Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach

The role of churn modelling is to maximize the value of marketing dollars spent and minimize the attrition of valuable customers. Though churn prediction is a common classification task, traditional approaches cannot be employed directly due to the unique issues inherent within the wealth management industry. Through this paper we address the issue of unseen churn in superannuation; whereby customer accounts become dormant following the discontinuation of compulsory employer contributions, and suggest solutions to the problem of scarce customer engagement data. To address these issues, this paper proposes a new approach for churn prediction and its application in the superannuation industry. We use the extreme gradient boosting algorithm coupled with contrast sequential pattern mining to extract behaviors preceding a churn event. The results demonstrate a significant lift in the performance of prediction models when pattern features are used in combination with demographic and account features.