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

Author

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  • K.W. de Bock

    (LEM - Lille - Economie et Management - 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. 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.
    2. Szeląg, Marcin & Słowiński, Roman, 2024. "Explaining and predicting customer churn by monotonic rules induced from ordinal data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 414-424.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
    13. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    14. 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.

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