Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
Scanned 9/2/2026
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---
name: aarrr-metrics
description: Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
license: MIT
metadata:
author: ClawFu
version: 1.0.0
mcp-server: "@clawfu/mcp-skills"
---
# AARRR Pirate Metrics
> Apply Dave McClure's AARRR framework to measure and optimize growth through the five stages: Acquisition, Activation, Retention, Revenue, and Referral.
## When to Use This Skill
- Building growth dashboards
- Identifying funnel bottlenecks
- Prioritizing growth experiments
- Reporting to investors
- Diagnosing growth problems
## Methodology Foundation
Based on **Dave McClure's AARRR framework** (500 Startups), providing:
- Stage-specific metrics definition
- Funnel conversion analysis
- Prioritization framework
- Experiment design guidance
## What Claude Does vs What You Decide
| Claude Does | You Decide |
|-------------|------------|
| Defines metrics per stage | Specific definitions for your product |
| Identifies bottlenecks | Experiment priorities |
| Suggests experiments | Resource allocation |
| Creates dashboards | Tool selection |
| Benchmarks performance | Acceptable thresholds |
## Instructions
### Step 1: Define Each Stage
**AARRR Stages:**
| Stage | Question | Focus |
|-------|----------|-------|
| **A**cquisition | How do users find you? | Traffic, channels |
| **A**ctivation | Do they have a great first experience? | Onboarding, aha moment |
| **R**etention | Do they come back? | Engagement, habit |
| **R**evenue | Do they pay? | Conversion, monetization |
| **R**eferral | Do they tell others? | Virality, NPS |
### Step 2: Set Stage-Specific Metrics
**Metrics Framework:**
| Stage | Primary Metric | Supporting Metrics |
|-------|----------------|-------------------|
| Acquisition | CAC, Unique visitors | Channel mix, CTR, CPM |
| Activation | Activation rate | Time to activate, drop-off points |
| Retention | D7/D30 retention | DAU/MAU, churn rate |
| Revenue | LTV, ARPU | Conversion rate, ACV |
| Referral | Viral coefficient | NPS, referral rate |
### Step 3: Map Your Funnel
**Funnel Template:**
```
Acquisition → Activation → Retention → Revenue → Referral
100% → 30% → 15% → 5% → 1%
```
Calculate conversion rates between each stage.
### Step 4: Identify Bottlenecks
**Bottleneck Analysis:**
| Drop-off | Diagnosis |
|----------|-----------|
| Acquisition → Activation | Poor onboarding or wrong traffic |
| Activation → Retention | Not finding core value |
| Retention → Revenue | Pricing or value misalignment |
| Revenue → Referral | Not delighting paying users |
### Step 5: Prioritize Experiments
**ICE Framework:**
- **I**mpact: How much will it move the metric?
- **C**onfidence: How sure are we?
- **E**ase: How easy to implement?
## Examples
### Example 1: Complete AARRR Dashboard
**Input:**
```
Build an AARRR dashboard for a B2B SaaS product.
Monthly visitors: 50,000
Signups: 3,000
Activated users: 900
Paying customers: 150
Referrals generating signups: 45
```
**Output:**
```
## AARRR Dashboard: B2B SaaS
### Funnel Overview
```
┌──────────────────────────────────────────────────────────────┐
│ AARRR FUNNEL │
├──────────────────────────────────────────────────────────────┤
│ │
│ ACQUISITION ACTIVATION RETENTION REVENUE REF │
│ 50,000 3,000 900 150 45 │
│ 100% 6% 30% 17% 30% │
│ │
│ ████████████ ████████ ████ ██ █ │
│ │
│ Visitors → Signups → Activated → Paid → Referral │
│ (-94%) (-70%) (-83%) (-70%) │
│ ⚠️ ⚠️ ✅ ⚠️ │
│ │
└──────────────────────────────────────────────────────────────┘
```
---
### Stage-by-Stage Analysis
#### 1. ACQUISITION
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Monthly visitors | 50,000 | - | Baseline |
| Visitor → Signup | 6% | 2-5% | ✅ Good |
| CAC | $45 | <$50 | ✅ Good |
| Signups | 3,000 | - | - |
**Channel Breakdown:**
| Channel | Visitors | Signups | Conv % | CAC |
|---------|----------|---------|--------|-----|
| Organic | 20,000 | 1,500 | 7.5% | $0 |
| Paid Search | 15,000 | 900 | 6% | $60 |
| Content | 10,000 | 450 | 4.5% | $20 |
| Social | 5,000 | 150 | 3% | $80 |
**Analysis:** Acquisition is healthy. Organic performing best.
Paid social has high CAC—consider reallocating.
---
#### 2. ACTIVATION ⚠️ BOTTLENECK
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Signup → Activated | 30% | 40-60% | ⚠️ Below |
| Time to activate | 3.2 days | <1 day | ⚠️ Slow |
| Activation rate | 900/3,000 | - | - |
**Activation Definition:**
"Activated" = Created first project + invited 1 team member
**Drop-off Analysis:**
| Step | Users | Drop-off |
|------|-------|----------|
| Signup complete | 3,000 | - |
| Email verified | 2,400 | -20% |
| Created project | 1,500 | -38% |
| Invited team | 900 | -40% ⚠️ |
**Primary Bottleneck:** "Invite team member" step losing 40%
**Experiment Ideas:**
| Experiment | Hypothesis | ICE |
|------------|------------|-----|
| Skip team invite in onboarding | Removes friction, activate solo first | 8/8/9 = 8.3 |
| In-app invite prompt (day 2) | Right timing, after value seen | 7/7/8 = 7.3 |
| Email team invite reminder | Low effort, catches drop-offs | 5/6/9 = 6.7 |
---
#### 3. RETENTION
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Week 1 retention | 65% | 60%+ | ✅ Good |
| Month 1 retention | 45% | 40%+ | ✅ Good |
| DAU/MAU ratio | 28% | 20%+ | ✅ Good |
| Churn rate | 5%/month | <5% | ✅ OK |
**Retention Curve:**
```
Day 1: 100% ████████████████████
Day 7: 65% █████████████
Day 14: 52% ██████████
Day 30: 45% █████████
Day 60: 38% ████████
Day 90: 32% ██████
```
**Analysis:** Retention is solid. Users who activate tend to stick.
This confirms activation is the primary bottleneck.
---
#### 4. REVENUE
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Activated → Paid | 17% | 5-15% | ✅ Strong |
| Trial → Paid | 17% | 10-25% | ✅ Good |
| ARPU | $89/mo | - | Baseline |
| LTV | $1,068 | - | 12mo × ARPU |
| LTV:CAC | 24:1 | >3:1 | ✅ Excellent |
**Revenue Breakdown:**
| Plan | Customers | % | MRR |
|------|-----------|---|-----|
| Starter ($29) | 60 | 40% | $1,740 |
| Pro ($89) | 75 | 50% | $6,675 |
| Enterprise ($249) | 15 | 10% | $3,735 |
| **Total** | **150** | - | **$12,150** |
**Analysis:** Strong conversion and healthy LTV:CAC.
Not a bottleneck—maintain current approach.
---
#### 5. REFERRAL ⚠️ OPPORTUNITY
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Referral rate | 30% | 20%+ | ✅ Good |
| Viral coefficient | 0.45 | >1 = viral | ⚠️ Below |
| NPS | +32 | >30 | ✅ Good |
| Referral signups | 45 | - | - |
**Calculation:**
- 150 paying customers
- 30% refer (45 referrals)
- Each referral generates 1 signup
- Viral coefficient = 45/150 × (6% activation) = 0.018
- Not organic virality, but healthy referral base
**Experiment Ideas:**
| Experiment | Hypothesis | ICE |
|------------|------------|-----|
| Referral incentive (2-way) | Motivation for both parties | 7/8/7 = 7.3 |
| In-app share prompts | Right moment capture | 6/7/8 = 7.0 |
| Case study program | Social proof + referral | 5/6/6 = 5.7 |
---
### Priority Recommendations
| Priority | Focus | Current | Target | Impact |
|----------|-------|---------|--------|--------|
| 1 | Activation | 30% | 45% | +450 activated/mo |
| 2 | Referral | 30% | 50% | +30 referral signups |
| 3 | Paid channel mix | 6% conv | 8% conv | -$10 CAC |
**90-Day Goal:**
If activation improves 30% → 45%:
- +450 more activated users
- At 17% conversion = +76 new customers
- At $89 ARPU = +$6,800 MRR
---
### Dashboard Template
```
┌────────────────────────────────────────────────────────────┐
│ AARRR MONTHLY DASHBOARD January 2026 │
├────────────────────────────────────────────────────────────┤
│ │
│ ACQUISITION ACTIVATION RETENTION │
│ Visitors: 50K Activated: 900 D30: 45% │
│ Signups: 3K Rate: 30% ⚠️ Churn: 5% │
│ CAC: $45 Time: 3.2d DAU/MAU: 28% │
│ │
│ REVENUE REFERRAL OVERALL │
│ Customers: 150 Referrals: 45 MRR: $12.2K │
│ Conv: 17% Rate: 30% LTV:CAC: 24:1 │
│ ARPU: $89 NPS: +32 Health: 🟢 │
│ │
│ BOTTLENECK: Activation (30% vs 45% target) │
│ EXPERIMENT: Skip team invite onboarding │
│ │
└────────────────────────────────────────────────────────────┘
```
```
### Example 2: Quick Bottleneck Diagnosis
**Input:**
```
Diagnose this funnel:
- 100K visitors, 5K signups (5%)
- 4K activated (80%)
- 1K retained at D30 (25%)
- 200 paid (20%)
```
**Output:**
```
## Quick AARRR Diagnosis
### Funnel
| Stage | Users | Conv | Benchmark | Status |
|-------|-------|------|-----------|--------|
| Acquisition | 100K → 5K | 5% | 2-5% | ✅ Good |
| Activation | 5K → 4K | 80% | 40-60% | ✅ Excellent |
| Retention | 4K → 1K | 25% | 40%+ | ❌ Problem |
| Revenue | 1K → 200 | 20% | 5-15% | ✅ Strong |
### Bottleneck: RETENTION
**Problem:** Only 25% retained at D30 (should be 40%+)
**What this means:**
- Great at attracting and activating
- Users try it, find value initially
- But don't form a habit / come back
- Losing 3,000 activated users monthly
**Likely Causes:**
1. Single-use case (solved problem, left)
2. Not enough ongoing value
3. Poor re-engagement
4. Competitor switching
**Recommended Experiments:**
1. User interviews with churned users
2. Email re-engagement sequence
3. Weekly value summary email
4. Add recurring use case
**Impact if fixed:**
If retention → 40%: 1,600 retained → 320 paid
That's +120 customers/month (+60%)
```
## Skill Boundaries
### What This Skill Does Well
- Structuring growth metrics
- Identifying funnel bottlenecks
- Prioritizing experiments
- Creating dashboards
### What This Skill Cannot Do
- Access your actual data
- Know your specific definitions
- Run experiments
- Guarantee results
## Iteration Guide
**Follow-up Prompts:**
- "Design activation experiments for [problem]"
- "What metrics matter for [stage]?"
- "Create a retention analysis framework"
- "How do we improve [specific conversion]?"
## References
- Dave McClure - Pirate Metrics (500 Startups)
- Reforge Growth Series
- Amplitude Product Analytics
- Mixpanel Growth Framework
## Related Skills
- `product-led-growth` - PLG motions
- `growth-loops` - Sustainable growth
- `startup-metrics` - Investor metrics
## Skill Metadata
- **Domain**: Growth
- **Complexity**: Intermediate
- **Mode**: cyborg
- **Time to Value**: 2-3 hours for full setup
- **Prerequisites**: Analytics access, metric definitions
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