Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar-value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feedforward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper 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减少客户参与度的行为。两种主要的业务营销策略在增加市场份额的货币价值方面发挥着至关重要的作用：获取新客户和保留现有客户。客户获取成本可能是客户保留成本的五到六倍，因此投资于有流失风险的客户是明智之举。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，并帮助企业识别流失的影响和可能原因，进而利用这些知识实施有针对性的激励措施。本文提出了一种框架，该框架使用深度前馈神经网络进行分类，并结合了针对高维稀疏数据的序列模式挖掘方法。我们还提出了一种因果贝叶斯网络，用于预测导致客户流失的原因概率。测试数据上的评估指标证实，XGBoost和我们的深度学习模型优于以往技术。实验分析证实，一些代表超级担保缴款水平、账户增长和客户任期等独立因果变量被识别为具有高置信度的客户流失混淆因素。本文提供了从当前状态推断到未来方向的本地养老基金客户流失真实案例分析。