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Customer Satisfaction Measurement - Clustering Approach

Author

Listed:
  • David Schüller

    (Department of Management, Faculty of Business and Management, Brno University of Technology, Kolejní 4, 61200 Brno, Czech Republic)

  • Jan Pekárek

    (Department of Informatics, Faculty of Business and Management, Brno University of Technology, Kolejní 4, 61200 Brno, Czech Republic)

Abstract

The paper deals with the issue of customer satisfaction measurement. The aim of this study is to determine the importance of the individual factors and their impact on total customer satisfaction for multiple segments by using linear regression and hierarchical clustering. This study is focused on the market of café establishment. We applied hierarchical clustering with Ward's criterion to partition customers into segments and then we developed linear regression models for each segment. Linear models for partitioned data showed higher coefficient of determination than the model for the whole market. The results revealed that there are quite significant differences in rankings of customer satisfaction factors among the segments. This is caused by the different preferences of customers. The clustered data allows to achieve a higher homogeneity of data within the segment, which is crucial both for marketing theory and practice. The approach i.e. partitioning the market into smaller more specific segments could become perspective for marketing use in different economic sectors. This attitude can allow marketers to target better on customer segments according to the importance of individual factors.

Suggested Citation

  • David Schüller & Jan Pekárek, 2018. "Customer Satisfaction Measurement - Clustering Approach," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(2), pages 561-569.
    • Handle: RePEc:mup:actaun:actaun_2018066020561
    DOI: 10.11118/actaun201866020561
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    References listed on IDEAS

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    1. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.
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