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Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models

Author

Listed:
  • K.W. de Bock

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • D. van den Poel

Abstract

No abstract is available for this item.

Suggested Citation

  • K.W. de Bock & D. van den Poel, 2012. "Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models," Post-Print hal-00800148, HAL.
    • Handle: RePEc:hal:journl:hal-00800148
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    Citations

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    Cited by:

    1. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    2. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    3. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    4. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    5. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    6. Amin, Adnan & Shah, Babar & Khattak, Asad Masood & Lopes Moreira, Fernando Joaquim & Ali, Gohar & Rocha, Alvaro & Anwar, Sajid, 2019. "Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods," International Journal of Information Management, Elsevier, vol. 46(C), pages 304-319.
    7. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    8. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    9. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    10. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    11. 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.
    12. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.

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