retention-strategy
GitHub指导客户留存与流失预防策略，涵盖流失类型分析、健康度评分及主动干预方案，旨在通过优化用户生命周期提升留存率和盈利能力。
触发场景
安装
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
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
版本历史
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26cef34
当前 2026-09-27 19:02
重构项目上下文读取逻辑，将 Contextus 设为唯一真实来源（SSOT），替代原有的 .claude/.cursor 路径检查。
- 70987ba 2026-07-06 00:11
