Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

Abstract

Technology advancement has developed a shift perception towards better service from internet providers, and the power to move easily to another provider to secure improved quality results in customer churn. Internet service providers must detect the risk of churn at the earliest opportunity if they want to retain their customers. This study aimed to predict churn using recent developments in machine learning approaches, and customer data from one of the biggest fixed broadband companies in Indonesia was selected as a case study. Ensemble learning is the collaboration of meta-algorithms to improve model performance, and two such approaches were performed in this study, namely random forest and extreme gradient boosting (XGBoost). The results show that the ensemble learning models outperform classical technique and XGBoost is the best algorithm for predicting customer churn. Customers are thereby clustered as being at high, medium, or low risk of churn, and the company can specify particular retention strategies towards each customer cluster.

Keywords

• customer churn prediction
• ensemble learning
• fixed broadband, random forest (rf)
• extreme gradient boosting (xgboost)