Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

Authors

  • Md Zahidul Islam Charles Stuart University
  • Steven D’Alessandro School of Management and Marketing, Charles Sturt University, Bathurst, NSW 2795, Australia.
  • Michael Furner School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia.
  • Lester Johnson Department of Management and Marketing, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.
  • David Gray Department of Marketing and Management, Macquarie University, NSW 219, Australia.
  • Leanne Carter Senior Lecturer, Department of Marketing and Management, Macquarie University, NSW 219, Australia.

DOI:

https://doi.org/10.3127/ajis.v20i0.1420

Keywords:

decision tree, decision forest, ensemble of decision trees, data mining, brand switching, switching behaviour

Abstract

There is more than one mobile-phone subscription per member of the Australian population. The number of complaints against the mobile-phone-service providers is also high. Therefore, the mobile service providers are facing a huge challenge in retaining their customers. There are a number of existing models to analyse customer behaviour and switching patterns. A number of switching models may also exist within a large market. These models are often not useful due to the heterogeneous nature of the market. Therefore, in this study we use data mining techniques to let the data talk to help us discover switching patterns without requiring us to use any models and domain knowledge. We use a variety of decision tree and decision forest techniques on a real mobile-phone-usage dataset in order to demonstrate the effectiveness of data mining techniques in knowledge discovery. We report many interesting patterns, and discuss them from a brand-switching and marketing perspective, through which they are found to be very sensible and interesting.

Author Biography

Md Zahidul Islam, Charles Stuart University

decision trees, classifiers of any type, ensemble of classifiers, clustering, missing value imputation, application of data mining, and privacy issues related to data mining.

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Published

2016-11-29

How to Cite

Islam, M. Z., D’Alessandro, S., Furner, M., Johnson, L., Gray, D., & Carter, L. (2016). Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia. Australasian Journal of Information Systems, 20. https://doi.org/10.3127/ajis.v20i0.1420

Issue

Vol. 20 (2016)

Section

Research Articles

License

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