retention-analysis
GitHub指导产品团队进行留存分析、流失调查及参与度深挖。通过定义核心指标、分群诊断、定位拐点与“顿悟时刻”，结合用户访谈，输出包含根因假设、PMF信号评估及优先级干预措施的详细分析报告。
触发场景
安装
npx skills add mohitagw15856/pm-claude-skills --skill retention-analysis -g -y
SKILL.md
Frontmatter
{
"name": "retention-analysis",
"description": "Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU\/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions."
}
Retention Analysis Skill
Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions.
Retention Fundamentals
The retention curve has two components:
- Steepness of initial drop (D1–D7) — onboarding problem
- Long-term floor level — product-market fit indicator
A product with PMF has a retention curve that flattens. If it trends to zero, you have a PMF problem, not an onboarding problem. Name this distinction explicitly.
Retention Metrics Definitions
| Metric | Formula | What It Tells You |
|---|---|---|
| D1 Retention | Users who return on day 2 ÷ new users day 1 | Quality of first experience |
| D7 Retention | Users active on day 8 ÷ users who joined 7 days ago | Early habit formation |
| D30 Retention | Users active on day 31 ÷ users who joined 30 days ago | Product-market fit signal |
| DAU/MAU Ratio | Daily active users ÷ monthly active users | Stickiness (>20% good, >50% excellent) |
| Churn Rate | Users lost in period ÷ users at start of period | Monthly or annual |
| Net Revenue Retention | MRR at end of period ÷ MRR at start (same cohort) | Revenue health including expansion |
Retention Investigation Framework
Step 1: Segment the problem
Don't analyse "retention" — analyse retention for specific cohorts:
- New vs returning users
- Paid vs free
- Acquisition channel (organic vs paid vs referral)
- Onboarding path completed vs not
- Feature usage (power users vs lurkers)
Step 2: Find the inflection points
Where does the drop happen? D1? D7? Month 3?
- D1 drop → First session experience
- D7 drop → Habit loop not formed
- D30 drop → Value not delivered at depth
- Month 3+ drop → Boredom, competition, or lifecycle event
Step 3: Identify the "aha moment" correlation
Which early behaviour predicts long-term retention?
- Run correlation: users who did [X] in first 7 days vs 30-day retention
- Common patterns: connected an integration, invited a teammate, completed a core action N times
Step 4: Qualify the churn
Interview churned users — never skip this. Survey data alone is insufficient.
- "What was the trigger that led you to cancel/stop?"
- "What were you trying to accomplish that you couldn't?"
- "What would need to change for you to come back?"
Output Format
Retention Analysis — [Product/Segment] — [Date]
Question: [Specific retention question being answered] Period Analysed: [Date range] Segment: [Which users]
Current Retention Snapshot:
| Metric | Current | Industry Benchmark | Status |
|---|---|---|---|
| D1 Retention | [X%] | 25–40% | 🔴/🟡/🟢 |
| D7 Retention | [X%] | 10–25% | 🔴/🟡/🟢 |
| D30 Retention | [X%] | 5–15% | 🔴/🟡/🟢 |
| DAU/MAU | [X%] | 10–20% typical | 🔴/🟡/🟢 |
Retention Curve Shape: [Flattening / Still declining / Trending to zero] PMF Signal: [Strong / Weak / Absent — based on curve shape]
Root Cause Hypotheses:
| Hypothesis | Evidence | Confidence | Test |
|---|---|---|---|
| [Cause] | [Data point] | H/M/L | [How to validate] |
"Aha Moment" Correlation: Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those who don't.
Recommended Interventions:
| Intervention | Target Drop | Expected Lift | Effort | Priority |
|---|---|---|---|---|
| [Specific change] | D1 / D7 / D30 | [X%] | S/M/L | 1/2/3 |
Monitoring Plan:
- Metric to track: [X]
- Review cadence: [Weekly / Monthly]
- Alert threshold: [If X drops below Y, investigate immediately]
Required Inputs
Ask the user for these if not provided:
- Product and business model (SaaS / consumer app / marketplace / other)
- Current retention metrics (D1, D7, D30 if available)
- Segment to analyse (all users / paid / free / a specific cohort)
- Key question to answer (why is retention dropping? what drives retention?)
- Available data (analytics events, churn surveys, interview notes)
Deeper Materials
This skill ships with support files — use them when they are available:
references/curve-reading.md— Reading Retention Curves Without Fooling Yourself. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.templates/retention-readout.md— a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.
Quality Checks
- Retention curve shape is diagnosed (flattening vs trending to zero = PMF vs onboarding)
- Cohorts are segmented before analysis (not all users lumped together)
- "Aha moment" correlation is identified or flagged as unknown
- Interventions are specific (not "improve onboarding")
- Churned user interviews are recommended (not just data analysis)
- Monitoring plan includes an alert threshold
Anti-Patterns
- Do not recommend "improve onboarding" without specifying what specific step to change and why
- Do not analyse retention without segmenting by cohort — aggregate retention curves hide cohort-specific patterns
- Do not treat DAU/MAU below 5% as a retention problem — at that level, it is a product-market fit problem
- Do not skip qualitative research — churned user interviews reveal reasons that quantitative data cannot
- Do not set a monitoring alert without specifying the threshold that triggers it
Guidelines
- Never recommend "improve onboarding" without specifying what to change and why
- Benchmark against industry — consumer apps, SaaS, and marketplaces have very different retention norms
- If DAU/MAU is below 5%, that's a PMF conversation, not a retention tactics conversation
- Always recommend talking to churned users — no amount of data replaces understanding the reason
版本历史
- a38bc30 当前 2026-07-05 11:11
