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Conjoint Analysis for Complex Services Using Clusterwise Hierarchical Bayes Procedures

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Data Analysis, Machine Learning and Applications

Abstract

Conjoint analysis is a widely used method in marketing research. Some problems occur when conjoint analysis is used for complex services where the perception of and the preference for attributes and levels considerably varies among individuals. Clustering and clusterwise estimation procedures as well as Hierarchical Bayes (HB) estimation can help to model this perceptual uncertainty and preference heterogeneity. In this paper we analyze the advantages of clustering and clusterwise HB as well as combined estimation procedures of collected preference data for complex services and therefore extend the analysis of Sentis and Li (2002).

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Author information

Authors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, Postbox 101344, 03013, Cottbus, Germany

    Michael Brusch & Daniel Baier

Authors
  1. Michael Brusch
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  2. Daniel Baier
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Institute of Business Economics and Information Systems, University of Hildesheim, Marienburgerplatz 22, 31141, Hildesheim, Germany

    Christine Preisach

  2. Lehrstuhl für Mustererkennung und Bildverarbeitung, Universität Freiburg, Gebäude 052, 79110, Freiburg i. Br, Germany

    Hans Burkhardt

  3. Institute of Computer Science and Institute of Business Economics and Information Systems, Marienburgerplatz 22, 31141, Hildesheim, Germany

    Lars Schmidt-Thieme

  4. Fakultät für Wirtschaftswissenschaften, Lehrstuhl für Betriebswirtschaftslehre, insbes. Marketing, Universitätsstraße 25, 33615, Bielefeld, Germany

    Reinhold Decker

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© 2008 Springer-Verlag Berlin Heidelberg

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Brusch, M., Baier, D. (2008). Conjoint Analysis for Complex Services Using Clusterwise Hierarchical Bayes Procedures. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_51

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