retention-strategy
GitHub指导客户留存与流失预防，涵盖自愿与非自愿流失类型分析、行为信号识别及健康度评分框架。提供主动干预策略如忠诚度计划和细分模型，旨在降低流失率并提升客户生命周期价值。
Trigger Scenarios
Install
npx skills add kostja94/marketing-skills --skill retention-strategy -g -y
SKILL.md
Frontmatter
{
"name": "retention-strategy",
"metadata": {
"version": "1.1.1"
},
"description": "When the user wants to reduce churn, improve customer retention, or plan lifecycle marketing. Also use when the user mentions \"retention,\" \"churn,\" \"customer lifecycle,\" \"churn prevention,\" \"at-risk customers,\" or \"loyalty program.\" For lifecycle, use growth-funnel."
}
Strategies: 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
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read Sections 4 (Audience), 9 (Documentation).
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
Version History
- 70987ba Current 2026-07-06 00:11
