# Sharp Edges: Growth Strategy

These are the growth mistakes that kill companies or doom them to mediocre outcomes. Each edge represents burned runway, failed experiments, and careers ended by premature scaling.

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## 1. Growth Before Product-Market Fit

**Severity:** Critical

**The Trap:**
You raise money, hire a growth team, and pour resources into acquisition. Users come in, but churn right out. You're spending faster than you're learning. By the time you realize PMF isn't there, you've burned your runway.

**Why It Happens:**
Investor pressure for growth metrics. "Growth" roles were hired. Vanity metrics show progress. It's easier to blame marketing than question the product. Growth activity feels like progress.

**The Fix:**
Growth amplifies what you have. If you have a leaky bucket, growth fills it faster - and empties it faster. Focus on retention before acquisition. Sean Ellis test: "Would 40%+ of users be very disappointed if they could no longer use the product?" Until yes, don't scale growth.

**Detection Pattern:**
- Month 1 retention below 40% (B2C) or 80% (B2B)
- Cohort curves don't flatten
- CAC increasing while retention flat
- Growth team hired before retention metrics solid
- "We'll improve retention as we scale"

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## 2. The Paid Acquisition Addiction

**Severity:** Critical

**The Trap:**
Paid acquisition works initially. You scale it. CAC increases as you exhaust early audiences. You're now dependent on paid, but economics don't work. You can't turn it off because growth stops. You're on a treadmill that's speeding up.

**Why It Happens:**
Paid is predictable and controllable. Results are immediate. It feels professional. Organic channels are slow and uncertain. Boards understand "we spent X, got Y users."

**The Fix:**
Paid should be frosting, not the cake. Build organic growth loops first. Use paid to accelerate proven loops, not replace them. Target LTV:CAC of 3:1+ and payback under 12 months. Have a plan to reduce paid as % of acquisition over time.

**Detection Pattern:**
- >70% of acquisition from paid
- CAC trending up quarter over quarter
- No organic growth when paid turned off
- LTV:CAC below 3:1
- Payback period >12 months

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## 3. The Leaky Bucket Acceleration

**Severity:** Critical

**The Trap:**
You celebrate growing signups while ignoring that activated users and retained users aren't growing at the same rate. Your funnel leaks more than it holds. You're acquiring users just to lose them.

**Why It Happens:**
Acquisition metrics are visible and celebrated. Churn is diffuse and delayed. Nobody gets promoted for "we slowed down churn." Retention requires product work; acquisition requires marketing spend.

**The Fix:**
Fix the bucket before filling it faster. Map your full funnel: Acquisition → Activation → Retention → Revenue → Referral. Find the biggest leak. Fix it before optimizing acquisition. Often the best "growth" work is retention work.

**Detection Pattern:**
- Signup growth >> active user growth
- Activation rate below 30%
- D1/D7/D30 retention declining
- Features launched, retention unchanged
- "More top of funnel" as solution to everything

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## 4. The Single Channel Dependency

**Severity:** High

**The Trap:**
You find a channel that works - SEO, Facebook ads, partnerships - and double down. It becomes 80%+ of your growth. Then it changes: algorithm update, policy change, market saturation. Your growth collapses.

**Why It Happens:**
Doubling down on what works seems smart. Diversification feels like distraction. The working channel demands resources to optimize. New channels require starting over.

**The Fix:**
No single channel should be >40% of sustainable acquisition. Actively invest in 2-3 channels even when one dominates. Build at least one owned channel (email, community). Every channel saturates or gets disrupted - plan for it.

**Detection Pattern:**
- One channel >50% of acquisition
- Channel concentration increasing
- No active experimentation in new channels
- "We have SEO" as the growth strategy
- Panic when channel performance drops

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## 5. The Vanity Metrics Celebration

**Severity:** High

**The Trap:**
Dashboards show hockey sticks: downloads, signups, page views, followers. Boards are impressed. But revenue is flat, retention is poor, and engagement is shallow. You're measuring noise, not signal.

**Why It Happens:**
Vanity metrics go up and to the right. They're easy to move. Press releases write themselves. Real metrics are embarrassing or hard to improve.

**The Fix:**
Define your One Metric That Matters (OMTM) - usually weekly active users doing the core action, or monthly retention. Everything else is interesting context, not success. Ask: "If this metric doubled, would the business double?" If not, it's vanity.

**Detection Pattern:**
- Celebrating downloads/signups as wins
- No cohort-based retention analysis
- Revenue not tracking with user metrics
- "Active users" defined loosely
- Metrics gamed for board presentations

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## 6. The Referral Program Silver Bullet

**Severity:** High

**The Trap:**
"Dropbox grew with referrals, let's add a referral program!" You build it, offer incentives, announce it. Nothing happens. Turns out your product isn't referral-worthy and incentives don't change that.

**Why It Happens:**
Referral success stories are famous. It feels like a growth hack. Easy to build (just add incentives). Avoids facing why users don't refer organically.

**The Fix:**
Referral programs amplify existing referral behavior; they don't create it. If users aren't referring organically, incentives won't help. First question: "Do users tell others about us without incentives?" If no, fix the product. If yes, then referral programs can accelerate.

**Detection Pattern:**
- Referral program launched without organic referral data
- High incentives (desperation sign)
- Low redemption rate
- Referred users churn faster than organic
- "Nobody knows about us" as referral problem

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## 7. The Growth Hack Obsession

**Severity:** Medium

**The Trap:**
You chase "growth hacks" - viral videos, influencer stunts, gamification tricks. Some work briefly. But nothing compounds. You're constantly searching for the next hack instead of building sustainable systems.

**Why It Happens:**
Growth hack stories are exciting. Quick wins feel good. Systematic work is boring. "10x Growth Hack" articles get clicks.

**The Fix:**
Growth hacks are lottery tickets. Growth systems are retirement accounts. Build loops that compound: user creates content → content gets indexed → search brings users → users create content. Each cycle strengthens the next. Sustainable growth is unsexy but wins.

**Detection Pattern:**
- Growth tactics but no growth strategy
- Success not repeatable
- Constant search for "what's next"
- No compounding over time
- Growth team doing random experiments

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## 8. The "We'll Figure Out Monetization Later" Trap

**Severity:** High

**The Trap:**
You grow users aggressively, assuming monetization will follow. When you finally introduce pricing, users revolt or churn. Turns out you trained them to expect free, or the users you attracted can't/won't pay.

**Why It Happens:**
Monetization feels like it will slow growth. Investors push user metrics over revenue. "Get users now, monetize later" worked for some (survivorship bias). Free products grow faster initially.

**The Fix:**
Build monetization into growth strategy from the start. Know who will pay and why. Ideally, monetize early to validate willingness to pay. Growth of paying users > growth of free users. Users attracted by "free" are different from users willing to pay.

**Detection Pattern:**
- Metrics focus on users, not revenue
- No paying customer segment defined
- Pricing discussions deferred
- Assumption that users will convert to paid
- "We'll monetize when we hit X users"

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## 9. The Network Effects Mirage

**Severity:** High

**The Trap:**
You believe you have network effects because more users make the product better. But you don't have true network effects - you have scale effects or content aggregation. When growth slows, there's no self-reinforcing loop.

**Why It Happens:**
"Network effects" sounds good to investors. Confusing supply-side scale with demand-side network effects. Misidentifying content volume as network value.

**The Fix:**
True network effects: each user directly increases value for other users. More friends on Facebook makes Facebook better for you. Test: if you lose 20% of users, does value drop more than 20%? If not, you don't have network effects - you have scale economies.

**Detection Pattern:**
- Claiming network effects without demonstrating them
- Value comes from content, not connections
- Users don't directly interact
- No winner-take-all dynamic
- Competitors easily replicate at scale

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## 10. The Optimization Trap

**Severity:** Medium

**The Trap:**
You A/B test everything. You optimize every step. Conversion rate goes from 2% to 2.3%. But you're optimizing a fundamentally broken funnel. 10x improvement on the wrong thing is still the wrong thing.

**Why It Happens:**
Optimization feels rigorous and data-driven. Small wins accumulate into big reports. It's safer than questioning fundamentals. Testing is a good excuse to avoid hard product decisions.

**The Fix:**
First, ask if you're solving the right problem. A 10% improvement on acquisition doesn't matter if retention is 10%. Prioritize: 1) Fix broken things, 2) Build missing things, 3) Optimize working things. Most growth comes from category changes, not incremental optimization.

**Detection Pattern:**
- A/B testing everything except strategy
- Tiny incremental wins celebrated
- Fundamental funnel issues ignored
- "Data-driven" as excuse for inaction
- No 10x thinking, only % improvement

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## 11. The "More Features = More Growth" Fallacy

**Severity:** High

**The Trap:**
Growth stalls, so you build more features. Users now have more to discover but core engagement doesn't improve. Product becomes bloated and confusing. The features that initially drove growth are buried.

**Why It Happens:**
Features feel like progress. Roadmap stays full. "Users asked for this." Engineering wants to build. Removing features is politically hard.

**The Fix:**
Growth usually comes from doubling down on what works, not adding more. Audit: which features drive activation and retention? Which are used by <10% of users? Consider removing complexity. The best growth lever is often simplification, not addition.

**Detection Pattern:**
- Feature count growing, engagement flat
- New features unused after launch
- Onboarding complexity increasing
- Core feature engagement declining
- "Feature X will drive growth" repeatedly fails

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## 12. The Wrong Audience Acquisition

**Severity:** High

**The Trap:**
Your acquisition is great - lots of signups! But they're the wrong users. They don't have the problem you solve, can't afford your solution, or are tire-kickers. Your top of funnel looks healthy, but nothing converts.

**Why It Happens:**
Volume metrics feel good. Broad targeting is easier. "More is better" mindset. Acquisition and activation teams aren't aligned.

**The Fix:**
Define your ideal customer profile (ICP) with extreme specificity. Measure acquisition quality, not just quantity. Track activation rate by acquisition source. Cut channels that bring low-quality users even if CPL looks good.

**Detection Pattern:**
- High acquisition, low activation
- Activation varies wildly by source
- Sales team says "leads are bad"
- High support volume from new users
- Users don't have problem you solve
