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International Conference on Internet and Distributed Computing Systems

IDCS 2016: Internet and Distributed Computing Systems pp 167–178Cite as

Predicting Telecommunication Customer Churn Using Data Mining Techniques

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9864))

Abstract

This paper will illustrate how to use data mining techniques to predict telecommunication customers churn. With a well analysis and interpretation of the data, valuable knowledge and key insights into the customers’ needs can be achieved. A sample data based on customer usage was gathered, and different data mining techniques were applied over it. This paper’s contribution is to test the capability of a prediction data mining technique, which is the RULES Family algorithm-6 that has never been applied in such a case before. Two pre-stages techniques were applied before the prediction, which are the segmentation “clustering” and the feature selection.

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Acknowledgements

First of all, we would like to thank our families for supporting us while working on this experiment. Furthermore, many thanks to Mobily Company for giving us the data we worked on, and special thanks to Dr. Ahmed Hashem, General Manager Analytics factory at Mobily Company, for his great cooperation and inspiration.

Author information

Authors and Affiliations

  1. Information Systems Department, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Diana AlOmari & Mohammad Mehedi Hassan

Authors
  1. Diana AlOmari
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  2. Mohammad Mehedi Hassan
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Corresponding author

Correspondence to Diana AlOmari .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology , Wuhan, China

    Wenfeng Li

  2. Central Queensland University , North Rockhampton, Queensland, Australia

    Shawkat Ali

  3. Delft University of Technology , Delft, The Netherlands

    Gabriel Lodewijks

  4. University of Calabria , Rende (CS), Italy

    Giancarlo Fortino

  5. University of Reading , Reading, United Kingdom

    Giuseppe Di Fatta

  6. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  7. CSIRO ICT , Acton, Australia

    Mukaddim Pathan

  8. ICAR-CNR, Rende (CS), Italy

    Antonio Guerrieri

  9. Wuhan University of Technology , Wuhan, China

    Qiang Wang

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© 2016 Springer International Publishing AG

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Cite this paper

AlOmari, D., Hassan, M.M. (2016). Predicting Telecommunication Customer Churn Using Data Mining Techniques. In: Li, W., et al. Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science(), vol 9864. Springer, Cham. https://doi.org/10.1007/978-3-319-45940-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-45940-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45939-4

  • Online ISBN: 978-3-319-45940-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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