  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.
