The global e-commerce sector loses over $100 billion annually to customer churn. While loyal customers generate 80% of revenue, at-risk customers often defect silently, reducing lifetime value by nearly 70%. Traditional churn prediction models generally optimize for generic accuracy or ROC-AUC. However, this competition focuses directly on the economic impact of probabilistic decisions, recognizing that every prediction carries a highly specific financial consequence.