# Patterns: Growth Strategy

These are the proven approaches that consistently drive sustainable, compounding growth.

---

## 1. The Growth Loop Model

**What It Is:**
Replacing linear funnels with self-reinforcing loops where output becomes input.

**When To Use:**
- Designing growth strategy from scratch
- Auditing why growth isn't compounding
- Building sustainable vs. campaign-driven growth
- Creating growth moats

**The Pattern:**

```
Linear Funnel (Old Model):
Ads → Landing → Signup → Activation → Retention
     [Money in, users out - no compounding]

Growth Loop (New Model):
     ┌──────────────────────────────────┐
     │                                  │
     ▼                                  │
New User → Core Action → Value Created ─┘
              │
              ▼
     Distribution Event
              │
              ▼
        New User (loop)

Example: Pinterest
User → Pins content → Content indexed by Google →
Google search → New user discovers Pin → Signs up → Pins content

Example: Dropbox
User → Invites friend for space → Friend joins →
Friend stores files → Invites their friends

Loop Components:
1. New User Input
2. Core Action (what users DO)
3. Output that drives distribution
4. Distribution event that acquires new user
```

**Why It Works:**
Loops compound; funnels deplete. Each cycle through a loop can bring more users than the last. Linear funnels require constant investment to maintain growth.

---

## 2. The Activation Rate Focus

**What It Is:**
Obsessing over getting new users to their "aha moment" as quickly as possible.

**When To Use:**
- Early stage before scaling acquisition
- When activation rate is below 30%
- When retention is poor for new users
- Redesigning onboarding

**The Pattern:**

```
Find the Aha Moment:
1. Interview retained users: "When did you realize this was valuable?"
2. Analyze behavior: What actions do retained users take that churned don't?
3. Define the moment: "User experiences value when they [specific action]"

Examples:
- Facebook: 10 friends in 14 days
- Slack: 2000 messages sent in a team
- Dropbox: File in Dropbox folder synced across devices
- Twitter: Following 30 accounts

Optimize Time to Aha:
1. Remove friction before Aha moment
2. Accelerate time to Aha (ideally <24 hours)
3. Measure: % of signups who reach Aha moment
4. Segment: D1, D7, D30 activation rates

Intervention Hierarchy:
1. Product changes (best) - make Aha easier to reach
2. Onboarding flows - guide to Aha moment
3. Triggered messages - nudge toward Aha
4. Personal outreach - help reach Aha (worst scalability)
```

**Why It Works:**
Activated users retain. Non-activated users churn regardless of acquisition quality. Doubling activation rate is often easier and more impactful than doubling acquisition.

---

## 3. The Retention Cohort Framework

**What It Is:**
Analyzing user behavior in time-based groups to understand true retention patterns.

**When To Use:**
- Measuring product health
- Diagnosing growth problems
- Tracking improvement over time
- Finding retention levers

**The Pattern:**

```
Cohort Analysis Structure:

Week    W0    W1    W2    W3    W4    W5    W6
Jan      100%  40%   30%   25%   23%   22%   22%
Feb      100%  42%   32%   27%   25%   24%   -
Mar      100%  45%   35%   -     -     -     -

Good Signs:
- Later cohorts perform better (product improving)
- Curves flatten (retained users stay)
- High floor (steady state retention)

Bad Signs:
- Cohorts declining (product degrading)
- No flattening (everyone eventually churns)
- Later cohorts worse (wrong users acquired)

Key Metrics:
- D1/D7/D30 retention: Early engagement
- W4/W8 retention: Medium-term health
- Steady state (month 3+): Long-term value

Benchmarks (vary by category):
- Social apps: 25%+ D1, 10%+ D30
- SaaS B2B: 95%+ monthly, 80%+ yearly
- Consumer subscriptions: 70%+ monthly
- E-commerce: 20-30% repeat in 90 days
```

**Why It Works:**
Aggregate metrics hide cohort-specific issues. A declining product can show growing users (if acquisition growing faster than churn). Cohorts reveal true health.

---

## 4. The Channel-Product Fit Strategy

**What It Is:**
Matching growth channels to product characteristics and user behavior.

**When To Use:**
- Choosing which channels to invest in
- Diagnosing why channels aren't working
- Building channel strategy
- Scaling growth efforts

**The Pattern:**

```
Product Type → Natural Channels:

Viral Products (strong sharing):
- Referrals
- Organic social
- Word of mouth
- Examples: Zoom, Calendly, Figma

Content-Generating Products:
- SEO (user content indexed)
- Social sharing
- Embeds
- Examples: Pinterest, Medium, YouTube

High-Intent Products:
- Paid search
- SEO (commercial intent)
- Review sites
- Examples: Expedia, Nerdwallet

Network Products:
- Network invites
- Team/org virality
- Platform integrations
- Examples: Slack, Notion

High-Touch Products:
- Sales
- Account-based marketing
- Events/conferences
- Examples: Salesforce, enterprise SaaS

Channel-Product Fit Questions:
1. How do users discover solutions in this category?
2. Does our product naturally create shareable moments?
3. What's the buying process for our users?
4. Where do our best users come from today?
```

**Why It Works:**
Channels work when they align with natural user behavior. Fighting against product-channel fit wastes resources. The best channel often seems obvious in retrospect.

---

## 5. The Bullseye Framework

**What It Is:**
Systematic approach to finding the one channel that will drive growth.

**When To Use:**
- Early stage channel discovery
- When current channels aren't working
- Resource-constrained growth
- Testing new markets

**The Pattern:**

```
Step 1: Brainstorm (Outer Ring)
List all possible channels (19 traction channels):
- Viral marketing, PR, SEO, Content, Email
- Paid ads, Sales, Partnerships, Community
- Engineering as marketing, Events, etc.

Step 2: Rank (Middle Ring)
For each channel, score:
- Potential: Could this work for our product?
- Fit: Does it match our users' behavior?
- Cost: Can we test it cheaply?

Select top 6 for testing.

Step 3: Test (Inner Ring)
Run cheap tests on top 3-6 channels:
- $500-$2000 budget each
- 2-4 week timeframe
- Clear success metrics defined

Find the ONE channel that works.

Step 4: Focus
Double down on the winning channel.
Ignore others (for now).
Exhaust the channel before diversifying.

"Most startups don't fail because they can't build
product. They fail because they can't get traction.
And most traction comes from one channel."
```

**Why It Works:**
Spreading thin across many channels means none work well. Finding one channel that works is better than mediocre performance across many.

---

## 6. The Growth Model

**What It Is:**
A quantitative model of how your company grows, making growth levers explicit.

**When To Use:**
- Strategic planning
- Identifying bottlenecks
- Forecasting
- Communicating growth strategy

**The Pattern:**

```
Basic Growth Equation:
Revenue = Users × Conversion × ARPU × Retention

Expanded Model:
┌─────────────────────────────────────────────┐
│                 ACQUISITION                  │
│  Organic + Paid + Viral = New Users         │
└────────────────────┬────────────────────────┘
                     ▼
┌─────────────────────────────────────────────┐
│                ACTIVATION                    │
│  New Users × Activation Rate = Active Users │
└────────────────────┬────────────────────────┘
                     ▼
┌─────────────────────────────────────────────┐
│                 REVENUE                      │
│  Active Users × Conversion × ARPU = Revenue │
└────────────────────┬────────────────────────┘
                     ▼
┌─────────────────────────────────────────────┐
│                RETENTION                     │
│  Active Users × Retention = Retained Users  │
│  Retained Users feed back to Active Users   │
└─────────────────────────────────────────────┘

Model Building Process:
1. Define your funnel stages
2. Get baseline numbers for each
3. Identify which lever moves most (sensitivity analysis)
4. Focus on highest-impact lever
5. Update model as you learn
```

**Why It Works:**
Makes growth tangible and measurable. Shows which lever matters most. Prevents random tactics without strategy.

---

## 7. The RACECAR Framework

**What It Is:**
Balanced approach to growth combining different "engines" of growth.

**When To Use:**
- Building growth strategy
- Diagnosing over-reliance on one engine
- Long-term growth planning
- Balancing short and long-term

**The Pattern:**

```
The RACECAR Model (Brian Balfour):

Two Engine Types:

1. Turbochargers (Short-term, spiky)
   - Paid acquisition
   - PR/Launch events
   - Partnerships
   - Sales
   Characteristics: Buy growth, immediate, diminishing returns

2. Long-Range Engines (Long-term, compounding)
   - SEO/Content
   - Viral loops
   - Network effects
   - Brand
   Characteristics: Earn growth, slow start, compounds

Healthy Mix:
- Early stage: 70% turbo, 30% long-range (need to survive)
- Growth stage: 50/50 (building sustainability)
- Scale stage: 30% turbo, 70% long-range (compounding)

Danger Zone:
- 100% turbo: Growth stops when spending stops
- 100% long-range: May die before engines kick in
```

**Why It Works:**
Turbochargers buy time while long-range engines build. Neither alone is sufficient. The mix should shift as company matures.

---

## 8. The Network Effects Playbook

**What It Is:**
Strategies for building and strengthening network effects.

**When To Use:**
- Marketplace products
- Social products
- Platform products
- Any multi-sided business

**The Pattern:**

```
Network Effect Types:

1. Direct (Same-side)
   More users → More valuable for users
   Example: Phone network, social network

2. Indirect (Cross-side)
   More of one side → More valuable for other side
   Example: Marketplace (buyers ↔ sellers)

3. Data Network Effects
   More users → More data → Better product → More users
   Example: Waze, recommendation engines

Building Network Effects:

1. Solve Chicken-Egg:
   - Single-player mode (Yelp: reviews without network)
   - Seed one side (Uber: pay drivers to be available)
   - Narrow market (Facebook: Harvard only)
   - Fake supply (Reddit: founders posted as users)

2. Reach Critical Mass:
   - Define minimum density for value
   - Hyper-focus on one market/segment
   - Don't expand until density achieved
   - Measure network effect strength

3. Defend the Network:
   - Multi-homing costs (hard to use competitors too)
   - Data/reputation portability (can't take it with you)
   - Relationship lock-in (your network is here)

Network Effect Strength Test:
"If 20% of users left, would value drop by more than 20%?"
If yes → True network effects
If no → Scale effects (valuable but different)
```

**Why It Works:**
Network effects create winner-take-all dynamics. First to critical mass often wins permanently. The moat deepens as network grows.

---

## 9. The Product-Led Growth (PLG) Model

**What It Is:**
Using the product itself as the primary driver of acquisition, conversion, and expansion.

**When To Use:**
- Self-serve products
- Products with viral potential
- Replacing or complementing sales-led growth
- Building scalable growth engine

**The Pattern:**

```
PLG Characteristics:
- Free tier or trial (product is the marketing)
- Self-serve onboarding (no sales needed to start)
- In-product conversion (upgrade when ready)
- Usage-based expansion (grow within accounts)

PLG Flywheel:
Free User → Activated User → Paying User → Team Expansion
     ↑                                          │
     └────── Invites & Word of Mouth ───────────┘

PLG Success Factors:

1. Time to Value < 5 minutes
   - Immediate value without setup
   - Pre-populated data/templates
   - Skip configuration, personalize later

2. Natural Expansion Triggers
   - Collaboration features drive invites
   - Usage limits drive upgrades
   - Success drives team expansion

3. Self-Serve Everything
   - Signup without sales call
   - Upgrade without approval
   - Support through product/docs

4. Viral Loops Built In
   - Sharing outputs (Canva designs)
   - Collaboration invites (Figma)
   - Powered by badges (Typeform)

PLG Metrics:
- PQL (Product Qualified Lead): Actions predicting conversion
- Time to PQL: How long to see buying signals
- Expansion revenue: Revenue from existing accounts
- Natural virality: Users acquired per existing user
```

**Why It Works:**
Product does the selling. Lower CAC than sales-led. Scales without linear headcount growth. Users who convert themselves retain better.

---

## 10. The LTV:CAC Optimization Framework

**What It Is:**
Balancing customer lifetime value against acquisition cost for profitable growth.

**When To Use:**
- Evaluating channel efficiency
- Setting acquisition budgets
- Scaling paid growth
- Investor discussions

**The Pattern:**

```
Core Metrics:

CAC (Customer Acquisition Cost):
Total Acquisition Spend / New Customers
Include: ads, sales, marketing salaries, tools

LTV (Lifetime Value):
ARPU × Gross Margin × Average Lifetime
Or: ARPU × Gross Margin / Churn Rate

Ratio Interpretation:
LTV:CAC < 1:1 → Losing money per customer (death)
LTV:CAC 1:1-3:1 → Break even to healthy (caution)
LTV:CAC 3:1+ → Healthy (invest more)
LTV:CAC 5:1+ → Under-investing in growth (grow faster!)

Payback Period:
Months to recover CAC from margin
Target: <12 months (B2C), <18 months (B2B)

Optimization Levers:

Improve LTV:
- Increase ARPU (pricing, upsell)
- Improve retention
- Increase margin
- Cross-sell additional products

Reduce CAC:
- Improve targeting
- Better creative/conversion
- Leverage organic channels
- Optimize funnel conversion

Real-World Benchmark:
- SaaS: LTV:CAC 3:1+, payback <18 months
- E-commerce: LTV:CAC 2-4:1, payback <6 months
- Consumer sub: LTV:CAC 2:1+, payback <12 months
```

**Why It Works:**
Unit economics determine scalability. High LTV:CAC means profitable scaling. Low ratio means growth burns money.
