aarrr-metrics

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
Acquisition	How do users find you?	Traffic, channels
Activation	Do they have a great first experience?	Onboarding, aha moment
Retention	Do they come back?	Engagement, habit
Revenue	Do they pay?	Conversion, monetization
Referral	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:

• Impact: How much will it move the metric?
• Confidence: How sure are we?
• Ease: 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