churn-analysis

# Churn Analysis Detect early warning signs of customer churn by aggregating usage telemetry, support interactions, billing history, and engagement metrics into a composite risk score per account. This skill segments accounts into risk tiers and produces actionable intervention playbooks tailored to each tier, enabling CS teams to proactively retain revenue. ## Workflow 1. **Collect usage and engagement data** — Pull metrics across product analytics (DAU, feature adoption, session duration), support history (ticket volume, CSAT scores, escalations), billing signals (late payments, downgrade requests, contract end dates), and engagement touchpoints (email opens, webinar attendance, QBR participation). Normalize all metrics to a consistent time window (typically 90 days trailing). 2. **Define churn signals** — Establish the leading indicators that correlate with churn in your specific context. Common signals include: login frequency dropping below 50% of the account's historical average, a spike in support tickets (3x baseline) within 30 days, missed or late renewal payment, champion contact leaving the company, feature adoption plateau (no new features used in 60 days), and declining NPS scores on consecutive surveys. 3. **Score risk per account** — Compute a weighted composite score from 0 (healthy) to 100 (imminent churn) for each account. Weight signals by their predictive power — usage decline typically carries 35% weight, support sentiment 25%, billing signals 20%, a...