AI-Driven Customer Churn Prediction and Retention Optimization: A Multi-Layer Decision Intelligence System

This research presents an AI-driven customer churn prediction and retention optimization system designed for subscription-based industries such as telecommunications, SaaS, fintech, and streaming platforms. Unlike traditional churn models that focus only on binary prediction, this work introduces a multi-layer decision intelligence approach that integrates behavioral analysis, risk prediction, causal uplift modeling, time-to-churn estimation, and budget-aware optimization. The proposed system combines advanced machine learning techniques including XGBoost for churn prediction, survival analysis for temporal insights, Hidden Markov Models for lifecycle understanding, and SHAP for explainability. It further incorporates causal inference methods to identify customers who can be effectively retained, along with a knapsack-based optimization strategy to maximize return on retention investments under budget constraints. A full-stack implementation using FastAPI and React enables real-time analytics and interactive decision-making. Experimental results on benchmark datasets demonstrate strong predictive performance (AUC: 0.921), improved targeting efficiency, and significant gains in retention ROI. The system also includes governance mechanisms such as drift detection and fairness monitoring, ensuring reliability and ethical AI deployment. This work bridges the gap between machine learning research and real-world business applications by delivering a scalable, explainable, and financially optimized customer retention intelligence platform.