Bala Aravind Vutla

An explainable survival aware intelligence framework for customer retention decision support beyond static churn prediction

Traditional customer churn prediction treats retention as a static binary classification task. This limits operational value because it fails to address when a customer will leave, why they are leaving, and which intervention is economically viable. No existing framework integrates temporal, explanatory, and prescripti...