Claude
572b8acf8c
Make the library multi-platform without duplicating content. Each skills/<name>/SKILL.md body remains the single source of truth; a new generator renders platform-ready exports from it. - scripts/build-exports.mjs — dependency-free Node generator with a PLATFORMS registry so new platforms (Gemini, Cursor, …) are a few lines. Ships ChatGPT exports at exports/chatgpt/<bundle>/<skill>/SYSTEM_PROMPT.md (172 skills), plus generated index READMEs. Supports --platform and --check. - exports/ — generated ChatGPT system prompts, ready to paste into a Custom GPT. - .github/workflows/check-generated.yml — fails a PR if exports or web/skills.json drift from the source skills. - README "Works With" now documents the ready-to-use exports and regen command. - CHANGELOG + SKILL-AUTHORING-STANDARD note the generated artifacts. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px
5.3 KiB
5.3 KiB
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)
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