In telecommunications, rising acquisition costs and high service parity make customer churn prediction vital for growth. Existing models, while accurate with static data, often lack real-time capability, individual-level explainability, and integration with CRM systems. We present REEF, a real-time framework that couples fairness-aware explainability with native CRM integration to turn churn scores into audited, business-actionable decisions. REEF unifies streaming ingestion, stacked learners, and per-decision attributions within a privacy-preserving, auditable pipeline that is designed for deployment rather than offline benchmarking.