy Market Structuring and Benefit Segmentation , 1991 .

[39]  W. Kamakura,et al.  Value Segmentation: A Model for the Measurement of Values and Value Systems , 1991 .

[40]  Wayne S. DeSarbo,et al.  A latent class probit model for analyzing pick any/N data , 1991 .

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

[42]  P. Green,et al.  An empirical test of optimal respondent weighting in conjoint analysis , 1993 .

[43]  Wayne S. DeSarbo,et al.  A Latent Class Binomial Logit Methodology for the Analysis of Paired Comparison Choice Data: An Application Reinvestigating the Determinants of Perceived Risk , 1993 .

[44]  Manoj K. Agarwal,et al.  A cross validation test of four models for quantifying multiattribute preferences , 1993 .

[45]  Michel Wedel,et al.  Fuzzy clusterwise regression in benefit segmentation: Application and investigation into its validity , 1993 .

[46]  Dick R. Wittink,et al.  Commercial use of conjoint analysis in Europe: Results and critical reflections , 1994 .

[47]  Kenneth G. Manton,et al.  Statistical applications using fuzzy sets , 1994 .

[48]  Michel Wedel,et al.  Concomitant Variable Latent Class Models for Conjoint Analysis , 1994 .

[49]  Dick R. Wittink,et al.  Pictorial stimuli in conjoint analysis , 1995 .

[50]  Steven H. Cohen,et al.  Market segmentation with choice-based conjoint analysis , 1995 .

[51]  Gh Loosschilder,et al.  PICTORIAL STIMULI IN CONJOINT-ANALYSIS - TO SUPPORT PRODUCT STYLING DECISIONS , 1995 .

[52]  Terry Elrod,et al.  A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data , 1995 .

[53]  Some Observations on, and Developments in, the Analysis of Multivariate Survey Data , 1996 .