Identify at-risk accounts, diagnose churn drivers, build health scores, and design retention interventions.
---
name: churn-analysis
description: When the user needs to identify at-risk accounts, understand why customers are leaving, reduce churn rate, build health scores, design save plays, or create win-back campaigns.
related: [feedback-synthesis, onboarding-flow, email-marketing]
reads: [startup-context]
---
# Churn Analysis
## When to Use
Activate when a founder needs to identify at-risk accounts before they churn, diagnose churn drivers, build a customer health scoring system, design cancellation or save flows, recover failed payments, or re-engage lost customers. This includes prompts like "our churn is too high," "which customers are about to leave," "why are customers canceling," "build a customer health score," "set up dunning emails," or "create a win-back campaign." Especially relevant for seed/Series A teams managing customers manually without dedicated CS platforms like Gainsight or ChurnZero.
## Context Required
- **From startup-context:** business model (B2B/B2C, subscription/usage-based), current churn rate (logo and revenue), customer segments, pricing tiers, contract terms, product usage data availability, and current retention tooling.
- **From the user:** available data sources (support tickets, Slack channels, NPS scores, usage logs, email logs, billing data), what "healthy" customer behavior looks like, any historical churn patterns, whether churn is primarily voluntary or involuntary, and the specific churn problem to solve.
Work with whatever data is available. Early-stage companies often lack formal CS systems - the skill works with support inboxes, Slack history, and spreadsheets.
## Workflow
1. **Intake and baseline** - Gather all available customer data: customer lists, support tickets, Slack/communication history, NPS scores, usage data, email logs, and billing records. Establish what "healthy" looks like and identify any known churn patterns.
2. **Extract signals** - Analyze four signal categories across every account: support signals, communication signals, usage signals, and commercial signals (see framework below).
3. **Score risk** - Build a composite risk score (0-100) for each account using weighted signal categories. Higher score means higher risk.
4. **Generate save plays** - For high-risk accounts, produce specific interventions: root cause hypothesis, recommended actions, talk tracks for the CS conversation, and escalation triggers.
5. **Build the weekly scorecard** - Compile into a weekly risk report with account-by-account analysis, MRR at risk, trend data, signal distribution, and recommended focus areas.
6. **Design interventions** - For each churn driver identified, design the appropriate intervention: product fix, CS outreach, cancel flow save offer, dunning sequence, or win-back campaign.… load the full skill through Skill Me