About retention-strategy
A customer retention and churn-prevention skill for growth and customer success teams. It helps classify churn, interpret risk signals, and plan interventions across the customer lifecycle.
Key features
- 01Distinguish voluntary and involuntary churn
- 02Identify behavioral and relationship signals
- 03Plan retention by lifecycle stage
- 04Health scoring and at-risk segmentation
- 05Feedback analysis and intervention planning
Use cases
- 01Analyze cancellations and payment failures
- 02Plan early interventions for at-risk customers
- 03Improve onboarding to support value realization
- 04Route customer feedback to product or operations
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/retention-strategyin Claude Code to confirm it loaded.
Details
From the project READMEStrategies: Retention
Guides customer retention and churn prevention. Acquiring new customers costs 5–25× more than retaining; 5% retention improvement can increase profitability 25–95%. Use this skill when reducing churn, building retention programs, or identifying at-risk customers.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Project context: Read root contextus.md when present and load only the modules relevant to this task. Without Contextus, use available project material or user-provided facts and ask for missing information; do not create a parallel context system.
Identify:
- Churn type: Voluntary (active cancel) vs involuntary (payment failure)
- Signals: Login frequency, feature usage, support tickets
- Stage: Onboarding, expansion, renewal
Churn Types
| Type | Share | Causes |
|---|---|---|
| Voluntary | 60–80% | Pricing, missing features, poor onboarding, relationship |
| Involuntary | 20–40% | Payment failures, expired cards, billing |
Predictability: Most churn is predictable 30–90 days before cancellation via behavioral signals.
Proactive vs Reactive
| Approach | Conversion |
|---|---|
| Reactive (after cancel) | 15–20% |
| Proactive (before decision) | 60–80% |
Move from lagging indicator to early warning systems.
Retention Strategies
| Strategy | Use |
|---|---|
| Health scoring | Behavioral + transactional + relationship signals |
| Loyalty programs | 5–15 percentage point retention lift |
| Segmentation | Predictive modeling for at-risk |
| Onboarding | Prevent low value realization early |
| Dunning | Retry logic; pre-expiry card updates for involuntary |
User Value & Feedback
| Dimension | Use |
|---|---|
| Product value | Registration; feature usage; payment |
| Marketing value | Testimonials; customer stories; webinar guests; feedback, bug reports, feature requests |
| Feedback analysis | Email, community, reviews—AI-assisted analysis; prioritize by impact; route to product vs ops |
Avoid: Treating users only as MAU/registration denominators. See creator-program for creator ecosystem.
Lifecycle Integration
Retention occurs after conversion; ongoing investment in customer success, not isolated campaigns. Map touchpoints: onboarding → adoption → expansion → renewal.
Output Format
- Churn analysis (voluntary vs involuntary; signals)
- Retention tactics (by stage)
- Health score framework (if applicable)
- Intervention playbook (at-risk triggers)
Related Skills
- email-marketing: Onboarding sequences; win-back campaigns
- pmf-strategy: Retention as PMF signal; churn as anti-signal
- cold-start-strategy: First users; differs from retention
- analytics-tracking: Usage data; churn signals
- traffic-analysis: Attribution; retention cohort analysis