retention-strategy by kostja94/marketing-skills
npx skills add https://github.com/kostja94/marketing-skills --skill retention-strategy指导客户留存和流失预防。获取新客户的成本比留存现有客户高出5-25倍;留存率提高5%可使盈利能力提升25-95%。在减少流失、构建留存计划或识别风险客户时使用此技能。
调用时机:在首次使用时,如果有助于理解,可以用1-2句话介绍此技能涵盖的内容及其重要性,然后提供主要输出。在后续使用或用户要求跳过时,直接进入主要输出。
首先检查项目上下文:如果存在 .claude/project-context.md 或 .cursor/project-context.md 文件,请阅读第4节(受众)和第9节(文档)。
识别:
| 类型 | 占比 | 原因 |
|---|---|---|
| 自愿流失 | 60–80% | 定价、功能缺失、引导不佳、客户关系 |
| 非自愿流失 |
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
| 20–40% |
| 支付失败、卡片过期、账单问题 |
可预测性:大多数流失行为可以通过行为信号在取消前30-90天预测到。
| 方法 | 转化率 |
|---|---|
| 被动(取消后) | 15–20% |
| 主动(决策前) | 60–80% |
从滞后指标转向早期预警系统。
| 策略 | 用途 |
|---|---|
| 健康度评分 | 行为 + 交易 + 关系信号 |
| 忠诚度计划 | 提升留存率5-15个百分点 |
| 用户分群 | 针对风险用户的预测建模 |
| 新用户引导 | 早期防止价值实现不足 |
| 催款管理 | 重试逻辑;针对非自愿流失的卡片到期前更新 |
| 维度 | 用途 |
|---|---|
| 产品价值 | 注册;功能使用;支付 |
| 营销价值 | 客户评价;客户案例;网络研讨会嘉宾;反馈、错误报告、功能请求 |
| 反馈分析 | 电子邮件、社区、评论——AI辅助分析;按影响优先级排序;分派给产品团队或运营团队 |
避免:仅将用户视为月活用户/注册分母。关于创作者生态系统,请参见 creator-program。
留存发生在转化之后;是对客户成功的持续投资,而非孤立的营销活动。绘制触点地图:引导 → 采用 → 扩展 → 续订。
每周安装量
200
仓库
GitHub 星标数
244
首次出现
2026年3月5日
安全审计
安装于
cursor183
gemini-cli182
kimi-cli182
codex182
opencode182
github-copilot182
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.
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read Sections 4 (Audience), 9 (Documentation).
Identify:
| 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.
| Approach | Conversion |
|---|---|
| Reactive (after cancel) | 15–20% |
| Proactive (before decision) | 60–80% |
Move from lagging indicator to early warning systems.
| 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 |
| 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.
Retention occurs after conversion; ongoing investment in customer success, not isolated campaigns. Map touchpoints: onboarding → adoption → expansion → renewal.
Weekly Installs
200
Repository
GitHub Stars
244
First Seen
Mar 5, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
cursor183
gemini-cli182
kimi-cli182
codex182
opencode182
github-copilot182
DOCX文件创建、编辑与分析完整指南 - 使用docx-js、Pandoc和Python脚本
47,500 周安装