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%, and engagement metrics 20%. Adjust weights based on historical churn correlation data if available. Accounts missing data for a signal category receive a neutral score for that dimension with a data-quality flag.

4. Segment into risk tiers — Bucket accounts into four tiers based on their composite score: Critical (75-100) — immediate intervention required, likely to churn within 30 days. High (50-74) — concerning trends, intervention needed within 2 weeks. Medium (25-49) — early warning signs, monitor and engage proactively. Healthy (0-24) — on track, maintain regular cadence.

5. Generate intervention recommendations — For each tier, produce specific action plans. Critical: executive sponsor outreach, emergency success plan, potential concessions or credits. High: CSM-led deep dive call, custom training session, product roadmap preview. Medium: automated check-in email sequence, in-app tips targeting underused features, invite to community events. Healthy: upsell/cross-sell opportunity identification, referral program invitation.

Usage

Provide account data or describe the account portfolio you want analyzed. The agent will compute risk scores and return tiered recommendations.