An important business domain that relies heavily on advanced statistical- and machine learning algorithms to support operational decision-making is customer retention management. Customer churn prediction is a crucial tool to support customer retention. It allows an early identification of customers who are at risk to abandon the company and provides the ability to gain insights into why customers are at risk. Hence, customer churn prediction models should complement predictive performance with model insights. Inspired by their ability to reconcile strong predictive performance and interpretability, this study introduces rule ensembles and their extension, spline-rule ensembles, as a promising family of classification algorithms to the customer churn prediction domain. Spline-rule ensembles combine the flexibility of a tree-based ensemble classifier with the simplicity of regression analysis. They do, however, neglect the relatedness between potentially conflicting model components which can introduce unnecessary complexity in the models and compromises model interpretability. To tackle this issue, a novel algorithmic extension, spline-rule ensembles with sparse group lasso regularization (SRE-SGL) is proposed to enhance interpretability through structured regularization. Experiments on fourteen real-world customer churn data sets in different industries (i) demonstrate the superior predictive performance of spline-rule ensembles with sparse group lasso over a set well yet powerful benchmark methods in terms of AUC and top decile lift; (ii) show that spline-rule ensembles with sparse group lasso regularization significantly outperform conventional rule ensembles whilst performing at least as well as conventional spline-rule ensembles; and (iii) illustrate the interpretable nature of a spline-rule ensemble model and the advantage of structured regularization in SRE-SGL by means of a case study on customer churn prediction for a telecommunications company.

一个严重依赖高级统计和机器学习算法来支持运营决策的重要业务领域是客户保留管理。客户流失预测是支持客户保留的重要工具。它允许及早识别面临放弃公司风险的客户，并提供深入了解客户为何面临风险的能力。因此，客户流失预测模型应该通过模型洞察来补充预测性能。受到它们协调强预测性能和可解释性的能力的启发，本研究引入了规则集成及其扩展，样条规则集成，作为客户流失预测领域的一个有前途的分类算法系列。样条规则集成结合了基于树的集成分类器的灵活性和回归分析的简单性。然而，他们确实忽略了潜在冲突模型组件之间的相关性，这可能会在模型中引入不必要的复杂性并损害模型的可解释性。为了解决这个问题，提出了一种新的算法扩展，具有稀疏组套索正则化的样条规则集成（SRE-SGL），以通过结构化正则化增强可解释性。在不同行业的 14 个真实客户流失数据集上进行的实验 (i) 证明了使用稀疏组套索的样条规则集成在 AUC 和顶级十分位提升方面优于一组良好而强大的基准方法的预测性能；(ii) 表明具有稀疏组套索正则化的样条规则集成显着优于传统规则集成，同时至少与传统样条规则集成一样好；(iii) 通过对电信公司客户流失预测的案例研究，说明样条规则集成模型的可解释性和结构化正则化在 SRE-SGL 中的优势。