@article{01JGXBN482NR9Q9JWRJR4T0DJJ,
  abstract     = {{This paper proposes a novel approach to enhance the profitability of business-to-business (B2B) customer retention campaigns through profit-driven pre-processing techniques, deviating from the traditional focus on inand post-processing methods. Our study explores the effectiveness of three pre-processing techniques-massaging, reweighing, and resampling-derived from fairness literature. We evaluate these techniques alongside a baseline model and three state-of-the-art in- and post-processing methods using the EMPB and a newly introduced metric, the Area Under the Expected Profit Curve (AUEPC). Our findings demonstrate that reweighing and resampling consistently outperform baselines up to a 49% profit increase. Furthermore, compared to state-of-the-art algorithms, reweighing and resampling methods surpass in-processing techniques and perform favorably against post-processing methods, particularly at optimal customer contact rates. However, post-processing methods are preferred under budget constraints. This study contributes to the current literature by offering a simpler, model-agnostic, and less computationally expensive framework for profit-driven churn modeling in B2B contexts.}},
  articleno    = {{115159}},
  author       = {{Rahman, Shimanto and Janssens, Bram and Bogaert, Matthias}},
  issn         = {{0148-2963}},
  journal      = {{JOURNAL OF BUSINESS RESEARCH}},
  keywords     = {{Marketing analytics,CRM,Data analytics,Profit-driven,Retention,strategies,RELATIONSHIP MANAGEMENT,PREDICTION,SELECTION,RETENTION,TELECOMMUNICATION,COMPANY,REGRESSION,SHARE}},
  language     = {{eng}},
  pages        = {{17}},
  title        = {{Profit-driven pre-processing in B2B customer churn modeling using fairness techniques}},
  url          = {{http://doi.org/10.1016/j.jbusres.2024.115159}},
  volume       = {{189}},
  year         = {{2025}},
}

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