Monitoring Customer Satisfaction in Service Industry: A Cluster Analysis Approach

Authors

  • Matúš Horváth Technical University of Košice, Slovakia
  • Alexandra Michalkova Technical University of Košice

DOI:

https://doi.org/10.12776/qip.v16i1.61

Keywords:

customer satisfaction, cluster analysis, service quality

Abstract

One of the key performance indicators of quality management system of an organization is customer satisfaction. The process of monitoring customer satisfaction is therefore an important part of the measuring processes of the quality management system. This paper deals with new ways how to analyse and monitor customer satisfaction using the analysis of data containing how the customers use the organisation services and customer leaving rates. The article used cluster analysis in this process for segmentation of customers with the aim to increase the accuracy of the results and on these results based decisions. The aplication example was created as a part of bachelor thesis.

References

Blake C. L., Merz C. J. (1998), Churn Data Set, UCI Repository of Machine Learning Databases, University of California, Department of Information and Computer Science, Irvine, CA

International Organization for Standardization, (2005). ISO 9000:2005 Quality management systems -- Fundamentals and vocabulary, Geneva: ISO.

Kriška, J. (2008). Hierarchická a nehierarchická zhluková analýza. [online] Available: http://www2.fiit.stuba.sk/~kapustik/ZS/Clanky0607/kriska/index.html, [Accessed 15.9.2012].

Larose, D. T. (2003), Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, Inc., Hoboken, New Jersey USA, 223 p., ISBN: 0-471-66657-2.

Pham D. T., Afify A. A. (2007), „Clustering techniques and their applications in engineering” in Proc. IMechE Vol. 221 Part C: J. Mechanical Engineering Science, Vol. 221, no. 11, pp. 1445- 1459.

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Issue

Vol. 16 No. 1 (2012)

Section

Articles

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How to Cite

Monitoring Customer Satisfaction in Service Industry: A Cluster Analysis Approach. (2012). Quality Innovation Prosperity, 16(1), 49-54. https://doi.org/10.12776/qip.v16i1.61