# AARRR Metrics (Pirate Metrics)

## Purpose
Track the complete customer journey through five critical stages, identifying the weakest point in your funnel to focus optimization efforts.

## When to Use
- Setting up metrics for a new product
- Diagnosing why growth has stalled
- Prioritizing where to focus improvement efforts
- Aligning team around growth metrics
- Reporting progress to stakeholders

## Process

### 1. Understand the Five Stages

Each stage represents a critical step in the customer journey:

**A - Acquisition:** How do users find you?
**A - Activation:** Do they have a good first experience?
**R - Retention:** Do they come back?
**R - Revenue:** Do they pay?
**R - Referral:** Do they tell others?

### 2. Define Metrics for Each Stage

**Acquisition - "How do users find us?"**

| Metric | Definition | Target |
|--------|------------|--------|
| Total Visitors | Unique visitors from all sources | Growth trend |
| Channel Breakdown | % from each source (organic, paid, social, direct) | Diversified |
| Cost Per Visit | Total spend / Visitors | Decreasing |
| Traffic Quality | % of visits meeting intent signals | >50% |

**Questions to answer:**
- Which channels drive the most traffic?
- Which channels drive the highest quality traffic?
- What's our cost per visit by channel?
- Are we dependent on one channel?

**Activation - "Do they have a good first experience?"**

| Metric | Definition | Target |
|--------|------------|--------|
| Signup Rate | Signups / Visitors | >5-10% |
| Onboarding Completion | % completing key onboarding steps | >80% |
| Time to Value | Time from signup to "aha moment" | <24 hours |
| Activation Rate | % reaching key engagement milestone | >30% |

**Questions to answer:**
- Where do users drop off in onboarding?
- How long until users experience core value?
- What's the definition of "activated"?
- What predicts long-term retention?

**Retention - "Do they come back?"**

| Metric | Definition | Target |
|--------|------------|--------|
| DAU/MAU Ratio | Daily active / Monthly active | >20% |
| Week 1 Retention | % returning in week 1 | >50% |
| Month 1 Retention | % returning in month 1 | >40% |
| Churn Rate | % of customers lost per period | <5%/mo |

**Questions to answer:**
- How many users return after first use?
- What's retention by cohort?
- What behaviors predict retention?
- Why do users churn?

**Revenue - "Do they pay?"**

| Metric | Definition | Target |
|--------|------------|--------|
| Conversion Rate | Paying / Total users | Varies |
| ARPU | Average Revenue Per User | Growth trend |
| LTV | Customer Lifetime Value | >3x CAC |
| MRR/ARR | Monthly/Annual Recurring Revenue | Growth trend |

**Questions to answer:**
- What % of users convert to paid?
- What's the average customer worth?
- Is revenue growing faster than users?
- What's the path from free to paid?

**Referral - "Do they tell others?"**

| Metric | Definition | Target |
|--------|------------|--------|
| NPS | Net Promoter Score | >50 |
| Referral Rate | % of users who refer | >10% |
| Viral Coefficient | New users per existing user | Approaching 1 |
| Referral Conversion | % of referrals who convert | >20% |

**Questions to answer:**
- Would customers recommend us?
- What % of growth is organic?
- What triggers referrals?
- What's the quality of referred users?

### 3. Build Your Funnel Dashboard

Map each stage with:
- Primary metric
- Supporting metrics
- Current value
- Target value
- Trend (improving/stable/declining)

**Example Dashboard:**
```
STAGE      | PRIMARY METRIC     | CURRENT | TARGET | TREND
-----------|--------------------|---------|---------|---------
Acquisition| Monthly Visitors   | 10,000  | 15,000  | ↑
Activation | Signup Rate        | 8%      | 10%     | →
Activation | Time to Value      | 3 days  | 1 day   | ↓
Retention  | Month 1 Retention  | 25%     | 40%     | ↑
Revenue    | Conversion Rate    | 3%      | 5%      | ↑
Referral   | NPS                | 35      | 50      | →
```

### 4. Identify the Constraint

**The Theory of Constraints:** The weakest stage limits overall growth.

To find your constraint:
1. Look for the biggest drop-off between stages
2. Compare your metrics to benchmarks
3. Calculate the impact of improving each stage

**Example Analysis:**
- Acquisition: 10,000 visitors (adequate)
- Activation: 800 signups (8% = okay)
- Retention: 200 Month 1 (25% = PROBLEM)
- Revenue: 60 paying (30% = good)
- Referral: 6 referrals (10% = okay)

**Constraint identified:** Retention (25% Month 1)

Even if you double acquisition to 20,000 visitors:
- With 25% retention: 400 retained → 120 paying
- With 40% retention: 320 retained → 96 paying

Improving retention by 15% would have bigger impact than doubling traffic.

### 5. Optimize the Constraint

Once you've identified the constraint, go deep:

**Retention example actions:**
1. Analyze churned users - what's common?
2. Interview retained users - what kept them?
3. Identify activation metrics that predict retention
4. Test interventions (emails, features, onboarding)
5. Measure cohort retention weekly

**Only move to the next constraint when the current one is solved.**

### 6. Cohort Analysis

Track metrics by cohort (users who started at the same time):

**Why cohorts matter:**
- Aggregate numbers hide improvements
- January users vs. June users may behave differently
- Product changes show up in newer cohorts first

**Example Cohort Retention:**
```
Cohort    | Week 1 | Week 2 | Week 3 | Week 4
----------|--------|--------|--------|--------
January   | 40%    | 30%    | 22%    | 18%
February  | 42%    | 32%    | 25%    | 20%
March     | 48%    | 38%    | 30%    | 25%
```

**Insight:** March cohort is improving - product changes working

### 7. Stage-Specific Experiments

**Acquisition experiments:**
- New channel tests
- Ad creative variations
- SEO content experiments
- Partnership pilots

**Activation experiments:**
- Onboarding flow changes
- Time-to-value reduction
- First-use experience improvements
- Tutorial/guidance additions

**Retention experiments:**
- Engagement features
- Email/notification cadence
- Feature adoption campaigns
- Re-engagement campaigns

**Revenue experiments:**
- Pricing tests
- Plan/tier changes
- Upsell timing
- Payment friction reduction

**Referral experiments:**
- Referral program design
- Share mechanics
- Incentive structures
- Social proof additions

### 8. Synthesis

After setting up your AARRR dashboard:
- Which stage is the current constraint?
- What's causing the drop-off at that stage?
- What experiments could improve it?
- How will you measure improvement?
- What's the target that would make you move to the next constraint?

## Example

**Product**: B2B email outreach tool

### Current Metrics

| Stage | Metric | Current | Target |
|-------|--------|---------|--------|
| Acquisition | Monthly visitors | 5,000 | 10,000 |
| Activation | Trial starts | 500 (10%) | 750 (15%) |
| Activation | Complete first sequence | 150 (30%) | 250 (50%) |
| Retention | Week 2 Active | 75 (50%) | 100 (67%) |
| Revenue | Convert to paid | 30 (40%) | 45 (60%) |
| Referral | Recommend (NPS >8) | 15 (50%) | 25 (83%) |

### Constraint Analysis

Biggest drop-off: Trial start → Complete first sequence (30%)

**Why this matters:**
- 70% of trial users never complete their first outreach sequence
- Users who complete first sequence retain 80%+ at Week 2
- This is THE activation bottleneck

### Experiment Plan

**Hypothesis:** Completing first sequence is too hard/confusing

**Experiments:**
1. **Template library:** Pre-built sequences (reduce blank page problem)
   - Success: First sequence completion >40%
2. **Guided onboarding:** Step-by-step wizard
   - Success: First sequence completion >45%
3. **Time-based help:** If no sequence in 24h, trigger outreach
   - Success: First sequence completion >50%

**Timeline:** Test each for 2 weeks, measure cohort completion rate

### 6-Week Results

| Experiment | First Sequence Completion |
|------------|---------------------------|
| Baseline | 30% |
| Template Library | 38% (+8%) |
| + Guided Onboarding | 52% (+14%) |
| + Time-based Help | 58% (+6%) |

**New metrics after optimization:**
- First sequence completion: 58% (was 30%)
- Week 2 retention: 68% (was 50%)
- Conversion to paid: 48% (was 40%)

**Insight:** Fixing the activation bottleneck (first sequence completion) improved downstream metrics without touching retention or revenue directly. This is the power of focusing on the constraint.

**New constraint:** Acquisition (need more traffic now that funnel converts better)
