Predictive analytics uses AI to forecast future outcomes, enabling proactive business decisions. E-commerce predictions: - Churn risk - Purchase likelihood - Customer lifetime value - Next best action - Inventory demand - Support ticket volume How it works: - Historical data analysis - Pattern recognition - Machine learning models - Continuous learning - Confidence scoring Predictive model types: - Classification (will/won't churn) - Regression (predicted LTV) - Clustering (customer segments) - Time series (demand forecasting) Applications: - Retention campaigns for at-risk customers - Personalized offers by purchase likelihood - Resource planning - Inventory management - Marketing optimization