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Telecom churn prediction and used techniques, datasets and performance measures: a review

Author

Listed:
  • Hemlata Jain

    (Poornima University)

  • Ajay Khunteta

    (Poornima Group of Colleges)

  • Sumit Srivastava

    (Manipal University Jaipur)

Abstract

Customer churn prediction in telecommunication industry is a very essential factor to be achieved and it makes direct impact to customer retention and its revenues. Developing a good and effective churn prediction model is very important however it is a time-consuming process. This study presents a very good review of customer churn, its effects, identification of its causes, business needs, methods, and all the techniques used for churn prediction. On the other hand, this study provides the best understanding of the telecom dataset, datasets used by past researches and features used in different researches. Also, this study shows the best techniques used for the churn prediction and describes all performance measures used in the churn prediction models. In this study a wide range of researches are added from the year 2005 to 2020. It includes variety of methods proposed by past researches and technologies used in these researches. At the end, a state of art comparison is added that gives a very good and meaningful comparison of past researches. The study indicates that machine learning techniques are mostly used and feature extraction is a very important task for developing an effective churn prediction model. Deep learning algorithm CNN itself has the capability of feature extraction and establish itself as a powerful technique for churn model, in particular for large datasets. For performance ‘Accuracy’ is a good measure however measuring performance only with ‘Accuracy’ is not sufficient because on small datasets accuracy is more predictable and will be the same. With Accuracy, researchers also need to look at other performance measures such as confusion matrix, ROC, precision. F-measure etc. This study assures that new researchers can find everything regarding their churn prediction model requirements at one place. This study provides a comprehensive view by extensively detailing work which has happened in this area and will act as a rich repositorory of all knowledge regarding churn prediction in telecom sector.

Suggested Citation

  • Hemlata Jain & Ajay Khunteta & Sumit Srivastava, 2021. "Telecom churn prediction and used techniques, datasets and performance measures: a review," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(4), pages 613-630, April.
    • Handle: RePEc:spr:telsys:v:76:y:2021:i:4:d:10.1007_s11235-020-00727-0
    DOI: 10.1007/s11235-020-00727-0
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    References listed on IDEAS

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    1. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    2. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    3. Keramati, Abbas & Ardabili, Seyed M.S., 2011. "Churn analysis for an Iranian mobile operator," Telecommunications Policy, Elsevier, vol. 35(4), pages 344-356, May.
    4. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    5. Muhammad Azeem & Muhammad Usman & A. C. M. Fong, 2017. "A churn prediction model for prepaid customers in telecom using fuzzy classifiers," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(4), pages 603-614, December.
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