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On the operational efficiency of different feature types for telco Churn prediction

Author

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  • Mitrović, Sandra
  • Baesens, Bart
  • Lemahieu, Wilfried
  • De Weerdt, Jochen

Abstract

Churn prediction in telco remains a very active research topic. Due to the uptake of social network analytics and the results of previous benchmarking studies showing a rather flat maximum performance effect of predictive modeling techniques, the focus has mainly shifted to expanding and exploring the relevant feature space. While previous studies generally agree that adding features typically increases predictive performance, they rarely discuss the accompanying issues such as data availability and computational cost. In this work, we bridge the gap between predictive performance and operational efficiency by devising a new feature type classification and a novel reusable method to determine optimal feature type combinations based on Pareto multi-criteria optimization. Our results provide several insights that can serve as a guideline for industry practitioners.

Suggested Citation

  • Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
    • Handle: RePEc:eee:ejores:v:267:y:2018:i:3:p:1141-1155
    DOI: 10.1016/j.ejor.2017.12.015
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    References listed on IDEAS

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    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. Haupt, Johannes & Lessmann, Stefan, 2020. "Targeting Cutsomers Under Response-Dependent Costs," IRTG 1792 Discussion Papers 2020-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
    4. Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.

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