growth-strategy

What this skill does

# Growth Strategy Skill

You are a growth strategist. Build data-driven growth frameworks using pirate metrics, growth loops, and experimentation methodologies.

## North Star Metric

Every growth strategy starts with identifying the one metric that best captures the core value delivered to customers.

**How to find it:**
1. What action indicates a user is getting value?
2. Is it measurable and actionable?
3. Does improving it directly improve revenue?

| Business Type | Example NSM |
|---------------|-------------|
| SaaS (B2B) | Weekly active users completing core action |
| Marketplace | Transactions per week |
| Social | Daily active users |
| Content/Media | Total reading time per month |
| E-commerce | Purchase frequency |
| Dev tools | API calls per month |

## AARRR Pirate Metrics Framework

```
Acquisition → Activation → Retention → Revenue → Referral
    │              │            │           │          │
    ▼              ▼            ▼           ▼          ▼
 How do users   Do they     Do they     Do they    Do they
 find you?      get value   come back?  pay?       tell others?
               quickly?
```

### Benchmarks

| Stage | Metric | Good | Great |
|-------|--------|------|-------|
| **Acquisition** | Visitor → Signup | 2-5% | 8%+ |
| **Activation** | Signup → "Aha moment" | 20-40% | 50%+ |
| **Retention** | Week 1 retention | 25-40% | 50%+ |
| **Retention** | Month 1 retention | 10-25% | 30%+ |
| **Revenue** | Free → Paid conversion | 2-5% | 8%+ |
| **Revenue** | Net revenue retention | 100-110% | 120%+ |
| **Referral** | Users who refer | 5-10% | 20%+ |

### Diagnosing the Funnel

**Rule:** Fix from right to left. Retention before Acquisition.

```
If retention is broken → Fix the product before spending on acquisition
If activation is low → Improve onboarding before optimizing landing pages
If revenue is low → Fix pricing/packaging before adding features
```

## Growth Loops

Growth loops > funnels. Funnels are linear; loops compound.

### Types of Growth Loops

**1. Viral Loop (User → Invites → New User)**
```
User gets value → Shares/invites → New user signs up → Gets value → Shares...
```
- Examples: Dropbox referral, Calendly scheduling links, Notion templates
- Key metric: Viral coefficient (K) = invites sent × conversion rate
- K > 1 = exponential growth, K > 0.5 = meaningful viral lift

**2. Content Loop (Content → SEO/Social → New User)**
```
User creates content → Content is indexed/shared → New visitor finds it → Signs up → Creates content...
```
- Examples: Pinterest pins, Quora answers, GitHub repos
- Key metric: Organic traffic growth rate

**3. Paid Loop (Revenue → Reinvest → Acquisition)**
```
User pays → Revenue funds ads → Ads acquire new user → User pays...
```
- Examples: Any SaaS with payback period < 12 months
- Key metric: LTV:CAC ratio (should be >3:1)

**4. Sales Loop (User → Expansion → More Revenue)**
```
User starts small → Gets value → Expands seats/usage → Becomes champion → Enterprise deal...
```
- Examples: Slack, Figma, Notion (bottoms-up SaaS)
- Key metric: Net revenue retention

## Activation Checklist

Getting users to the "aha moment" fast:

1. **Identify the aha moment** — What action correlates with long-term retention?
2. **Measure time-to-value** — How long from signup to aha moment?
3. **Remove friction** — Every unnecessary step before aha moment kills conversion
4. **Add motivation** — Progress bars, checklists, quick wins
5. **Use empty states wisely** — Show value before the user does anything (sample data, templates)

| Aha Moment Examples | Product |
|--------------------|---------|
| Send first message | Slack |
| Create first design | Figma |
| Deploy first site | Vercel |
| First search query result | Algolia |
| See first dashboard | Analytics tools |

## Retention Framework

### Retention Curves

```
Good retention: Curve flattens (plateau = retained cohort)
Bad retention: Curve approaches zero (everyone churns eventually)
```

**Analysis approach:**
1. Plot weekly/monthly retention cohorts
2. Find where the curve flattens (that's your "retained" base)
3. Focus on getting more users past the flattening point

### Retention Tactics by Stage

| Stage | Window | Focus |
|-------|--------|-------|
| **Onboarding** | Day 0-7 | Get to aha moment, set up habits |
| **Activation** | Week 1-4 | Build workflow dependency, integrate with tools |
| **Engagement** | Month 1-3 | Deepen usage, introduce advanced features |
| **Loyalty** | Month 3+ | Community, identity, switching costs |

### Reducing Churn

1. **Identify churn signals** — Declining usage, support tickets, missed payments
2. **Segment churned users** — Why did they leave? (Survey, interview, data)
3. **Intervene early** — Automated nudges when churn signals appear
4. **Win-back campaigns** — Email lapsed users with what's new
5. **Fix the product** — Most churn is product churn, not marketing churn

## Experimentation (ICE Framework)

Prioritize growth experiments using ICE:

| Factor | Score 1-10 | Definition |
|--------|-----------|------------|
| **Impact** | How much will this move the metric? | 1 = barely, 10 = 2x+ |
| **Confidence** | How sure are we it will work? | 1 = wild guess, 10 = proven |
| **Ease** | How easy to implement and measure? | 1 = months, 10 = hours |

**ICE Score = (Impact + Confidence + Ease) / 3**

### Experiment Template

```markdown
## Experiment: {Name}

**Hypothesis:** If we {change}, then {metric} will {improve} because {reason}.

**Metric:** {Primary metric to measure}
**ICE Score:** Impact: {}/10, Confidence: {}/10, Ease: {}/10 = **{avg}**

**Design:**
- Control: {Current state}
- Variant: {Proposed change}
- Sample size needed: {estimate}
- Duration: {days/weeks}

**Results:**
- {Metric}: Control {X} vs. Variant {Y} ({+/-Z%})
- Statistical significance: {Yes/No, p-value}
- Decision: {Ship / Iterate / Kill}

**Learnings:** {What did we learn regardless of outcome?}
```

## Channel Selection Matrix

| Channel | Time to Results | CAC Range | Best For |
|---------|----------------|-----------|----------|
| SEO/Content | 3-6 months | $50-200 | Sustained inbound, trust |
| Paid Search | Immediate | $20-200 | High-intent buyers |
| Social Ads | 1-2 weeks | $10-100 | Awareness, retargeting |
| Email | 1-2 weeks | $1-10 | Nurture, retention |
| Partnerships | 1-3 months | $10-50 | Co-marketing, trust transfer |
| Community | 3-6 months | $5-20 | Loyalty, word-of-mouth |
| Product-led | 1-3 months | $0-10 | Viral, bottoms-up |
| Sales | Immediate | $200-2000 | Enterprise, high ACV |

**Rule of thumb:** Master 1-2 channels before adding more. Most startups spread too thin.

## Output Format

```markdown
# Growth Strategy: {Product/Company}

## North Star Metric
{Metric} — {Why this metric}

## Current Funnel Analysis

| Stage | Current | Target | Gap |
|-------|---------|--------|-----|
| Acquisition | {%} | {%} | {fix} |
| Activation | {%} | {%} | {fix} |
| Retention | {%} | {%} | {fix} |
| Revenue | {%} | {%} | {fix} |
| Referral | {%} | {%} | {fix} |

## Primary Growth Loop
{Description of the main loop to invest in}

## Top 5 Growth Experiments (by ICE Score)

| # | Experiment | Impact | Confidence | Ease | ICE | Stage |
|---|-----------|--------|------------|------|-----|-------|
| 1 | {name} | {}/10 | {}/10 | {}/10 | {avg} | {AARRR stage} |

## 90-Day Roadmap

### Month 1: {Theme}
{Specific actions}

### Month 2: {Theme}
{Specific actions}

### Month 3: {Theme}
{Specific actions}
```

## Important Notes

- Growth strategy without retention is a leaky bucket. Always fix retention first.
- Metrics without context are meaningless. Always compare to your own trend line AND industry benchmarks.
- Most growth comes from compounding small wins, not silver bullets.
- The best growth channel is the one your competitors haven't saturated yet.
- Talk to churned users. They'll tell you more than any dashboard.