RetentionOS: A Closed-Loop Prescriptive Machine Learning Framework for Real-Time Customer Retention Smeet Patel

Existing customer retention systems in subscription-based industries rely on open-loop churn prediction that never systematically measures whether interventions caused customers to stay. This paper proposes RetentionOS, a closed-loop prescriptive machine learning framework that integrates churn propensity, pricing sensitivity, and customer lifetime value (CLV) profitability models with a low-latency feature store (AWS DynamoDB, Redis) and a tlive scoring pipeline, delivering top-3 curated retention offers via API within strict sub-second latency requirements. A champion-challenger traffic split with formal statistical promotion criteria drives continuous evidence-based model evolution. Outcome measurement via daily order processing tables, combined with holdout-based causal attribution, closes the learning loop. The framework contributes mathematical formulation of the decision objective and CLV-weighted retention metric, formal algorithms for arbitration and champion-challenger promotion, a capability comparison against four baseline approaches, and a reproducible ablation methodology. RetentionOS is proposed as a reference framework directly applicable to any high-CLV customer li