Growth Strategy

Growth Strategy

Comprehensive growth strategy combining technical SEO/SMO/CRO implementation with strategic growth frameworks.

Quick Reference

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Part 1: Core Principles

Foundational Truths

• Growth follows product-market fit, never precedes it
• Retention is the foundation; acquisition without retention is a leaky bucket
• The best growth is product-driven, not marketing-driven
• Compound effects beat linear efforts
• Every growth channel eventually saturates
• Network effects are the ultimate moat
• Premature growth destroys companies

Growth Is Not Marketing

Growth is the systematic application of product, engineering, and data to create compounding user acquisition, activation, and retention. It's a mindset, not a department.

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Part 2: Growth Loops Framework

Why Loops, Not Funnels

Funnels: Linear. Pour effort in, get results out, start over. Loops: Each cycle generates fuel for the next cycle.

The key shift: Move from "How do we get more users?" to "How does each user we acquire generate more users?"

Loop Types

Critical Patterns

Platform Cycles

New platforms open, then close. Time your bets correctly:

• Early: High reach, low competition
• Mid: Reach peaks, competition grows
• Late: High competition, diminishing returns

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Part 3: SEO × SMO × CRO Framework

SEO Checklist

Page-Level

• [ ] <title> — unique, 50-60 chars, primary keyword
• [ ] <meta name="description"> — 150-160 chars
• [ ] <link rel="canonical"> — self-referencing
• [ ] Single <h1> with primary keyword
• [ ] Primary keyword in first 100 words
• [ ] Descriptive alt text on all images
• [ ] Internal links to related pages

Site-Level

• [ ] robots.txt not blocking important resources
• [ ] sitemap.xml up to date
• [ ] HTTPS everywhere; mobile-friendly
• [ ] Core Web Vitals passing (LCP < 2.5s, CLS < 0.1, INP < 200ms)

OGP / Twitter Cards

<meta property="og:type" content="website">
<meta property="og:title" content="Page Title">
<meta property="og:description" content="Description">
<meta property="og:image" content="https://example.com/og-image.png">
<meta property="twitter:card" content="summary_large_image">

CRO Core

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Part 4: Growth Models

The LTV Equation

LTV = (Average Revenue Per Customer × Average Customer Lifespan) - CAC

Unit Economics

• LTV:CAC ratio: 3:1 minimum for sustainable growth
• Payback period: < 12 months preferred
• CAC: Cost to acquire a customer
• ARPU: Average revenue per user

AARRR Framework

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Part 5: Network Effects

Types of Network Effects

Building Network Effects

1. Cross-side presence: Ensure both sides of marketplace exist 2. Liquidity thresholds: Hit critical mass in each segment 3. Switching costs: Users invest in platform 4. Platform stickiness: Integration with workflows

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Part 6: Product-Led Growth (PLG)

PLG Core Principles

• Product as the main driver of acquisition
• Free trials and freemium models
• In-product virality
• Self-serve onboarding
• Usage-based expansion

PLG Metrics

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Part 7: Growth Experimentation

ICE Framework

Test Execution

• Document hypothesis clearly
• Define primary and guardrail metrics
• Calculate required sample size
• Wait for statistical significance (95%)
• Run full business cycle (1-2 weeks minimum)

What NOT to Test

• Button colors before understanding objections
• Copying competitors blindly
• Optimizing without funnel context

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Part 8: Growth Team & Timing

When to Hire Growth

Growth Team Structure

• Individual Contributor: Run experiments
• Growth Manager: Prioritize and coordinate
• Growth Lead: Strategy and team management

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Part 9: Key Frameworks Summary

Andrew Chen's Wisdom

• Marketplace dynamics
• Network effects as moat
• Platform distribution
• Cold start problem

Brian Balfour's Frameworks

• Growth loops methodology
• Platform cycles
• Paid ≠ loop insight

Casey Winters' Playbooks

• Kindle before fire (non-scalable → scalable)
• Product-led sales
• PQA > PQL

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Common Mistakes

1. Premature scaling — Growth before PMF burns cash 2. Acquisition without retention — Leaky bucket 3. Chasing channels — Without understanding loops 4. Vanity metrics — Focus on leading indicators 5. One-hit wonders — Not building compounding systems

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Related Skills

• product-market-fit-analysis: For PMF assessment
• conversion-rate-optimization: For CRO implementation
• customer-success-and-retention: For retention strategy
• ab-test-setup: For growth experiments

Growth Strategy

Patterns

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Name

Retention Before Acquisition

Description

Fix retention first, then scale acquisition. Leaky buckets don't fill.

When

Building sustainable growth systems

Example

Don't: Run ads to drive signups when Week 1 retention is 20% Do: Improve Week 1 retention to 60%, then scale acquisition The retention curve is your growth strategy. Everything else is tactics.

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Name

Growth Loop Architecture

Description

Design systems where users create value that attracts more users

When

Building compounding growth mechanisms

Example

Yelp: Reviews → SEO → Users → Reviews Dropbox: Referrals → Storage → Sharing → Referrals TikTok: Content → Algorithm → Viewers → Creators Map the loop. Accelerate each stage. Compound.

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Name

Single Channel Dominance

Description

Master one channel before diversifying

When

Early growth or entering new markets

Example

Airbnb: Craigslist integration until millions of listings PayPal: eBay penetration until dominant Don't: Try 10 channels at 10% effort each Do: One channel at 100% until you own it

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Name

Magic Moment Acceleration

Description

Identify the "aha" moment and ruthlessly reduce time to reach it

When

Optimizing activation and early retention

Example

Facebook: "7 friends in 10 days" Slack: "2,000 messages sent by team" Find your magic moment. Measure time to reach it. Shorten it.

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Name

Unit Economics Before Scale

Description

Prove LTV > CAC with margin before scaling spend

When

Evaluating channel viability and scaling decisions

Example

LTV = $300, CAC = $100, margin = 3x → Scale LTV = $100, CAC = $120 → Fix product or targeting, don't scale Math doesn't lie. Feelings do. Run the numbers.

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Name

Qualitative Before Quantitative

Description

Understand why users behave before optimizing what they do

When

Investigating growth opportunities or blockers

Example

Don't: Just A/B test button colors blindly Do: Watch 20 users onboard. Find the confusion. Fix it. Then optimize. Insights from 10 user interviews beat 10,000 data points without context.

Anti-Patterns

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Name

Premature Scaling

Description

Pouring fuel on a fire that isn't burning

Why

Amplifies a broken experience. Burns money faster. Teaches nothing.

Instead

Get to strong retention first. Then scale. Growth follows product-market fit, never precedes it.

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Name

Vanity Metrics Obsession

Description

Optimizing for signups, downloads, or traffic instead of retention and revenue

Why

These metrics are easy to game and don't predict business success

Instead

Track cohort retention, engaged users, revenue per user. Metrics that predict survival.

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Name

Channel Hopping

Description

Constantly switching channels before learning from any

Why

No deep learning. No compounding. No expertise.

Instead

Commit to one channel for 3 months minimum. Learn everything. Then evaluate.

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Name

Growth Hacks Over Systems

Description

Chasing viral tricks instead of building sustainable loops

Why

Hacks expire. Platforms close loopholes. What worked yesterday fails tomorrow.

Instead

Build loops that work because they create value, not because they exploit.

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Name

Product-Market Fit Delusion

Description

Claiming PMF when retention is weak and growth is paid

Why

You're lying to yourself. The market decides PMF, not you.

Instead

Honest retention metrics. If < 40% Week 1 retention, you don't have PMF yet.

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Name

Copying Competitor Channels

Description

Assuming what works for competitors will work for you

Why

Different products, different stages, different resources. Channels are context-dependent.

Instead

Test based on your unique value prop and customer behavior. Find your own channel-product fit.

Growth Strategy - Sharp Edges

Growth Before Pmf

Id

growth-before-pmf

Summary

Pouring resources into acquisition before product-market fit

Severity

critical

Situation

Raise money, hire growth team, pour resources into acquisition. Users come in, churn right out. Spending faster than learning. By the time you realize PMF isn't there, runway is burned.

Why

Growth amplifies what you have. Leaky bucket fills faster—and empties faster. You're accelerating failure. Every dollar spent acquiring users who churn is wasted. You learn nothing except how to lose money.

Solution

Focus on retention before acquisition:

Growth without retention is waste

Sean Ellis test:

"Would 40%+ of users be very disappointed if they could no longer use the product?" Until YES, don't scale growth.

PMF signals to hit first:

• Month 1 retention >40% (B2C) or >80% (B2B)
• Cohort curves flatten (not decline to zero)
• Organic word-of-mouth happening
• Users expanding usage unprompted

Then and only then: Scale growth

Symptoms

• 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

Detection Pattern

Paid Acquisition Addiction

Id

paid-acquisition-addiction

Summary

Dependency on paid acquisition with deteriorating economics

Severity

critical

Situation

Paid acquisition works initially. Scale it. CAC increases as you exhaust early audiences. Now dependent on paid, but economics don't work. Can't turn it off because growth stops. Treadmill speeding up.

Why

Paid is predictable but expensive. CAC trends up as you exhaust cheap audiences. No organic foundation means zero growth when paid stops. You're renting customers, not building a business.

Solution

Paid should be frosting, not the cake:

Build organic growth loops first Use paid to accelerate proven loops Not replace them

Healthy metrics:

• LTV:CAC of 3:1 or better
• Payback under 12 months
• <40% of acquisition from paid
• Organic growth when paid turned off

Plan to reduce paid:

Paid % of acquisition should decline over time As organic loops compound

If addicted:

Systematically build organic channels Accept temporary growth slowdown Better than death spiral

Symptoms

• ">70% of acquisition from paid"
• CAC trending up quarter over quarter
• No organic growth when paid turned off
• "LTV:CAC below 3:1"

Detection Pattern

Leaky Bucket Acceleration

Id

leaky-bucket-acceleration

Summary

Celebrating acquisition while ignoring activation and retention

Severity

critical

Situation

Celebrating growing signups. But activated users and retained users aren't growing at same rate. Funnel leaks more than it holds. Acquiring users just to lose them.

Why

Acquisition metrics are visible and celebrated. Churn is diffuse and delayed. Nobody gets promoted for "we slowed down churn." But filling a leaky bucket is useless work.

Solution

Fix the bucket before filling faster:

Map your full funnel: Acquisition → Activation → Retention → Revenue → Referral

Find the biggest leak:

Where do most users fall off? Fix that before optimizing acquisition

Often the best "growth" work is retention work:

10% improvement in retention > 10% more acquisition Retention compounds, acquisition doesn't

Funnel health check:

Activation rate: >30% D1 retention: >25% D7 retention: >10% D30 retention: >5% (B2C; B2B should be higher)

Symptoms

• Signup growth >> active user growth
• Activation rate below 30%
• D1/D7/D30 retention declining
• "More top of funnel" as solution to everything

Detection Pattern

Single Channel Dependency

Id

single-channel-dependency

Summary

Relying on one channel for majority of growth

Severity

high

Situation

Find a channel that works - SEO, Facebook ads, partnerships. Double down. Becomes 80%+ of growth. Then it changes: algorithm update, policy change, market saturation. Growth collapses.

Why

Every channel saturates or gets disrupted. Algorithm changes happen. Policies change. Competition increases. Single channel dependency is existential risk.

Solution

Diversification rules:

No single channel >40% of sustainable acquisition Actively invest in 2-3 channels Build at least one owned channel (email, community)

Even when one dominates:

Keep investing in alternatives New channels require time to mature Can't start from zero when main channel dies

Owned channels are insurance:

Email list you own Community you control Direct relationships

Symptoms

• One channel >50% of acquisition
• Channel concentration increasing
• No active experimentation in new channels
• Panic when channel performance drops

Detection Pattern

Vanity Metrics Celebration

Id

vanity-metrics-celebration

Summary

Measuring noise instead of signal

Severity

high

Situation

Dashboards show hockey sticks: downloads, signups, page views, followers. Boards are impressed. But revenue is flat, retention is poor, engagement is shallow.

Why

Vanity metrics go up and to the right but don't indicate business health. They're easy to move and make good press releases. Real metrics are harder and often embarrassing.

Solution

Define your OMTM (One Metric That Matters):

Usually: Weekly active users doing the core action Or: Monthly retention rate Or: Monthly recurring revenue

Test your metrics:

"If this metric doubled, would the business double?" If not, it's vanity.

Metric hierarchy:

Tier 1: Revenue, retention, profit Tier 2: Activation, engagement depth Tier 3: Signups, traffic, followers

Focus on Tier 1. Tier 3 is context, not success.

Symptoms

• Celebrating downloads/signups as wins
• No cohort-based retention analysis
• Revenue not tracking with user metrics
• Metrics gamed for board presentations

Detection Pattern

Referral Program Silver Bullet

Id

referral-program-silver-bullet

Summary

Expecting referral program to create organic referrals that don't exist

Severity

high

Situation

"Dropbox grew with referrals, let's add a referral program!" Build it, offer incentives, announce it. Nothing happens. Product isn't referral-worthy. Incentives don't change that.

Why

Referral programs amplify existing referral behavior; they don't create it. If users aren't referring organically, incentives won't help. The problem is the product, not the program.

Solution

First question:

"Do users tell others about us without incentives?"

If NO: Fix the product

• Make it remarkable
• Create share-worthy moments
• Build in natural sharing

If YES: Then referral program can accelerate

• Reduce friction
• Add small incentive
• Track and optimize

Referral program design:

• Two-sided incentives (both parties benefit)
• Gate rewards behind activation, not signup
• Monitor for fraud patterns
• Measure referred user quality

Symptoms

• Referral program launched without organic referral data
• High incentives (desperation sign)
• Low redemption rate
• Referred users churn faster than organic

Detection Pattern

Growth Hack Obsession

Id

growth-hack-obsession

Summary

Chasing viral tricks instead of building sustainable systems

Severity

medium

Situation

Chase "growth hacks" - viral videos, influencer stunts, gamification tricks. Some work briefly. Nothing compounds. Constantly searching for the next hack instead of building sustainable systems.

Why

Growth hacks are lottery tickets. Occasionally win big, usually nothing. No compounding. You're always starting over. Meanwhile, competitors build systems that compound.

Solution

Growth hacks = lottery tickets

Growth systems = retirement accounts

Build loops that compound:

User creates content → Content gets indexed → Search brings users → Users create content Each cycle strengthens the next.

System examples:

• Content/SEO loop
• Referral loop
• UGC loop
• Marketplace loop

Sustainable > Viral:

10% monthly compound > occasional spike Unsexy but wins

Symptoms

• Growth tactics but no growth strategy
• Success not repeatable
• Constant search for "what's next"
• No compounding over time

Detection Pattern

Monetization Later Trap

Id

monetization-later-trap

Summary

Growing free users without validating willingness to pay

Severity

high

Situation

Grow users aggressively, assuming monetization will follow. When you introduce pricing, users revolt or churn. Trained them to expect free. Or attracted users who can't/won't pay.

Why

Users attracted by "free" are different from users willing to pay. You've built an audience that doesn't match your business model. Monetization becomes painful instead of natural.

Solution

Build monetization into growth strategy from start:

Know who will pay and why Ideally, monetize early to validate willingness

What matters:

Growth of paying users > Growth of free users

Healthy free-to-paid:

• Free tier adds value to paid (network effects)
• Clear upgrade path
• Paying users from day one
• Unit economics work

Dangerous free:

• No paying customers after months
• "We'll monetize when we hit X users"
• Assuming free users convert

Symptoms

• Metrics focus on users, not revenue
• No paying customer segment defined
• Pricing discussions deferred
• "We'll monetize when we hit X users"

Detection Pattern

Network Effects Mirage

Id

network-effects-mirage

Summary

Claiming network effects that don't actually exist

Severity

high

Situation

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, no self-reinforcing loop.

Why

"Network effects" sounds good to investors. But scale effects are different. True network effects create winner-take-all dynamics. Without them, competitors easily replicate at scale.

Solution

True network effects test:

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 NO → you don't have network effects You have scale economies (different, weaker)

Types of "moats":

Network effects (exponential defensibility) Scale effects (linear defensibility) Content aggregation (no defensibility)

Be honest about which you have

Symptoms

• Claiming network effects without demonstrating them
• Value comes from content, not connections
• Users don't directly interact
• Competitors easily replicate at scale

Detection Pattern

Optimization Trap

Id

optimization-trap

Summary

Optimizing a fundamentally broken funnel

Severity

medium

Situation

A/B test everything. Optimize every step. Conversion rate goes from 2% to 2.3%. But optimizing a fundamentally broken funnel. 10x improvement on the wrong thing is still the wrong thing.

Why

Optimization feels rigorous and data-driven. Small wins accumulate. But you might be polishing a turd. Most growth comes from category changes, not incremental optimization.

Solution

First, ask if you're solving the right problem:

10% improvement on acquisition doesn't matter if retention is 10%

Prioritize:

1. Fix broken things 2. Build missing things 3. THEN optimize working things

10x thinking before optimization:

What would 10x this metric? Usually not A/B testing button colors

Optimization is last step:

Right product → Right audience → Right funnel THEN optimize

Symptoms

• A/B testing everything except strategy
• Tiny incremental wins celebrated
• Fundamental funnel issues ignored
• No 10x thinking, only % improvement

Detection Pattern

More Features More Growth

Id

more-features-more-growth

Summary

Adding features to solve growth problems

Severity

high

Situation

Growth stalls, so build more features. Users now have more to discover but core engagement doesn't improve. Product becomes bloated. Features that initially drove growth are buried.

Why

Features feel like progress. But growth usually comes from doubling down on what works, not adding more. Complexity hurts more than it helps. The best growth lever is often simplification.

Solution

Audit before adding:

Which features drive activation and retention? Which are used by <10% of users?

Consider removing complexity:

Simplification often > addition Focus beats breadth

Growth features are different:

• Referral mechanics
• Share triggers
• Onboarding improvements

These drive growth

Not growth features:

• More settings
• More options
• More complexity

These often hurt growth

Symptoms

• Feature count growing, engagement flat
• New features unused after launch
• Onboarding complexity increasing
• "Feature X will drive growth" repeatedly fails

Detection Pattern

Wrong Audience Acquisition

Id

wrong-audience-acquisition

Summary

Acquiring users who aren't your ideal customers

Severity

high

Situation

Acquisition looks great - lots of signups! But wrong users. They don't have the problem you solve, can't afford your solution, or are tire-kickers. Top of funnel healthy, nothing converts.

Why

Volume metrics feel good. But wrong users waste resources. They don't convert, they don't retain, they clog support. Quality matters more than quantity.

Solution

Define ICP with extreme specificity:

Not: "Businesses that need project management" But: "15-50 person agencies struggling with client communication"

Measure acquisition quality:

Track activation rate by acquisition source Track retention by acquisition source Track LTV by acquisition source

Cut low-quality channels:

Even if CPL looks good Bad users are expensive users

Align acquisition and activation:

If sales says "leads are bad" Acquisition team must adjust

Symptoms

• High acquisition, low activation
• Activation varies wildly by source
• Sales team says "leads are bad"
• Users don't have problem you solve

Detection Pattern

Growth Strategy - Validations

Growth Strategy Without Metrics

Id

growth-no-metrics

Severity

error

Type

regex

Pattern

• growth|scale|expand|increase

Message

Growth mentioned without metrics. What are you growing? How much? By when? Define it.

Fix Action

Set specific metrics: MAU, revenue, signups, retention? What number? What timeframe? Make it measurable.

Applies To

• *.md
• *.txt
• README*

Unclear Growth Channels

Id

growth-unclear-channels

Severity

error

Type

regex

Pattern

• marketing|acquisition|attract|reach

Message

Growth without specific channels. 'Marketing' is not a channel. How exactly will you reach people?

Fix Action

Name channels: SEO? Paid ads? Referral? Content? Pick 1-2 to start. More channels = less focus.

Applies To

• *.md
• *.txt

Vanity Metrics Focus

Id

growth-vanity-metrics

Severity

warning

Type

regex

Pattern

• impressions|views|followers|likes|traffic|visitors

Message

Vanity metrics detected. Views and followers don't pay bills. Focus on revenue, retention, engagement.

Fix Action

Track value metrics: paying customers, revenue, retention rate, LTV, activation rate

Applies To

• *.md
• *.txt

No Growth Experiments Defined

Id

growth-no-experiments

Severity

error

Type

regex

Pattern

• test|try|experiment|hypothesis

Message

Growth experiments without structure. What's the hypothesis? Success criteria? Timeline to evaluate?

Fix Action

Structure experiments: hypothesis, metric, target, timeline. Run, measure, learn, iterate.

Applies To

• *.md
• *.txt

No Funnel Metrics

Id

growth-no-funnel

Severity

warning

Type

regex

Pattern

• conversion|signup|trial|purchase|onboard

Message

Conversion mentioned without funnel breakdown. Where are people dropping off? You need to know.

Fix Action

Map funnel: visit → signup → activate → convert → retain. Measure each step. Find bottleneck.

Applies To

• *.md
• *.txt

Paid Acquisition Only Strategy

Id

growth-paid-only

Severity

warning

Type

regex

Pattern

• ads|advertising|paid|ppc|sponsored

Message

Paid-only growth strategy. When money stops, growth stops. Build owned channels: SEO, content, referral.

Fix Action

Mix channels: 1 paid (fast), 1 organic (sustainable), 1 viral (scalable). Don't depend on one.

Applies To

• *.md
• *.txt

Acquisition Without Retention

Id

growth-no-retention

Severity

error

Type

regex

Pattern

• acquire|acquisition|get.?users|attract.?customers

Message

Acquisition focus without retention strategy. Leaky bucket problem. Fix retention before scaling acquisition.

Fix Action

Measure retention first: Week 1, Week 4, Month 3 retention. If below 40%, fix product before scaling.

Applies To

• *.md
• *.txt

No Unit Economics Defined

Id

growth-no-unit-economics

Severity

error

Type

regex

Pattern

• scale|grow|expand|acquire

Message

Scaling without unit economics. CAC vs LTV ratio must work before scaling. Otherwise you lose money faster.

Fix Action

Calculate: CAC (cost to acquire), LTV (lifetime value). LTV should be 3x CAC minimum. If not, fix first.

Applies To

• *.md
• *.txt

Trying Every Channel at Once

Id

growth-everything-at-once

Severity

warning

Type

regex

Pattern

• and|also|plus|as.?well.?as|in.?addition

Message

Too many channels at once. Spreading thin across 10 channels = mediocre results everywhere. Focus wins.

Fix Action

Pick ONE primary channel to master. Get it working. Then layer on a second. Serial, not parallel.

Applies To

• *.md
• *.txt

No Growth Loops Identified

Id

growth-no-loops

Severity

info

Type

regex

Pattern

• viral|network.?effect|referral|word.?of.?mouth

Message

Viral growth mentioned without loop structure. What action creates more users? Map the actual loop.

Fix Action

Design loop: user action → new exposure → new users → more actions. Make it measurable and improvable.

Applies To

• *.md
• *.txt

Premature Scaling Signs

Id

growth-premature-scaling

Severity

warning

Type

regex

Pattern

• scale.?up|ramp.?up|accelerate|aggressive|blitz

Message

Scaling language before product-market fit. Scaling a product people don't want = fast death.

Fix Action

Validate PMF first: 40%+ users very disappointed if product disappeared. Get that, then scale.

Applies To

• *.md
• *.txt

No Activation Metric

Id

growth-no-activation

Severity

warning

Type

regex

Pattern

• signup|register|new.?user|trial

Message

Signup without activation definition. Signup is not success. What action = real value experienced?

Fix Action

Define activation: first action that correlates with retention. Measure and optimize that, not just signups.

Applies To

• *.md
• *.txt