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Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach

Author

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  • Arno de Caigny

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Kristof Coussement

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Wouter Verbeke

    (KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven)

  • Khaoula Idbenjra
  • Minh Phan

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Business-to-business (B2B) customer retention relies heavily on analytics and predictive modeling to support decision making. Given this, we introduce uplift modeling as a relevant prescriptive analytics tool. In particular, the uplift logit leaf model offers a segmentation-based algorithm that combines predictive performance with interpretability. Applied to a real-world data set of 6432 customers of a European software provider, the uplift logit leaf model achieves superior performance relative to three other popular uplift models in our study. The accessibility of output gained from the uplift logit leaf model also is showcased with a case study, which reveals relevant managerial insights. This new tool thus delivers novel insights in the form of customized, global, and segment-level visualizations that are especially pertinent to industrial marketing settings. Overall, the findings affirm the viability of uplift modeling for improving decisions related to B2B customer retention management.

Suggested Citation

  • Arno de Caigny & Kristof Coussement & Wouter Verbeke & Khaoula Idbenjra & Minh Phan, 2021. "Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach," Post-Print hal-03599615, HAL.
    • Handle: RePEc:hal:journl:hal-03599615
    DOI: 10.1016/j.indmarman.2021.10.001
    Note: View the original document on HAL open archive server: https://hal.science/hal-03599615
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    References listed on IDEAS

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    1. Meltem Sanisoglu & Sebnem Burnaz & Tolga Kaya, 2024. "A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 909-924, December.
    2. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Marra, Marianna & Guo, Yihan, 2024. "Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach," Journal of Business Research, Elsevier, vol. 184(C).
    3. Jonathan Legare & Ping Yao & Victor S. Y. Lo, 2023. "A case for conducting business-to-business experiments with multi-arm multi-stage adaptive designs," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 490-502, September.
    4. Fareniuk Yana & Zatonatska Tetiana & Dluhopolskyi Oleksandr & Kovalenko Oksana, 2022. "Customer churn prediction model: a case of the telecommunication market," Economics, Sciendo, vol. 10(2), pages 109-130, December.
    5. Bram Janssens & Matthias Bogaert & Astrid Bagué & Dirk Van den Poel, 2024. "B2Boost: instance-dependent profit-driven modelling of B2B churn," Annals of Operations Research, Springer, vol. 341(1), pages 267-293, October.
    6. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.

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