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B2Boost: instance-dependent profit-driven modelling of B2B churn

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Abstract

The purpose of this paper is to enhance current practices in business-to-business (B2B) customer churn prediction modelling. Following the recent trend from accuracy-based to profit-driven evaluation business-to-customer churn prediction, we present a novel expected maximum profit measure for B2B customer churn (EMPB), which is used to demonstrate how current practices are suboptimal due to large discrepancies in customer value. To directly incorporate the heterogeneity of customer values and profit concerns of the company, we propose an instance-dependent profit maximizing classifier based on gradient boosting, named B2Boost. The main innovation of B2Boost is the fact that it considers these differences and incorporates them into the model construction by maximizing the objective function in terms of the EMPB. The results indicate that the expected maximal profit gains made in our analyses are substantial. This study arguments towards both deploying models based on customer-specific profitability differences, as well as evaluating based on our instance-dependent EMPB measure.

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Acknowledgements

The authors are thankful to Dr. Michel Ballings for providing the data and to the three anonymous reviewers whose comments have helped significantly improve earlier versions of this paper. The authors are also grateful to the Guest Editor of the Data Science & Decision Analytics Special Issue, Dr. Asil Oztekin, for his guidance and timely management of this manuscript.

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Authors and Affiliations

  1. Department of Marketing, Innovation and Organisation, Ghent University, Tweekerkenstraat 2, 9000, Ghent, Belgium

    Bram Janssens, Matthias Bogaert, Astrid Bagué & Dirk Van den Poel

  2. FlandersMake@UGent–corelab CVAMO, Ghent, Belgium

    Matthias Bogaert & Dirk Van den Poel

Authors
  1. Bram Janssens
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  2. Matthias Bogaert
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  3. Astrid Bagué
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  4. Dirk Van den Poel
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Correspondence to Matthias Bogaert.

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Janssens, B., Bogaert, M., Bagué, A. et al. B2Boost: instance-dependent profit-driven modelling of B2B churn. Ann Oper Res 341, 267–293 (2024). https://doi.org/10.1007/s10479-022-04631-5

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