# Retention Analysis

> 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.

- Skill: `mohitagw15856/retention-analysis` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add mohitagw15856/retention-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mohitagw15856/retention-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Mohit Aggarwal (https://skillmd.com/u/mohitagw15856)
- Updated: 2026-09-07
- Page: https://skillmd.com/skills/mohitagw15856/retention-analysis

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# 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.

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## 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 |

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## 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?"

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## 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.

## Scoring Rubric (0–40)

Score any output of this skill before handing it over; 32+ is ship-quality.

| Dimension | 0 | 5 | 10 |
|---|---|---|---|
| Curve diagnosis | Reports a retention number without curve shape | Shape shown but not interpreted | Flattening vs trending-to-zero explicitly diagnosed and tied to what it means (PMF vs onboarding problem) |
| Cohort discipline | All users lumped into one blended rate | Cohorts split but read as a table dump | Cohorts segmented before analysis, with the divergent cohort called out and explained |
| Aha-moment linkage | Activation never connects to retention | Correlation claimed without data or caveat | The behavior separating retained from churned users identified with evidence, or honestly flagged unknown with a plan to find it |
| Intervention specificity | "Improve onboarding"-grade advice | Specific actions but no measurement plan | Interventions name the user moment they target, plus a monitoring plan with an alert threshold and churned-user interviews |

## 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*