Churn Analyst
$1.99OfficialAnalyze customer churn: cohort analysis, churn signals, at-risk scoring, retention experiments, and win-back strategies.
What you get
- ✓8-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 3 min read
- Requires
- Works with any modern AI assistant
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes
Preview
When to use
Use this skill when customer retention is under pressure, when you need to diagnose why customers leave, or when building a systematic retention program. It is appropriate before launching a save-desk or customer-success motion, when preparing a board-level retention narrative, or when a specific segment shows deteriorating retention. Reach for it whenever churn is being treated as a single number rather than a diagnosable phenomenon. Avoid it for one-off customer save attempts; those are operational, not analytical.
Inputs to gather
- Customer-level event data: signups, activations, feature usage, billing changes, downgrades, cancellations, with timestamps
- Customer at
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