Growth Engine — Complete System

Phase 1: North Star & Growth Model

Define Your North Star Metric (NSM)

Your NSM is the ONE number that best captures the core value you deliver.

Selection criteria — must pass ALL four:

Reflects value delivered to customer (not just company revenue)
Is a leading indicator of revenue (predicts future revenue)
Every team can influence it (not siloed)
Measurable weekly or daily

NSM by business type:

Business Model	North Star Metric	Why
B2B SaaS	Weekly Active Users performing core action	Value = usage
Marketplace	Transactions completed per week	Both sides got value
E-commerce	Purchase frequency per customer per quarter	Repeat = love
Content/Media	Weekly engaged reading time	Attention = value
Fintech	$ processed per user per month	More $ = more trust
Developer tools	Builds/deploys per user per week	Integration depth
Social	Daily content interactions	Network value

NSM Quality Test (score 1-5 each, need 20+):

Can you measure it weekly? ___/5
Does improving it directly improve revenue? ___/5
Can marketing influence it? ___/5
Can product/engineering influence it? ___/5
Would a competitor with a higher number be winning? ___/5
Total: ___/25

Growth Model — Input-Output Map

growth_model:
  north_star: "[Your NSM]"
  inputs:
    - name: "New signups"
      current: 0
      target: 0
      lever: "acquisition"
    - name: "Activation rate"
      current: "0%"
      target: "0%"
      lever: "onboarding"
    - name: "Weekly retention"
      current: "0%"
      target: "0%"
      lever: "engagement"
    - name: "Referral rate"
      current: "0%"
      target: "0%"
      lever: "virality"
  formula: "NSM = new_signups × activation_rate × retention_rate × (1 + referral_rate)"
  current_nsm: 0
  target_nsm: 0
  bottleneck: "[Which input has the biggest gap?]"

Rule: Always fix the bottleneck input first. Growing acquisition when activation is broken = pouring water into a sieve.

Phase 2: AARRR+ Funnel Deep Dive

Stage 1 — Acquisition (How They Find You)

Channel scoring matrix:

channel_evaluation:
  - channel: "[e.g., Google Ads]"
    estimated_cac: "$___"
    volume_potential: "low|medium|high"
    time_to_test: "days|weeks|months"
    competitive_density: "low|medium|high"
    content_fit: "1-5"  # How natural is your message here?
    ice_score: 0  # Impact × Confidence × Ease (1-10 each)

Channel categories with tactics:

Paid (fast, expensive, measurable):

Google Ads → bottom-funnel, high intent, high CPC
Meta Ads → top-funnel, broad targeting, visual-first
LinkedIn Ads → B2B, expensive ($8-15 CPC), precise targeting
Influencer/creator → trust transfer, variable ROI
Podcast sponsorship → niche audiences, hard to track

Organic (slow, cheap, compounding):

SEO → 3-6 month payoff, compounds forever
Content marketing → thought leadership, lead magnets
Social media → brand, community, distribution
Community → forums, Discord, Slack groups
Product Hunt / marketplaces → launch spikes

Product-led (scalable, free):

Referral program → viral coefficient
Integrations/marketplace → partner distribution
Freemium → try-before-buy, land-and-expand
Open source → community-driven awareness
API/embeds → other products distribute you

Sales-led (high ACV, relationship):

Outbound (email/LinkedIn) → targeted, low volume
Partnerships/channel → leverage others' audiences
Events/conferences → high-touch, expensive
Account-based marketing → 1:many to 1:1

Test protocol:

Score all candidate channels using ICE
Pick top 3 (one from each category if possible)
Set budget cap: $500 or 2 weeks, whichever comes first
Define success metric BEFORE starting
Kill anything below threshold at budget cap
Double down on winner

Stage 2 — Activation (First Value Moment)

"Aha Moment" Discovery Framework:

Step 1 — Hypothesize:

What action do retained users take that churned users don't?
When does a user first say "this is worth it"?
What's the minimum experience to demonstrate core value?

Step 2 — Validate with data:

Compare: Users retained at Day 30 vs churned before Day 30
Find: What % of each group completed [action] in first [timeframe]?
If: Retained users do [action] at 3x+ rate → that's your aha moment

Step 3 — Define activation metric:

activation:
  aha_moment: "[Specific action, e.g., 'Created first project with 3+ tasks']"
  target_timeframe: "[e.g., Within first 48 hours]"
  current_rate: "___% of signups reach aha moment"
  target_rate: "___% (aim for 40-60% minimum)"
  steps_to_aha:
    - step: "Sign up"
      current_completion: "100%"
      drop_off: "0%"
    - step: "[Step 2]"
      current_completion: "____%"
      drop_off: "____%"
    - step: "[Aha moment]"
      current_completion: "____%"
      drop_off: "____%"

Activation optimization checklist:

Stage 3 — Retention (Do They Come Back?)

Cohort analysis template:

Week 0 | Week 1 | Week 2 | Week 3 | Week 4 | Week 8 | Week 12
100%   |  ___% |  ___% |  ___% |  ___% |  ___% |  ___%

Retention curve diagnosis:

Flattens above 20% → Product has core value. Focus on moving the flat line UP and reducing early drop-off
Flattens below 10% → Niche value. Either expand use cases or accept small market
Never flattens (→ 0%) → Product problem. Stop all growth spending. Fix product.
Early cliff (Week 1 drop > 60%) → Activation problem. Users never got value.
Gradual decline → Engagement problem. Need habit loops or re-engagement.

Retention improvement playbook:

Retention Problem	Tactic	Expected Impact
Day 1 drop > 50%	Fix onboarding, reduce time-to-value	High
Week 1 drop > 70%	Trigger emails, in-app nudges, help	High
Gradual decline	Build habit loops, notifications, content	Medium
Sudden cliff at Day X	Find what breaks — billing? Feature wall?	High
Seasonal churn	Re-engagement campaigns before drop	Medium

Habit loop design:

habit_loop:
  trigger: "[What reminds them to return? Email, notification, calendar, peer]"
  action: "[What do they do? Must be easy and quick]"
  variable_reward: "[What's different each time? New content, data, social]"
  investment: "[What do they put in that makes leaving harder? Data, connections, customization]"
  frequency: "[How often should the loop fire? Daily, weekly, event-driven]"

Stage 4 — Revenue (Are They Paying?)

Monetization readiness checklist:

Users consistently reach aha moment (activation > 40%)
Retention curve flattens (product-market fit signal)
Users request features / complain about limits (willingness to pay signal)
Competitor charges for similar value (market validation)
Unit economics work at current scale (or projected)

Pricing strategy quick-select:

Signal	Strategy	Example
High volume, low willingness to pay	Freemium + upsell	Slack, Dropbox
Low volume, high willingness to pay	Sales-led, annual contracts	Salesforce
Usage varies wildly	Usage-based	AWS, Twilio
Clear feature tiers	Good/Better/Best	Most SaaS
Network effects	Free for users, charge businesses	LinkedIn

Revenue metrics to track:

MRR / ARR (growth rate month-over-month)
ARPU (average revenue per user) — segment by plan
Conversion rate (free → paid) — benchmark: 2-5% freemium, 10-25% free trial
Expansion revenue % (upsells + cross-sells as % of new revenue)
Net Revenue Retention (NRR) — benchmark: >100% good, >120% great
LTV:CAC ratio — benchmark: >3:1
Payback period — benchmark: <12 months

Stage 5 — Referral (Are They Telling Others?)

Viral coefficient formula:

K = invitations_per_user × conversion_rate_per_invitation
K > 1 = exponential growth (very rare)
K = 0.3-0.7 = meaningful viral supplement
K < 0.1 = referral isn't a growth lever (yet)

Referral program design template:

referral_program:
  type: "double-sided|single-sided|milestone"
  giver_incentive: "[What the referrer gets]"
  receiver_incentive: "[What the new user gets]"
  trigger_moment: "[When to ask — after value delivery, not before]"
  mechanic: "link|code|invite|auto-detect"
  sharing_channels:
    - channel: "email"
      template: "[Pre-written share message]"
    - channel: "social"
      template: "[Share-optimized copy + visual]"
    - channel: "in-app"
      template: "[Invite flow within product]"
  fraud_prevention:
    - "Reward on activation, not signup"
    - "Limit rewards per user per month"
    - "Flag same-IP signups"
  tracking:
    - invites_sent_per_user
    - invite_conversion_rate
    - time_from_invite_to_activation
    - viral_coefficient_k

Referral timing rules:

✅ Ask AFTER user achieves success (completed project, got result, hit milestone)
✅ Ask when user gives positive feedback (NPS 9-10, support thank you)
❌ Never ask during onboarding (they haven't gotten value yet)
❌ Never ask immediately after payment (feels extractive)
✅ Ask when user invites a team member (they're already sharing)

Phase 3: Growth Loops

Loop 1 — Content Loop (SEO + Content)

Create valuable content → Google/social indexes it → New users discover it
→ Some users create content (UGC) or share → More content → More discovery

Content-led growth playbook:

Find 50 keywords your audience searches (tools: Ahrefs, Google autocomplete, "People also ask")
Cluster into 5-7 topic pillars
Create 1 pillar page per cluster (3,000+ words, comprehensive)
Create 5-10 supporting posts per pillar (long-tail keywords)
Internal link everything to pillar pages
Add lead magnets to top 20% of traffic pages
Repurpose top posts into social, email, video
Track: organic traffic → signups → activation → revenue per content piece

Content ROI tracking:

content_piece:
  title: ""
  url: ""
  publish_date: ""
  target_keyword: ""
  monthly_traffic: 0
  signups_attributed: 0
  revenue_attributed: "$0"
  cac_equivalent: "$0"  # What would this traffic cost via ads?
  status: "growing|plateau|declining"

Loop 2 — Viral Loop (Product-Led)

User gets value → Shares/invites → New users see value → They share → Compound

Viral mechanics ranked by strength:

Inherent virality — product requires others (Zoom, Slack, Figma multiplayer)
Collaborative virality — better with others (Notion shared workspaces)
Output virality — work product is visible (Canva "Made with Canva")
Incentivized virality — rewards for sharing (Dropbox extra storage)
Social proof virality — badges, profiles, leaderboards
Word of mouth — so good people talk about it (no mechanic needed)

Design your viral loop:

viral_loop:
  type: "[inherent|collaborative|output|incentivized|social_proof|wom]"
  trigger: "[What makes them share?]"
  payload: "[What does the recipient see?]"
  landing: "[Where do they land? Must show value immediately]"
  conversion: "[What's the first action for the new user?]"
  cycle_time: "[How long from share to new share?]"
  current_k: 0
  target_k: 0

Loop 3 — Paid Loop (Profitable Acquisition)

Revenue → Reinvest in ads → New users → Revenue → Reinvest more

Unit economics requirement:

LTV > 3× CAC (minimum for paid to be sustainable)
Payback period < 12 months (cash flow)
Marginal CAC < Average CAC (scaling efficiently)

Paid growth scaling checklist:

CAC stable or declining at current spend level
Creative fatigue monitored (refresh every 2-4 weeks)
Audience segmented (lookalikes, retargeting, cold)
Attribution tracked (UTM, pixel, conversion API)
Landing pages A/B tested per channel
Budget increases in 20% increments (not 2x jumps)
Daily spend caps set to prevent blowouts
Negative keywords / exclusions maintained weekly

Loop 4 — Sales Loop (High ACV)

Sales closes deal → Customer succeeds → Case study + referral → Pipeline → Sales closes

Sales-led growth framework:

sales_loop:
  ideal_customer:
    company_size: ""
    industry: ""
    budget_range: ""
    buying_trigger: ""
  outbound_velocity:
    emails_per_week: 0
    meetings_per_week: 0
    proposals_per_month: 0
    close_rate: "0%"
  case_study_production:
    cadence: "Every closed deal > $X"
    format: "Problem → Solution → Results (with numbers)"
    distribution: ["website", "sales deck", "social", "email"]
  referral_ask:
    timing: "90 days post-close, after first success milestone"
    script: "Who else in your network faces [problem we solved for you]?"

Phase 4: Experimentation Engine

Experiment Design Template

experiment:
  id: "EXP-001"
  name: ""
  hypothesis: "If we [change], then [metric] will [increase/decrease] by [amount] because [reason]"
  primary_metric: ""
  secondary_metrics: []
  funnel_stage: "acquisition|activation|retention|revenue|referral"
  ice_score:
    impact: 0  # 1-10: How much will this move the metric?
    confidence: 0  # 1-10: How confident based on evidence?
    ease: 0  # 1-10: How fast/cheap to implement?
    total: 0  # I × C × E
  sample_size_needed: 0
  duration: ""
  variant_a: "[Control — current experience]"
  variant_b: "[Treatment — the change]"
  success_threshold: "[e.g., >10% improvement at 95% confidence]"
  status: "planned|running|complete|killed"
  result: ""
  learning: ""

ICE Prioritization Board

Run experiments highest ICE score first. Review weekly.

| ID | Name | I | C | E | ICE | Stage | Status |
|----|------|---|---|---|-----|-------|--------|

Statistical Significance Rules

Minimum sample sizes (for 95% confidence, 80% power):

Baseline Rate	Minimum Detectable Effect	Sample per Variant
2%	50% relative (2% → 3%)	~4,700
5%	20% relative (5% → 6%)	~14,700
10%	10% relative (10% → 11%)	~14,400
30%	5% relative (30% → 31.5%)	~22,600

Rules:

Never peek at results before minimum duration (peeking inflates false positives)
Minimum 1 full business cycle (usually 1-2 weeks)
If you can't get enough traffic, test bigger changes (not subtle ones)
Sequential testing frameworks (e.g., Bayesian) allow earlier stopping if needed
Document EVERY experiment — even failures teach

Experiment Velocity Benchmarks

Company Stage	Experiments per Month	Notes
Pre-PMF (<50 users)	2-4	Big bets, qualitative validation
Early growth (50-1K)	4-8	Mix of big and small
Growth (1K-10K)	8-15	Data-driven, statistical rigor
Scale (10K+)	15-30+	Micro-optimizations compound

Phase 5: Growth Scoring & Health Dashboard

Weekly Growth Health Score (0-100)

Score each dimension 0-20:

1. Acquisition Health (0-20)

20: CAC declining, volume increasing, 3+ channels working
15: CAC stable, volume growing, 2 channels working
10: CAC stable, volume flat, 1 channel working
5: CAC rising or volume declining
0: No systematic acquisition

2. Activation Health (0-20)

20: >60% reach aha moment, improving trend
15: 40-60% activation, stable
10: 20-40% activation
5: <20% activation
0: Aha moment undefined or unmeasured

3. Retention Health (0-20)

20: Cohort curve flattens >40%, NRR >120%
15: Flattens >25%, NRR >100%
10: Flattens >15%, NRR 90-100%
5: Flattens <15%
0: Curve trends to zero

4. Revenue Health (0-20)

20: LTV:CAC >5:1, payback <6mo, expansion revenue >30% of new
15: LTV:CAC >3:1, payback <12mo
10: LTV:CAC 2-3:1
5: LTV:CAC 1-2:1
0: Unit economics negative

5. Experimentation Health (0-20)

20: >10 experiments/month, documented learnings, velocity increasing
15: 5-10 experiments/month, mostly documented
10: 2-4 experiments/month
5: <2 experiments/month or undocumented
0: No systematic experimentation

Total: ___/100

80-100: Growth machine — optimize and scale
60-79: Solid foundation — fix weakest dimension
40-59: Growth fundamentals incomplete — focus on basics
20-39: Pre-growth — product/market fit work needed
0-19: No growth system — start from Phase 1

Weekly Growth Dashboard YAML

growth_dashboard:
  week_of: "YYYY-MM-DD"
  north_star:
    metric: ""
    current: 0
    previous_week: 0
    wow_change: "0%"
    target: 0
    on_track: true|false
  acquisition:
    new_signups: 0
    by_channel:
      organic: 0
      paid: 0
      referral: 0
      direct: 0
    total_cac: "$0"
    cac_by_channel: {}
  activation:
    signup_to_aha_rate: "0%"
    median_time_to_aha: ""
    onboarding_completion: "0%"
  retention:
    week1_retention: "0%"
    week4_retention: "0%"
    week12_retention: "0%"
    dau_mau_ratio: 0
  revenue:
    mrr: "$0"
    mrr_growth: "0%"
    arpu: "$0"
    ltv: "$0"
    nrr: "0%"
    free_to_paid_conversion: "0%"
  referral:
    viral_coefficient_k: 0
    referral_invites_sent: 0
    referral_conversion: "0%"
  experiments:
    running: 0
    completed_this_week: 0
    wins_this_week: 0
    win_rate_last_30_days: "0%"
  health_score: 0
  top_priority: "[What to fix this week]"
  blockers: []

Phase 6: Growth Playbooks by Stage

Pre-PMF (0-50 Users)

Goal: Find product-market fit. Growth spending = waste.

Talk to 20+ potential users (interviews, not surveys)
Build MVP that solves ONE problem for ONE persona
Get 5 users who would be "very disappointed" without your product (Sean Ellis test)
Manually onboard every user — learn what confuses them
Don't optimize funnels. Don't run ads. Don't build referral programs.
Signal you're ready for growth: 40%+ "very disappointed" AND retention curve flattens

Early Growth (50-500 Users)

Goal: Find 1-2 scalable channels. Prove unit economics.

Double down on whatever got your first 50 users
Test 3 acquisition channels with small budgets ($500 each)
Build onboarding that gets 40%+ to aha moment without manual help
Start measuring AARRR weekly
Implement basic referral mechanic (even just "invite a friend" link)
Signal you're ready to scale: One channel produces users at <1/3 LTV CAC

Growth (500-5,000 Users)

Goal: Scale proven channels. Build growth loops.

Increase spend on winning channels (20% increments)
Build content engine (SEO pillar + supporting content)
Launch formal referral program with incentives
Run 5-10 experiments per month
Hire first growth-focused role (or allocate 50%+ of your time)
Build retention loops (email sequences, notifications, habit features)
Signal you're ready to scale: Multiple channels working, NRR >100%

Scale (5,000+ Users)

Goal: Efficiency at volume. Compound loops.

Diversify to 5+ acquisition channels
Build growth team (analyst, engineer, marketer minimum)
Automate experiment pipeline (feature flags, A/B framework)
Focus on micro-optimizations (1% improvements compound)
Build second-order growth loops (content → SEO → signups → content)
International expansion if applicable
Develop partnerships and channel/integration strategy

Phase 7: Advanced Growth Tactics

Pricing as a Growth Lever

Pricing changes are the highest-ROI growth tactic — they require zero traffic increase.

Quick tests:

Raise price 20% for new users → measure conversion rate change
Add annual plan with 2-month discount → measure plan mix shift
Add usage-based component → measure expansion revenue
Remove cheapest plan → measure conversion to next tier
Add enterprise tier with "Contact us" → measure inbound

1% price increase = 11% profit increase (on average, across industries)

Product-Led Growth (PLG) Framework

plg_checklist:
  self_serve_signup: true|false
  time_to_value: "[< 5 minutes ideal]"
  free_tier_or_trial: "freemium|free_trial|both|neither"
  in_product_upsell: true|false
  usage_limits_as_upgrade_triggers: true|false
  team_invite_built_in: true|false
  public_api_or_integrations: true|false
  community_or_forum: true|false
  product_qualified_leads_defined: true|false
  expansion_revenue_automated: true|false

Network Effects Playbook

Types of network effects:

Direct — more users = more value (social networks, messaging)
Indirect/Cross-side — more supply = more demand value (marketplaces)
Data — more usage = better product (ML, recommendations)
Platform — more developers = more apps = more users (iOS, Shopify)

Building network effects:

Start with the "hard side" of the market (supply for marketplaces, creators for platforms)
Seed with curated content/supply before opening up
Build switching costs through data, relationships, integrations
Create local network effects first (geographic, community, niche)

Expansion Revenue Playbook

Expansion > new revenue (cheaper, higher close rate, compounds).

Expansion signals to track:

Usage approaching plan limit (trigger upsell)
Team size growing (trigger seat expansion)
New use case adoption (trigger cross-sell)
Power user behavior (trigger premium feature pitch)
Account requesting features in higher tier (trigger upgrade conversation)

Expansion tactics:

Usage-based pricing with natural expansion (Twilio model)
Feature gating by plan tier with in-app upgrade prompts
Seat-based with team invite friction removal
Success milestones → celebration + "unlock more" offer
QBR (Quarterly Business Review) with ROI data + expansion recommendation

Phase 8: Common Growth Mistakes (Avoid These)

The 10 Growth Killers

Scaling before PMF — Pouring gasoline on a broken engine. Fix retention first.
Too many channels — 5 half-tested channels < 1 proven channel scaled hard.
Vanity metrics — Signups, pageviews, followers mean nothing without activation/revenue.
No measurement — "I think it's working" isn't growth. Instrument everything.
Premature optimization — A/B testing button colors when onboarding is 10% completion.
Ignoring retention — Acquisition is glamorous. Retention is profitable. Fix the bucket.
Copying competitors — Their strategy fits their context. Understand principles, not tactics.
No experiment discipline — Running tests without hypotheses, sample sizes, or documentation.
Discounting as growth — Discounts attract price-sensitive users who churn. Build value instead.
Feature-as-growth — "If we just build X, growth will come." Features don't acquire users.

Diagnostic: Why Growth Stalled

Symptom	Root Cause	Fix
Traffic up, signups flat	Landing page / messaging problem	A/B test headlines, social proof, CTA
Signups up, activation flat	Onboarding broken or aha moment unclear	Map and fix first-run experience
Activation up, retention flat	Product value is one-time, not recurring	Build habit loops, recurring value
Retention up, revenue flat	Monetization timing or pricing wrong	Test pricing, add expansion paths
Revenue up, growth slowing	Channel saturation	Diversify channels, build new loops
Everything flat	PMF lost or market shifted	Back to user interviews

Phase 9: Growth Team Design

Solo Founder Growth Stack

Do these yourself, in this order:

Weekly user interviews (30 min each, 3 per week)
One content piece per week (SEO-optimized)
Basic email sequences (welcome, activation, re-engagement)
Monthly experiment (one real A/B test)
Weekly dashboard review (30 min)

First Growth Hire

Hire when: You've found 1 working channel but can't scale it alone.

Profile: T-shaped — deep in one channel (paid, content, or product) + broad understanding of full funnel. Must be data-comfortable.

Don't hire: A "growth hacker" who promises 10x with tricks. Hire someone who can build systems.

Growth Team at Scale

Head of Growth
├── Growth Engineering (build experiments, instrumentation)
├── Growth Marketing (channels, content, campaigns)
├── Growth Analytics (measurement, dashboards, insights)
└── Growth Product (onboarding, activation, monetization)

Phase 10: Templates & Quick-Start Commands

Natural Language Commands

Use these to interact with this skill:

"Audit my growth" → Run full AARRR assessment, identify bottleneck, create action plan
"Score my growth health" → Calculate 0-100 health score across 5 dimensions
"Design a growth loop for [business type]" → Select and design optimal loop
"Plan an experiment for [metric]" → Create full experiment YAML with hypothesis, sample size, duration
"Diagnose why [metric] stalled" → Root cause analysis with fix recommendations
"Build my referral program" → Design double-sided referral with mechanics, timing, tracking
"Create my weekly dashboard" → Generate growth dashboard YAML customized for your business
"Evaluate [channel]" → Score acquisition channel with ICE, estimate ROI, create test plan
"Design my pricing for growth" → Select pricing model, tier structure, expansion mechanics
"What should I focus on?" → Based on current metrics, identify single highest-leverage action
"Build my content growth engine" → Keyword clusters, content calendar, distribution plan
"Calculate my unit economics" → LTV, CAC, payback, LTV:CAC with health assessment

Edge Cases & Advanced Situations

B2B vs B2C Growth Differences

Dimension	B2C	B2B
Decision maker	Individual	Committee (3-7 people)
Sales cycle	Minutes to days	Weeks to months
CAC	$1-50	$100-10,000+
Primary channels	Paid, viral, content	Content, outbound, events
Retention metric	DAU/MAU	Monthly active accounts
Expansion	Upsell features	Add seats, departments
Key growth lever	Virality + activation	Content + sales efficiency

Marketplace Growth (Two-Sided)

The chicken-and-egg problem:

Pick one side to subsidize (usually supply)
Start hyper-local or hyper-niche (Uber = SF, Airbnb = events)
Manually fill supply initially (founders do the work)
Build tools that make supply side's life better (even without demand)
Measure liquidity: % of searches that result in transaction

International Growth

Expansion decision framework:

Market size > $10M opportunity? (or strategic importance)
Product works without localization? Test with English first.
Legal/regulatory barriers? Research BEFORE building.
Local competitors? If dominant, need 10x differentiation.
Support coverage? Need timezone-appropriate support.

Localization priority:

Currency and pricing (mandatory)
Language (high impact)
Payment methods (region-specific)
Content/marketing (local references)
Support (native speakers)

Growth for Developer Tools

Documentation IS your growth engine
Free tier should be genuinely useful (not crippled)
API-first: let developers build on you
Community (Discord, GitHub, forums) > traditional marketing
Measure: API calls, integrations built, docs traffic
Content: tutorials, use cases, comparisons, migration guides

Zero-Budget Growth

When you can't spend money on acquisition:

SEO + content — write what your audience searches for
Community participation — be helpful in forums, Reddit, HN, Discord
Product virality — build sharing into the product experience
Partnerships — find complementary products, cross-promote
Cold outreach — personal emails to ideal customers (10/day, personalized)
Launch platforms — Product Hunt, HN Show, Indie Hackers, Reddit
Integration marketplaces — Shopify, Slack, Zapier app stores