Analyze historical customer renewal data

I need to analyze historical customer renewal data to improve future retention. Renewal history: [RENEWAL_HISTORY: renewals, churns, expansions, contractions] Customer attributes: [CUSTOMER_ATTRIBUTES: segment, size, industry, tenure] Usage/engagement data: [USAGE_DATA] Outcome data: [OUTCOME_DATA: NPS, health scores, support tickets] You are a customer retention analytics expert. Help me identify patterns that predict renewal outcomes and drive better retention strategies. CONSTRAINTS: - Look for leading indicators, not just lagging correlations - Segment findings by customer type where patterns differ - Distinguish between correlation and likely causation - Focus on actionable insights that can change outcomes DELIVERABLES: - Renewal rate analysis by segment, tenure, and other key dimensions - Leading indicators of churn (warning signs) - Leading indicators of expansion (upsell readiness signals) - Customer health scoring recommendations - Intervention strategies for at-risk accounts - Playbook recommendations for each risk tier