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This canvas implements a comprehensive end-to-end churn prediction and retention optimization system for a telecom company. It combines machine learning for customer churn forecasting with advanced financial modeling, customer segmentation, and personalized intervention recommendations. The workflow spans four major domains: (1) Data preparation and model training—loading telco data, encoding features, training optimized Random Forest and Gradient Boosting classifiers achieving 75.7% recall on churn detection; (2) Financial impact analysis—calculating business ROI, projecting $44K+ annual savings from true positives, and performing scenario modeling across baseline, current, and optimal retention strategies; (3) Customer segmentation and churn drivers—analyzing churn patterns by contract type, tenure, and service tier, identifying top drivers like contract flexibility and pricing as key churn factors; and (4) Retention campaign design—building a win-back system for churned customers with personalized offers, segment-specific intervention recommendations, budget allocation optimization, and win-back success rate matrices by churn reason. The canvas delivers executive dashboards with quarterly revenue projections, offer performance visualizations, and strategic recommendations for maximizing retention ROI.