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Churn Prediction in Telecommunication Industry Using Rough Set Approach

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New Trends in Computational Collective Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 572))

Abstract

The Customer churn is a crucial activity in rapidly growing and mature competitive telecommunication sector and is one of the greatest importance for a project manager. Due to the high cost of acquiring new customers, customer churn prediction has emerged as an indispensable part of telecom sectors’ strategic decision making and planning process. It is important to forecast customer churn behavior in order to retain those customers that will churn or possible may churn. This study is another attempt which makes use of rough set theory, a rule-based decision making technique, to extract rules for churn prediction. Experiments were performed to explore the performance of four different algorithms (Exhaustive, Genetic, Covering, and LEM2). It is observed that rough set classification based on genetic algorithm, rules generation yields most suitable performance out of the four rules generation algorithms. Moreover, by applying the proposed technique on publicly available dataset, the results show that the proposed technique can fully predict all those customers that will churn or possibly may churn and also provides useful information to strategic decision makers as well.

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Author information

Authors and Affiliations

  1. Institute of Management Sciences Peshawar, Zip Code: 25000, Peshawar, Pakistan

    Adnan Amin, Changez Khan, Imtiaz Ali & Sajid Anwar

  2. City University of Science and Technology, Peshawar, Zip Code: 25000, Pakistan

    Saeed Shehzad

Authors
  1. Adnan Amin
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  2. Saeed Shehzad
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  3. Changez Khan
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  4. Imtiaz Ali
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  5. Sajid Anwar
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Correspondence to Adnan Amin .

Editor information

Editors and Affiliations

  1. Autonomous University of Madrid, Madrid, Spain

    David Camacho

  2. Hanyang University, Seoul, Korea, Republic of (South Korea)

    Sang-Wook Kim

  3. Wrocław University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Amin, A., Shehzad, S., Khan, C., Ali, I., Anwar, S. (2015). Churn Prediction in Telecommunication Industry Using Rough Set Approach. In: Camacho, D., Kim, SW., Trawiński, B. (eds) New Trends in Computational Collective Intelligence. Studies in Computational Intelligence, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-319-10774-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-10774-5_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10773-8

  • Online ISBN: 978-3-319-10774-5

  • eBook Packages: EngineeringEngineering (R0)

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