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Predicting time-to-churn of prepaid mobile telephone customers using social network analysis

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Journal of the Operational Research Society

Abstract

Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.

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Acknowledgements

This research was made possible with support of the Odysseus program (Grant B.0915.09) and Grant G.0816.12N of the Fund for Scientific Research Flanders (FWO).

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Authors and Affiliations

  1. Katholieke Universiteit Leuven, Leuven, Belgium

    Aimée Backiel, Bart Baesens & Gerda Claeskens

  2. University of Southampton, Highfield Southampton, UK

    Bart Baesens

  3. Vlerick, Leuven-Gent Management School, Gent, Belgium

    Bart Baesens

Authors
  1. Aimée Backiel
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  2. Bart Baesens
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  3. Gerda Claeskens
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Correspondence to Aimée Backiel.

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Backiel, A., Baesens, B. & Claeskens, G. Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. J Oper Res Soc 67, 1135–1145 (2016). https://doi.org/10.1057/jors.2016.8

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  • DOI: https://doi.org/10.1057/jors.2016.8

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