Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.

翻译：客户流失描述的是在特定时期内终止与企业的关系或降低客户参与度的现象。获取新客户的成本可能是保留现有客户的五到六倍，因此投资于存在流失风险的客户是明智之举。通过客户流失模型的因果分析，可以预测客户在可预见的未来是否会流失，并识别影响流失的效应及可能原因。总体而言，本研究提出一个概念框架，用于发现与自变量相关且与影响流失的因变量存在因果关系的混杂特征。我们结合了SMOTE、集成人工神经网络和贝叶斯网络等不同算法，应对金融机构中通常因使用客户关系管理系统中基于区间的特征而产生的大规模高维金融数据的流失预测问题。通过递归特征消除方法评估维度诅咒与维度祝福效应，以克服高维特征空间问题。此外，我们进行了因果发现，寻找能够描述导致客户流失的原因概率的可能解释方法。验证数据上的评估指标证实，随机森林模型和我们的集成人工神经网络模型以86%的准确率优于其他方法。因果分析结果确认，代表养老金缴款水平、账户增长和账户余额的一些独立因果变量被识别为导致客户流失的高置信度混杂变量。本文从当前状态推断至本地养老基金未来方向，提供了真实的客户流失分析。