Using Extremes to Design Products and Segment Markets

Current marketing methodologies used to study consumers are inadequate for identifying and understanding respondents whose preferences for a product offering are most extreme. These “extreme respondents” have important implications for product design and market segmentation decisions. The authors develop a hierarchical Bayes random-effects model and apply it to a conjoint study of credit card attributes. Their proposed model facilitates an in-depth study of respondent heterogeneity, especially of extreme respondents. The authors demonstrate the importance of characterizing extremes in identifying product attributes and predicting the success of potential products.

[1]  William R. Dillon,et al.  Capturing Individual Differences in Paired Comparisons: An Extended BTL Model Incorporating Descriptor Variables , 1993 .

[2]  Peter E. Rossi,et al.  Hierarchical Modelling of Consumer Heterogeneity: An Application to Target Marketing , 1995 .

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

[4]  Greg M. Allenby,et al.  Incorporating Prior Knowledge into the Analysis of Conjoint Studies , 1995 .

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

[6]  Dick R. Wittink,et al.  Exploring Territorial Differences in the Relationship between Marketing Variables , 1977 .

[7]  Greg M. Allenby,et al.  Modeling Household Purchase Behavior with Logistic Normal Regression , 1994 .

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

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

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

[11]  Jordan J. Louviere,et al.  Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data , 1983 .

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

[13]  William L. Moore,et al.  Levels of Aggregation in Conjoint Analysis: An Empirical Comparison , 1980 .

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

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

[16]  Peter E. Rossi,et al.  A Bayesian Approach to Estimating Household Parameters , 1993 .

[17]  Philippe Cattin,et al.  A Simple Bayesian Procedure for Estimation in a Conjoint Model , 1983 .

[18]  Paul E. Green,et al.  A Hybrid Utility Estimation Model for Conjoint Analysis , 1981 .

[19]  H. Gatignon.  Competition as a Moderator of the Effect of Advertising on Sales , 1984 .