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This paper investigates how many days of early behavior are sufficient for subscription churn prediction, using the KKBox dataset. It shows that the optimal observation window depends on the experimental design, and that claims of window sufficiency should specify cohort construction, target definition, and feature families.
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.