Customer retention management relies on advanced analytics for decision making. Decision makers in this area require methods that are capable of accurately predicting which customers are likely to churn and that allow to discover drivers of customer churn. As a result, customer churn prediction models are frequently evaluated based on both their predictive performance and their capacity to extract meaningful insights from the models. In this paper, we extend hybrid segmented models for customer churn prediction by incorporating powerful models that can capture non-linearities. To ensure the interpretability of such segmented hybrid models, we introduce a novel model-agnostic approach that extends SHAP. We extensively benchmark the proposed methods on 14 customer churn datasets on their predictive performance. The interpretability aspect of the new model-agnostic approach for interpreting hybrid segmented models is illustrated using a case study. Our contributions to decision making literature are threefold. First, we introduce new hybrid segmented models as powerful tools for decision makers to boost predictive performance. Second, we provide insights in the relative predictive performance by an extensive benchmarking study that compares the new hybrid segmented methods with their base models and existing hybrid models. Third, we propose a model-agnostic tool for segmented hybrid models that provide decision makers with a tool to gain insights for any hybrid segmented model and illustrate it on a case study. Although we focus on customer retention management in this study, this paper is also relevant for decision makers that rely on predictive modeling for other tasks.

中文翻译：

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通过分段可解释性分析进行客户流失预测的混合黑盒分类

客户保留管理依赖于先进的决策分析。该领域的决策者需要能够准确预测哪些客户可能流失并能够发现客户流失驱动因素的方法。因此，客户流失预测模型经常根据其预测性能和从模型中提取有意义的见解的能力进行评估。在本文中，我们通过结合可以捕获非线性的强大模型来扩展用于客户流失预测的混合分段模型。为了确保此类分段混合模型的可解释性，我们引入了一种新的模型无关方法来扩展 SHAP。我们在 14 个客户流失数据集上对所提出的方法的预测性能进行了广泛的基准测试。通过案例研究说明了用于解释混合分段模型的新模型不可知方法的可解释性方面。我们对决策文献的贡献有三个方面。首先，我们引入新的混合分段模型，作为决策者提高预测性能的强大工具。其次，我们通过广泛的基准测试研究来提供相对预测性能的见解，该研究将新的混合分段方法与其基本模型和现有混合模型进行比较。第三，我们提出了一种用于分段混合模型的模型不可知工具，为决策者提供了一种工具来获得对任何混合分段模型的见解并在案例研究中进行说明。尽管我们在本研究中重点关注客户保留管理，但本文也适用于依赖预测模型来完成其他任务的决策者。