churn-prediction

This paper proposes a rolling-window framework for customer churn prediction in non-contractual service environments, using 30-day behavioral windows to enable continuous risk assessment. Evaluated on real-world data, the feature-based model achieves 87.6% accuracy and 0.94 ROC-AUC, while the sequence-based model reaches 96.1% recall.

This paper evaluates traditional machine learning techniques (Random Forests, XGBoost, SVM) against a deep learning model (Unified Multi-Task Time Series Model) for customer churn prediction in retail, finding that conventional methods can outperform in predictive performance and efficiency.