A Unifying Approach to Benefit Segmentation and Product Line Design Based on Rank Order Conjoint Data

  • E. Aust
  • W. Gaul
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Simultaneous part-worths estimation, benefit segmentation, repositioning of established products, and product line design can be achieved by estimating the parameters of a constrained latent class model. In an application concerning swimming pool design questions the output of the new approach is contrasted both with the results of traditional benefit segmentation and product line design modeling.


Conjoint Analysis Ideal Product Latent Class Model Established Product Public Bath 
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  1. AUST, E., and GAUL, W. (1994): Decision Making Concerning Product Line Design Based on Conjoint Analysis. Proceedings of the 18. Symposium OR, Köln 1993. Physica Verlag.Google Scholar
  2. BEN-AKIVA, M., MORIKAWA, T., and, SHIROISHI, F. (1992): Analysis of the Reliability of Preference Ranking Data. Journal of Business Research, 24, 149–164.CrossRefGoogle Scholar
  3. BOX, M.J. (1966): A Comparison of Several Current Optimization Methods, and the Use of Transformations in Constrained Problems. Computing Journal, 9, 67–77.Google Scholar
  4. CHAPMAN, R.G., and STAELIN, R. (1982): Exploiting Rank Order Choice Set Data Within the Stochastic Utility Model. Journal of Marketing Research, 19, 288–301.CrossRefGoogle Scholar
  5. DEMPSTER, A.P., LAIRD, N.M., and RUBIN, D.B. (1977): Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, B39, 1–38.Google Scholar
  6. FORMANN, A.K. (1985): Constrained Latent Class Models: Theory and Applications. British Journal of Mathematical and Statistical Psychology, 38, 87–111.CrossRefGoogle Scholar
  7. GAUL, W. (1978): Zur Methode der paarweisen Vergleiche und ihrer Anwendung im Marketingbereich. Methods of Operations Research, 35, 123–139.Google Scholar
  8. GAUL, W., and BAIER, D. (1994): Marktforschung und Marketing Management. Oldenbourg Verlag. München, 2. Auflage.Google Scholar
  9. GAUL, W., and BOTH, M. (1990): Computergestiitztes Marketing. Springer, Berlin, Heidelberg, New York, etc.Google Scholar
  10. GAUL, W., LUTZ, U., and AUST, E. (1994): Goodwill Towards Domestic Products as Segmentation Criterion: An Empirical Study Within the Scope of Research on Country-of-Origin Effects. Studies in Classification, Data Analysis, and Knowledge Organization, 4, 421–430.Google Scholar
  11. GAUL, W., WARTENBERG, F., and BAIER, D. (1994): Comparing Proposals for the Solution of Data Analysis Problems in a Knowledge-Based-System. Annals of OR, 52, 131–150.CrossRefGoogle Scholar
  12. GREEN, P.E., and KRIEGER, A.M. (1991): Segmenting Markets with Conjoint Analysis. Journal of Marketing, 55, 20–31.CrossRefGoogle Scholar
  13. KOHLI, R., and SUKUMAR, R. (1990): Heuristics for Product-Line Design Using Conjoint Analysis. Management Science, 36, 12, 1464–1478.CrossRefGoogle Scholar
  14. MCLACHLAN, G.J., and BASFORD, K.E. (1988): Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York.Google Scholar
  15. OGAWA, K. (1987): An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis. Marketing Science, 6, 1, 66–81.CrossRefGoogle Scholar
  16. RAMASWAMY, V., DESARBO, W., REIBSTEIN, D.J., and ROBINSON, W.T. (1993): An Empirical Pooling Approach for Estimating Marketing Mix Elasticities With PIMS Data. Marketing Science, 12, 1, 103–124.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • E. Aust
    • 1
  • W. Gaul
    • 1
  1. 1.Institut für Entscheidungstheorie und UnternehmensforschungUniversität KarlsruheKarlsruheGermany

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