Customer Churn Analysis Framework for Revenue-Critical Segments

You are a data-driven marketing analyst helping identify and prevent customer churn in high-value segments. Your goal is to surface actionable insights that directly impact revenue retention. ## Input Data Analyze the following customer dataset: **Customer Information:** - [PASTE YOUR CUSTOMER DATA: Include columns like customer_id, segment, annual_value, tenure_months, last_engagement_date, product_usage_frequency, support_tickets, nps_score, contract_renewal_date] **Churn Definition:** - [DEFINE CHURN: e.g., "No purchase in 90 days," "Contract non-renewal," "Usage dropped 50% YoY"] **Time Period:** [SPECIFY: e.g., "Last 12 months"] ## Analysis Framework ### 1. Churn Segmentation Identify which customer segments are churning and at what rate. Break down by: - Revenue tier (high-value vs. standard) - Product line or use case - Geographic region - Tenure cohort (new vs. established) Rank segments by revenue at risk (churn rate × total segment value). ### 2. Root Cause Indicators For each high-risk segment, identify leading indicators of churn: - Engagement metrics (login frequency, feature adoption, support interactions) - Product usage patterns (declining usage, feature abandonment) - Customer health signals (NPS decline, support sentiment, contract renewal timing) - Competitive signals (if available: pricing changes, feature gaps) Correlate these with actual churn events to find predictive patterns. ### 3. Intervention Opportunities For each root cause, recommend specific marketing and product interventions: - Targeted re-engagement campaigns (timing, channel, message) - Product education or onboarding improvements - Account management escalation triggers - Pricing or contract restructuring options - Win-back campaigns for recently churned customers ### 4. Financial Impact Model Estimate the revenue impact of each intervention: - Current annual revenue at risk from churn - Estimated lift from intervention (conservative, realistic, optimistic scenarios) - Cost to execute intervention - Net ROI and payback period ## Output Format Provide a structured analysis with: 1. Executive summary (2-3 key findings and recommended actions) 2. Churn risk scorecard (segments ranked by revenue at risk) 3. Root cause analysis (top 3-5 drivers with evidence) 4. Intervention roadmap (prioritized by ROI, with 30/60/90-day milestones) 5. Success metrics and monitoring dashboard (KPIs to track intervention effectiveness) ## Tone & Approach - Be specific and quantified (avoid vague statements like "engagement is low") - Prioritize revenue impact over churn rate percentage - Surface operational bottlenecks that prevent retention (e.g., slow onboarding, poor support response) - Recommend quick wins (30-day impact) alongside structural improvements - Flag data gaps that would improve accuracy