# Retention analysis Human Guide

## What This Is For
Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions. It gives the agent a clearer input/output frame for retention analysis: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Retention analysis agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for retention analysis.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Retention analysis guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- **Steepness of initial drop** (D1–D7) — onboarding problem
- **Long-term floor level** — product-market fit indicator
- Acquisition channel (organic vs paid vs referral)
- Onboarding path completed vs not
- Feature usage (power users vs lurkers)
- 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
- 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
- "What was the trigger that led you to cancel/stop?"

## Decision Points And Nuance
The original skill emphasizes: Retention Fundamentals, Retention Metrics Definitions, Retention Investigation Framework, Step 1: Segment the problem, Step 2: Find the inflection points, Step 3: Identify the "aha moment" correlation, Step 4: Qualify the churn, Output Format, Retention Analysis — [Product/Segment] — [Date], Required Inputs.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Don't analyse "retention" — analyse retention for specific cohorts:
- Interview churned users — never skip this. Survey data alone is insufficient.
- Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those who don't.
- [ ] 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

## Copy-And-Paste Prompt
```text
Use the Retention analysis human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the mohitagw15856/pm-claude-skills skill entry for `retention-analysis`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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:**
1. **Steepness of initial drop** (D1–D7) — onboarding problem
2. **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]

---