# Anti-Patterns: Growth Strategy

These approaches look like growth strategy but consistently fail to create sustainable, compounding growth.

---

## 1. The Vanity Dashboard

**The Mistake:**
```
Weekly Growth Report:
✅ Downloads: 50,000 (+15%)
✅ Signups: 12,000 (+20%)
✅ Page Views: 2M (+25%)
✅ Social Followers: 100K (+10%)

"Growth is on track!"

[Meanwhile: Active users flat, revenue declining]
```

**Why It's Wrong:**
- Vanity metrics go up while business health declines
- Easy metrics measured because hard metrics embarrass
- Dashboard designed to impress, not inform
- False confidence delays necessary changes
- Team optimizes for wrong targets

**The Fix:**
```
The OMTM Test (One Metric That Matters):

"If this metric doubled, would the business double?"

Downloads doubled → Business doubles? NO
Active users doubled → Business doubles? YES

Build dashboard around OMTM:
- Weekly Active Users doing core action
- Cohort retention curves
- Revenue per user
- Activation rate by source

If a metric doesn't connect to OMTM, remove it.
```

---

## 2. The Growth Team Silo

**The Mistake:**
```
Company Structure:
├── Product Team → Builds features
├── Engineering Team → Ships code
├── Growth Team → "Does growth"
└── Marketing Team → Runs campaigns

Growth Team activities:
- A/B testing button colors
- Optimizing email subject lines
- Running referral promotions
- Managing paid acquisition

[Growth isolated from core product decisions]
```

**Why It's Wrong:**
- Real growth is product-driven, not bolt-on
- Growth team optimizes edges while core leaks
- No authority to fix fundamental issues
- Blamed for problems they can't control
- Creates "growth theater" - activity without impact

**The Fix:**
```
Growth is a lens, not a department.

Model 1: Growth Engineers on Product Teams
- Growth expertise embedded in product
- Growth goals, product authority
- Can change the product, not just the funnel

Model 2: Growth as Cross-Functional Focus
- Product, eng, design, data work on growth together
- Growth sprints with full team
- Growth metrics are everyone's metrics

Model 3: CEO-Led Growth
- CEO owns growth
- All teams accountable to growth metrics
- No handoff between "product" and "growth"

Growth works when it can change the product.
```

---

## 3. The Experiment Factory

**The Mistake:**
```
Q4 Growth Experiments:
- 47 A/B tests run
- 312 experiments in backlog
- 15 "wins" shipped
- Average lift: 2.3%

"We're data-driven and always experimenting!"

[No fundamental improvement in growth rate]
```

**Why It's Wrong:**
- Quantity of experiments ≠ quality of learning
- Small optimizations don't compound
- Testing everything means understanding nothing
- "Experimentation" as excuse for no strategy
- Team busy feeling productive while growth stalls

**The Fix:**
```
Strategy Before Experiments:

1. Define your growth model (on paper)
   Acquisition → Activation → Retention → Revenue → Referral

2. Identify the biggest leak
   Where do you lose the most users/value?

3. Form a hypothesis about WHY
   Not "what to test" but "what we believe"

4. Design experiment to validate hypothesis
   Not "try stuff" but "test belief"

5. Act on learning, not just data
   Winning variant is output; understanding is outcome

10 strategic experiments beat 100 random tests.
```

---

## 4. The Viral Feature Bolt-On

**The Mistake:**
```
Product Manager: "Let's add viral features!"

Implemented:
- Share buttons everywhere
- "Invite friends" popup
- Social login
- Referral program with $10 bonus
- "Powered by [Brand]" badge

Result: 0.1 viral coefficient
[Product is fundamentally not shareable]
```

**Why It's Wrong:**
- Virality is a property of the product, not a feature
- Bolting on share buttons doesn't make things shareable
- Incentivized sharing without organic sharing = low quality
- Users sense desperation
- Viral mechanics without viral value create spam

**The Fix:**
```
Viral Audit Questions:

1. Do users naturally tell others WITHOUT prompting?
   If no → Product isn't share-worthy yet. Fix product.

2. What's the "magic moment" users want to share?
   No clear answer → No viral potential yet.

3. Is sharing selfish or selfless?
   Best: Sharing benefits the sharer (Dropbox: get space)
   Good: Sharing benefits recipient (useful content)
   Bad: Sharing only benefits company (spam)

4. Does sharing happen as part of using the product?
   Calendly: Must share link to use it
   vs. "Share this app" (extra step = friction)

Make the product worth sharing before adding share buttons.
```

---

## 5. The "More Traffic" Solution

**The Mistake:**
```
Problem: Revenue is flat

Diagnosis: "We need more top of funnel"

Solution:
- Increase ad spend 50%
- Launch content marketing
- Add SEO resources
- Partner for distribution

Result: Traffic up 50%, revenue up 5%

"Let's do more of the same, but bigger"
```

**Why It's Wrong:**
- Traffic isn't the bottleneck if conversion is low
- More traffic through a broken funnel = more waste
- Masks activation/retention problems
- CAC increases as you scale
- Eventually hits ceiling anyway

**The Fix:**
```
Growth Bottleneck Analysis:

Traffic → Signup → Activation → Retention → Revenue

Map your funnel with REAL numbers:
100,000 visitors
→ 10,000 signups (10% conversion)
→ 3,000 activated (30% activation) ← PROBLEM
→ 1,500 retained (50% retention)
→ 750 paying (50% monetization)

Find the WORST conversion:
- Signup: 10% (industry: 5-15%) ✓ OK
- Activation: 30% (benchmark: 40%+) ✗ FIX THIS
- Retention: 50% (varies by type)
- Monetization: 50% (varies by model)

Fix the biggest leak BEFORE adding traffic.
"More traffic" is almost never the right answer.
```

---

## 6. The Feature Launch Growth Plan

**The Mistake:**
```
Growth Strategy Document:

Q1: Launch Feature A → Users will grow
Q2: Launch Feature B → Users will grow more
Q3: Launch Feature C → Growth accelerates
Q4: Launch Feature D → Hit targets

[Features ≠ growth strategy]
```

**Why It's Wrong:**
- Features are outputs, not growth mechanisms
- No connection between features and acquisition
- Assumes building = growth (build it and they will come)
- No feedback loops or compounding
- Entire strategy depends on hope

**The Fix:**
```
Features must connect to growth mechanics:

Bad: "Launch collaboration features"
Good: "Launch collaboration features that require
      inviting teammates, creating acquisition loop"

For each feature, answer:
1. How does this acquire new users?
2. How does this activate new users faster?
3. How does this retain existing users?
4. How does this expand revenue?
5. How does this create referrals?

If feature doesn't clearly impact one of these,
it's a product feature, not a growth feature.

Growth strategy = HOW features drive growth,
not WHICH features to build.
```

---

## 7. The Channel Copying

**The Mistake:**
```
"Competitor X is crushing it on TikTok!"
"Company Y grew through SEO!"
"Z raised money with their community strategy!"

→ Let's do all three!

Six months later:
- TikTok: 500 followers
- SEO: Page 7 rankings
- Community: 50 members (40 are employees)
```

**Why It's Wrong:**
- What works for others may not work for you
- Channel success depends on product-channel fit
- You only see their successes, not failures
- Resources spread thin across channels
- No channel gets enough focus to work

**The Fix:**
```
Product-Channel Fit Analysis:

Your product characteristics:
□ Users create content? → SEO potential
□ Inherently shareable? → Viral potential
□ Visual/entertaining? → Social potential
□ High intent purchase? → Paid search potential
□ Requires collaboration? → Network viral potential
□ Long consideration? → Content/email potential

Match YOUR product to channels:

Pinterest works for SEO because:
→ Users create content (pins)
→ Content is searchable (descriptions)
→ Search intent matches product (discovery)

Would that work for your B2B SaaS? Probably not.
Find YOUR natural channel.
```

---

## 8. The Premature Scaling

**The Mistake:**
```
Month 1: 100 users, $50 CAC → Working!
Month 2: "Let's scale to 1000 users!"
Month 3: 1000 users, $150 CAC, 10% retention

"If we just get to 10,000 users,
unit economics will improve"

[They won't]
```

**Why It's Wrong:**
- Early success often isn't repeatable
- First users are not representative
- Scaling multiplies problems, not just users
- CAC increases, quality decreases at scale
- Burns runway proving the wrong thing

**The Fix:**
```
Scale Readiness Checklist:

□ Retention: Do users stick around?
  Month 1 retention > 40% (consumer)
  Month 1 retention > 85% (B2B)

□ Activation: Can you reliably activate users?
  Activation rate > 30%

□ Unit economics: Does each user make money?
  LTV:CAC > 3:1
  Payback < 12 months

□ Repeatability: Can you acquire more like this?
  Tested in multiple segments
  CAC stable across 3x increase

If ANY is no → Fix before scaling.

"We'll figure it out at scale" =
"We'll run out of money faster"
```

---

## 9. The Retention Afterthought

**The Mistake:**
```
Team structure:
- Acquisition team: 15 people
- Growth marketing: $500K/month
- Retention: 1 person, "lifecycle emails"

Strategy:
- Phase 1: Get users
- Phase 2: Get more users
- Phase 3: Maybe look at retention
```

**Why It's Wrong:**
- Acquiring users you can't retain is waste
- Retention is a multiplier on all acquisition
- 10% retention improvement > 10% acquisition improvement
- Acquisition costs money; retention makes money
- Late retention focus means years of lost users

**The Fix:**
```
The Retention Math:

Scenario A: Focus on acquisition
- 10,000 users acquired
- 20% retained
- 2,000 active users

Scenario B: Focus on retention first
- 8,000 users acquired (less budget)
- 40% retained (fixed retention)
- 3,200 active users

Better outcome with LESS acquisition.

Priority Order:
1. Retention (fix the bucket)
2. Activation (fill it efficiently)
3. Acquisition (scale what works)

Build retention team BEFORE scaling acquisition.
```

---

## 10. The "Free Will Convert" Fallacy

**The Mistake:**
```
Strategy: "Get as many free users as possible"

Theory:
- Free users will eventually convert
- Big numbers impress investors
- Monetization comes later

Reality:
- Free users expect free forever
- Large free base creates support burden
- Converts at 0.5%, need 200 free for 1 paid
- Free users ≠ potential paying users
```

**Why It's Wrong:**
- Users attracted by "free" are different population
- Free trains users to expect free
- Free users may never have budget/authority to pay
- Large free user base is liability, not asset
- Delayed monetization means unknown unit economics

**The Fix:**
```
Freemium vs Free Strategy:

Freemium (Good):
- Free tier is intentionally limited
- Clear value in paid tier
- Free users WANT to upgrade
- Conversion path is obvious
- Free tier qualifies leads

Free-for-now (Bad):
- "We'll figure out pricing later"
- No conversion mechanism
- Free users are just... free
- Monetization is bolt-on afterthought

Test willingness to pay EARLY:
- Charge from day 1, even if small
- Free trial (limited time) not free tier (forever)
- Ask free users: "Would you pay for X?"
- Watch behavior, not just survey responses
```

---

## 11. The Growth Hack Lottery

**The Mistake:**
```
Growth team standup:

"Let's try this TikTok thing I saw"
"What about an AI feature?"
"Competitor did a viral campaign"
"I read about this growth hack..."

→ Random experiments with no framework
→ Hope something "goes viral"
```

**Why It's Wrong:**
- Growth hacks are lottery tickets
- No systematic learning or compounding
- Requires constant new ideas
- Team exhausted chasing trends
- Nothing builds on previous work

**The Fix:**
```
Growth System vs Growth Hacks:

Hack: "Viral video might get us users"
System: "Every user action creates discoverable content"

Hack: "Referral bonus might work"
System: "Product requires sharing to function"

Hack: "PR might get us coverage"
System: "We do remarkable things worth covering"

Build systems that compound:
1. Identify repeatable mechanism
2. Instrument and measure
3. Optimize the system
4. Let it compound

One working system > 100 hacks
```

---

## 12. The "We're Different" Dismissal

**The Mistake:**
```
Advisor: "Your retention is 15%. That's concerning."
Founder: "Our users are different. They use us
         intensely for a period then naturally churn."

Advisor: "Have you tested price increases?"
Founder: "Our market is price-sensitive. That won't work."

Advisor: "What about activation rate?"
Founder: "Our product is complex. Low activation is normal."

[Rationalizing mediocre metrics as acceptable]
```

**Why It's Wrong:**
- "We're different" is usually cope
- Prevents learning from benchmarks and best practices
- Excuses avoid hard work of improvement
- Creates blind spots for real problems
- Competitors without excuses will win

**The Fix:**
```
The "We're Different" Test:

When tempted to say "we're different," ask:

1. Is there ANY successful company in similar space?
   If yes → Study them. You're not that different.

2. Have you actually TESTED the assumption?
   "Our market is price-sensitive" → Did you test prices?
   "Users naturally churn" → Did you try to retain them?

3. Would you accept this from an employee?
   Employee: "My work is different, metrics don't apply"
   You: "That's not acceptable"

   Then why accept it from yourself?

Default: You're not different.
Benchmark metrics apply.
Underperformance needs fixing, not explaining.
```

