aarrr-metrics
정보
이 스킬은 AARRR(해적 지표) 프레임워크를 적용하여 개발자들이 제품 성장을 측정하고 최적화하는 데 도움을 줍니다. 단계별 핵심 성과 지표(KPI)를 정의하고, 퍼널 전환율을 분석하며, 성장 실험의 우선순위를 설정하는 방법을 안내합니다. 대시보드를 구축하고, 병목 현상을 파악하며, 애플리케이션 내 성장 문제를 진단하는 데 활용하세요.
빠른 설치
Claude Code
추천npx skills add guia-matthieu/clawfu-skills -a claude-code/plugin add https://github.com/guia-matthieu/clawfu-skillsgit clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/aarrr-metricsClaude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요
문서
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 motionsgrowth-loops- Sustainable growthstartup-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
GitHub 저장소
연관 스킬
llamaguard
기타LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.
cost-optimization
기타이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.
quantizing-models-bitsandbytes
기타이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.
dispatching-parallel-agents
기타이 Claude Skill은 3개 이상의 독립적인 문제를 동시에 조사하고 해결하기 위해 다중 에이전트를 배치합니다. 공유 상태나 의존성 없이 해결 가능한 무관련 장애 시나리오에 맞게 설계되었습니다. 핵심 기능은 병렬 문제 해결로, 각 독립 문제 영역마다 하나의 에이전트를 할당하여 효율성을 극대화합니다.
