@article{8747228,
  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.}},
  author       = {{Janssens, Bram and Bogaert, Matthias and Bagué, Astrid and Van den Poel, Dirk}},
  issn         = {{0254-5330}},
  journal      = {{ANNALS OF OPERATIONS RESEARCH}},
  keywords     = {{Management Science and Operations Research,General Decision Sciences,B2B customer churn,Cost-sensitive learning,Churn,Data mining,Profit-driven model evaluation,Retention strategies,CUSTOMER CHURN,PREDICTION,RETENTION,CLASSIFICATION,SELECTION,REGRESSION,IMBALANCE,DEFECTION,LONG,PAY}},
  language     = {{eng}},
  pages        = {{267--293}},
  title        = {{B2Boost : instance-dependent profit-driven modelling of B2B churn}},
  url          = {{http://doi.org/10.1007/s10479-022-04631-5}},
  volume       = {{341}},
  year         = {{2024}},
}

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