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- OpenClaw
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Other
Growth Engineering Mastery
Design, execute, and measure growth systems — from North Star definition through viral loops, experimentation, and scaling. Complete AARRR+ framework with te...
skill openclawclawhub Free
Design, execute, and measure growth systems — from North Star definition through viral loops, experimentation, and scaling. Complete AARRR+ framework with te...
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Trust & source
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Documentation
Growth Engineering Mastery
Complete growth system: experimentation engine, viral mechanics, channel playbooks, funnel optimization, retention loops, and scaling frameworks. From zero users to exponential growth.
1. Growth Audit — Where Are You Now?
Before experimenting, diagnose. Run this 8-dimension health check:
Growth Health Scorecard
Rate each 1-5, multiply by weight: DimensionWeightScore (1-5)WeightedProduct-Market Fit3x____Activation Rate3x____Retention (Week 4)3x____Referral/Virality2x____Revenue per User2x____Channel Diversity1x____Experiment Velocity2x____Data Infrastructure1x____ Scoring: 68-85 = Growth-ready. 50-67 = Fix foundations first. <50 = Stop growth spending, fix product.
PMF Validation Gate
Do NOT invest in growth until these pass: pmf_gate: sean_ellis_test: "≥40% would be 'very disappointed' if product disappeared" retention_curve: "Flattens (does not trend to zero) by week 8" organic_growth: "≥10% of new users come from referral/word-of-mouth" nps: "≥30" qualitative: "Users describe product to friends without prompting" If PMF gate fails: Stop. Go back to product. Growth without PMF = pouring water into a leaky bucket.
Selection Framework
Your North Star Metric (NSM) must pass all 4 tests: Revenue proxy — More of this metric = more revenue (eventually) User value — Captures the moment users get value Measurable — Can track daily/weekly with existing tools Influenceable — Team actions can move it within 2-4 weeks
NSM Examples by Business Type
Business TypeNSMWhySaaS (B2B)Weekly Active TeamsTeams = sticky, revenue followsMarketplaceWeekly TransactionsBoth sides getting valueSubscription MediaWeekly Reading TimeEngagement predicts retentionE-commerceWeekly Repeat PurchasesRetention > acquisitionSocial/CommunityDaily Active Users postingCreators drive content loopDev ToolsWeekly API CallsUsage = integration depthFintechWeekly $ ManagedTrust + engagement
Supporting Metrics Tree
North Star Metric ├── Input Metric 1: [driver you can directly influence] ├── Input Metric 2: [driver you can directly influence] ├── Input Metric 3: [driver you can directly influence] └── Guard Metric: [thing that must NOT decrease] Example (SaaS): Weekly Active Teams (NSM) ├── New team activations/week (acquisition input) ├── Features used per team/week (engagement input) ├── Teams inviting 3+ members/week (virality input) └── Guard: Churn rate must stay <3%/month
ICE Scoring Framework
Every experiment gets scored before running: DimensionScore 1-10DefinitionImpact__If this works, how much does NSM move?Confidence__How sure are we it'll work? (data/analogies/gut)Ease__How fast/cheap to test? (days, not weeks) ICE Score = (Impact + Confidence + Ease) / 3 Run experiments scoring ≥7 first. Kill anything below 5.
Experiment Log Template
experiment: id: "GRW-042" name: "Add social proof counter to pricing page" hypothesis: "Showing '2,847 teams trust us' increases plan selection by 15%" north_star_impact: "More paid conversions → more Weekly Active Teams" ice_score: impact: 7 confidence: 6 ease: 9 total: 7.3 type: "A/B test" audience: "All pricing page visitors" sample_size_needed: 2400 # for 95% confidence, 80% power duration: "7-14 days" primary_metric: "Pricing page → checkout conversion rate" secondary_metrics: - "Average plan tier selected" - "Time on pricing page" guard_metrics: - "Support tickets about pricing must not increase >10%" status: "running" # proposed | running | won | lost | inconclusive result: lift: "+18.3%" confidence: "97.2%" decision: "Ship to 100%" learnings: "Social proof most effective on annual plans. Monthly plan conversion unchanged." next_experiment: "Test specific customer logos vs generic count"
Experiment Velocity Targets
StageExperiments/WeekFocusPre-PMF5-10Product experiments (features, UX, messaging)Early Growth3-5Activation + retention experimentsScaling5-10Channel + conversion experimentsMature10-20Micro-optimizations + new channels
Statistical Rigor Rules
Minimum sample size: Calculate BEFORE launching (use: n = 16 × σ² / δ² or online calculator) Minimum runtime: 2 full business cycles (usually 2 weeks) No peeking: Don't stop tests early on positive results (peeking inflates false positives 3-5x) One change per test: Isolate variables. Multivariate only with massive traffic Document losses: Failed experiments are data. Log why the hypothesis was wrong
4.1 Acquisition — Getting Users In
Channel Evaluation Matrix Score each channel before investing: channel_evaluation: name: "[Channel]" scores: estimated_volume: 8 # 1-10: How many users can this deliver? targeting_precision: 7 # 1-10: Can we reach our ICP specifically? cost_per_acquisition: 6 # 1-10: How cheap? (10 = free/organic) time_to_results: 4 # 1-10: How fast? (10 = same day) scalability: 7 # 1-10: Can we 10x spend and 10x output? defensibility: 8 # 1-10: Hard for competitors to copy? total: 40 # out of 60 verdict: "Test with $500 budget over 2 weeks" Channel Playbooks (Top 12) Organic Channels (low cost, slow build): SEO/Content Target: Bottom-of-funnel keywords first (high intent, lower volume) Playbook: 1 pillar page + 8-12 cluster articles per topic Timeline: 3-6 months to meaningful traffic Experiment: Test 3 content formats (how-to, comparison, listicle) — measure organic signups per article Killer metric: Organic signups/article/month Community/Forum Marketing Target: Where your ICP already hangs out (Reddit, HN, Discord servers, Slack groups) Playbook: Provide genuine value for 30 days before any self-promotion. 20:1 value:ask ratio Experiment: Track which communities drive highest-quality signups (activation rate, not just volume) Warning: Getting banned kills the channel permanently. Authenticity is non-negotiable Referral/Word-of-Mouth Target: Existing happy users Playbook: See Section 5 (Viral Mechanics) below Killer metric: K-factor (viral coefficient) Social Media (Organic) Target: Platform where your ICP consumes content Platform selection: LinkedIn (B2B), Twitter/X (tech/startup), TikTok (consumer/SMB), Instagram (visual/lifestyle) Playbook: Post 5x/week, 80% value + 20% product. Reply to every comment for 90 days Experiment: Test content types (text, carousel, video, thread) — measure profile visits → signups Partnerships/Integrations Target: Products your users already use Playbook: Build integration → get listed in partner's marketplace → co-market Experiment: Partner A vs Partner B — which integration drives more activated users? Product-Led SEO Target: Create public-facing pages that rank (templates, tools, directories) Examples: Canva templates page, Zapier app directory, Ahrefs free tools Experiment: Build 1 free tool targeting a high-volume keyword — measure signups from tool Paid Channels (fast results, requires budget): Search Ads (Google/Bing) Target: High-intent keywords (bottom of funnel) Playbook: Start with exact match branded + competitor terms. Expand to problem-aware keywords Budget rule: Don't spend >$50/day until CAC is profitable Experiment: Ad copy A vs B, then landing page A vs B (sequential, not simultaneous) Social Ads (Meta/LinkedIn/TikTok) Target: Lookalike audiences from best customers Playbook: 3 creatives × 3 audiences × 3 copy variants. Kill losers at $50 spend, scale winners LinkedIn: Only for B2B with ACV >$5K (expensive CPMs) Experiment: Audience segmentation — which cohort has lowest CAC AND highest LTV? Influencer/Creator Target: Micro-influencers (10K-100K followers) in your niche Playbook: Product-for-post for micro. Paid for 50K+. Always track with UTM + unique codes Experiment: 5 micro-influencers at $500 each. Compare CAC to paid ads Cold Outreach (Email/LinkedIn) Target: Named accounts (ABM) Playbook: 5-touch sequence over 14 days. Personalized first line. Clear CTA Volume: 50-100/day per domain (warm up first). Separate domain from main Experiment: Subject line tests (5 variants, 200 sends each) Leverage Channels (unconventional): PR/Media Target: Industry publications, podcasts, newsletters Playbook: Newsjack trending topics. Offer original data/research. Be a source, not an ad Experiment: 10 podcast appearances — measure signups per appearance Platform Piggyback Target: Launch on Product Hunt, HN Show, AppSumo, marketplaces Playbook: Coordinate launch day (Tuesday-Thursday). Mobilize existing users to upvote. Respond to every comment Timeline: 1 day of effort, potentially thousands of signups Experiment: Which platform delivers highest-LTV users? Channel Prioritization Rule The "Bull's Eye" Framework: Brainstorm all 12+ channels Rank by ICE score Test top 3 with minimum viable spend ($500-1K each, 2 weeks) Double down on the ONE winner Don't diversify until that channel is saturated (CAC rising >30% month-over-month)
4.2 Activation — The "Aha Moment"
Define Your Aha Moment aha_moment: description: "The specific action where users first experience core value" examples: slack: "Sent 2,000 team messages" dropbox: "Put 1 file in Dropbox folder" facebook: "Added 7 friends in 10 days" hubspot: "Imported contacts and sent first email" your_product: action: "[specific action]" threshold: "[quantity/frequency]" timeframe: "[within X days of signup]" validation: "Users who reach aha moment retain at 2x+ rate of those who don't" Activation Funnel Map Signup → [Step 1] → [Step 2] → ... → Aha Moment → Retained User | | | | v v v v Drop-off Drop-off Drop-off Success rate % rate % rate % rate % Map EVERY step. Measure EVERY drop-off. Fix the BIGGEST leak first. Activation Tactics (by drop-off point) Signup → First Session: Reduce signup friction (social login, no credit card, fewer fields) Welcome email within 5 minutes with ONE clear next step In-app checklist showing progress to aha moment Experiment: Remove 1 signup field → measure completion rate First Session → Key Action: Interactive onboarding tour (max 4 steps) Pre-populate with sample data so product feels alive Contextual tooltips on first encounter (not all at once) Experiment: Guided tour vs self-serve vs video walkthrough Key Action → Aha Moment: Trigger celebration/reward when they complete key action Show value immediately (dashboard, report, insight) Prompt sharing/inviting while enthusiasm is high Experiment: Time-to-value — can you deliver aha moment in <5 minutes? Activation Scorecard activation_metrics: signup_to_first_session: "Target: >80% within 24h" first_session_to_key_action: "Target: >60% within session 1" key_action_to_aha: "Target: >40% within 7 days" overall_activation_rate: "Target: >30% (signup → aha within 14 days)" benchmark_comparison: "[industry average is X%, we're at Y%]"
4.3 Retention — The Only Metric That Matters
Cohort Analysis Template Track weekly cohorts (by signup week): Week 0 Week 1 Week 2 Week 3 Week 4 Week 8 Week 12 Cohort A 100% 45% 32% 28% 25% 22% 20% Cohort B 100% 52% 38% 33% 30% 27% 25% Cohort C 100% 48% 35% 30% 27% 24% 22% What to look for: Does the curve flatten? (Good — you have a retention floor) Is each cohort better than the last? (Good — product is improving) Where's the biggest week-over-week drop? (Fix that transition) Retention Curve Benchmarks Product TypeGood Week-4Great Week-4Week-12 FloorSaaS (B2B)30%50%+20%+Consumer App15%25%+10%+Marketplace20%35%+15%+Gaming10%20%+5%+ Retention Improvement Playbook Week 1 drop-off (activation problem): Improve onboarding (see 4.2) Add "quick win" in first session Re-engagement email at 24h, 72h, 7 days Week 2-4 drop-off (habit problem): Build triggers: notifications, emails, in-app prompts at optimal times Create recurring use case (weekly report, daily digest, scheduled task) Social hooks: team features, sharing, collaboration Week 4+ decline (value problem): Feature depth: are power users hitting ceiling? New use cases: expand the "jobs to be done" Community: forums, events, user groups create switching cost Engagement Loops Design self-reinforcing loops: User takes action → Gets value → Triggers notification/reminder → User returns → Takes deeper action Types of engagement loops: Content loop: User creates content → others consume → creator gets feedback → creates more Social loop: User invites friend → friend joins → both get value → invite more Data loop: User adds data → product gets smarter → better recommendations → user adds more Habit loop: Trigger (email/notification) → Action (check dashboard) → Reward (insight) → Investment (customize)
4.4 Revenue — Monetization That Doesn't Kill Growth
Pricing-Growth Alignment Pricing ModelGrowth ImpactBest ForFreemiumHigh viral potential, low conversion (2-5%)Network effects, large TAMFree trialHigher conversion (10-25%), time pressureClear aha moment within trialUsage-basedNatural expansion, low barrierAPI/infrastructure, measurable valueFlat rateSimple, predictable, easy to sellSimple product, single personaPer-seatExpansion revenue, team adoption incentiveCollaboration tools Revenue Experiments Pricing page layout: Test 2-tier vs 3-tier vs slider Anchor pricing: Test showing enterprise tier first vs starter first Trial length: 7-day vs 14-day vs 30-day (shorter often converts better) Feature gating: Which free feature, if paywalled, would drive most upgrades? Annual discount: Test 10%, 17%, 20%, 25% annual discount — optimize for LTV not just conversion Unit Economics Health Check unit_economics: cac: "$[X]" # Total sales+marketing / new customers ltv: "$[X]" # Average revenue × average lifetime ltv_cac_ratio: "[X]:1" # Target: >3:1. Below 1 = losing money payback_months: "[X]" # Target: <12 months (SaaS), <3 months (consumer) gross_margin: "[X]%" # Target: >70% (SaaS), >40% (marketplace) expansion_revenue: "[X]%" # % of revenue from existing customers expanding ndr: "[X]%" # Net Dollar Retention. Target: >100% (ideally >120%)
4.5 Referral — Turning Users Into a Growth Channel
See Section 5 (Viral Mechanics) for complete referral system design.
Viral Coefficient (K-Factor)
K = invites_sent_per_user × conversion_rate_of_invites K > 1 = exponential growth (every user brings >1 new user) K = 0.5 = good amplifier (50% more users from virality) K < 0.3 = not meaningfully viral
Viral Cycle Time
K-factor alone isn't enough. Speed matters: Viral Cycle Time = time from user signup → their invite → invitee signup Shorter cycle = faster growth (even with K < 1) Goal: Reduce viral cycle time to <48 hours.
Types of Virality (Design for ALL of them)
1. Inherent Virality (product requires sharing) Example: Zoom (you invite people to join meetings), Figma (collaborate on designs) Design: Core use case involves other people Strongest form. Build this into the product if possible 2. Collaboration Virality (better with more people) Example: Slack (more teammates = more valuable), Notion (shared workspace) Design: Features that work better with team/network Trigger: Prompt team invites during high-value moments 3. Word-of-Mouth Virality (users talk about it) Example: ChatGPT (people share outputs), Canva (people share designs) Design: Create shareable outputs with subtle branding Trigger: Make outputs beautiful/impressive enough that users WANT to show them off 4. Incentivized Virality (rewards for sharing) Example: Dropbox (250MB per referral), Uber ($10 credit per referral) Design: Two-sided reward (referrer AND referee both get something) Warning: Attracts low-quality users if reward is too generous. Gate the reward behind activation 5. Artificial Scarcity/FOMO Example: Clubhouse (invite-only), Gmail (invite-only launch) Design: Limited access creates desire. Waitlists with position number Timing: Only effective at launch or for new features. Wears off fast
Referral Program Design Template
referral_program: name: "[Program name]" mechanics: referrer_reward: "[What they get]" referee_reward: "[What invitee gets]" reward_trigger: "Referee must [complete activation action] before rewards unlock" reward_type: "product_credit" # cash | product_credit | feature_unlock | status cap: "10 referrals/month" # Prevent gaming distribution: share_methods: - "Unique referral link (primary)" - "Email invite from product" - "Social share buttons (Twitter, LinkedIn)" - "QR code for in-person" placement: - "Post-aha-moment celebration screen" - "Settings/account page" - "Monthly usage summary email" - "In-app prompt after positive action (e.g., saved money, closed deal)" tracking: metrics: - "Share rate: % of users who share referral link" - "Click-through rate: % of link viewers who click" - "Conversion rate: % of clickers who sign up" - "Activation rate: % of referred signups who activate" - "K-factor: shares × CTR × signup × activation" cohort_quality: "Compare referred users vs non-referred on Day 30 retention + LTV" optimization_experiments: - "Test reward amount ($5 vs $10 vs $20)" - "Test reward type (credit vs cash vs feature)" - "Test referral prompt timing (post-signup vs post-aha vs post-payment)" - "Test share copy (3 variants)"
Viral Content Strategies
For products where output sharing drives growth: Branded outputs: Add subtle watermark/badge ("Made with [Product]") to exports, reports, shares Public profiles/pages: User-created content that's publicly accessible (SEO + social sharing) Embed widgets: Let users embed product functionality on their sites Template marketplace: User-created templates others can discover and use Leaderboards/badges: Shareable achievements that demonstrate status
Why Loops > Funnels
Funnels are linear (top → bottom, then done). Loops are circular — output becomes input.
Loop Architecture
[New User] → [Takes Action] → [Creates Value] → [Attracts New User] → repeat
6 Growth Loop Templates
1. User-Generated Content Loop User creates content → Content gets indexed/shared → New user discovers content → Signs up to create own → Creates content Examples: Medium, GitHub, Canva templates Key metric: Content pieces created/week Leverage point: Make content creation effortless + discoverable 2. Paid Marketing Loop Revenue → Reinvest in ads → Acquire users → Users generate revenue → Reinvest more Key metric: LTV:CAC ratio (must be >3:1) Leverage point: Increase LTV (expansion revenue, retention) → can afford higher CAC 3. Sales Loop Close deal → Case study/testimonial → Use in sales materials → Close next deal faster Key metric: Win rate improvement per quarter Leverage point: Systematize case study collection (ask at Month 3 of every account) 4. Data Network Effect Loop Users use product → Product collects data → Product improves (AI/ML/recommendations) → More valuable for all users → More users join Examples: Waze, Netflix recommendations, Google Search Key metric: Improvement in core metric per doubling of data Leverage point: Show users how product gets better with more usage 5. Marketplace/Platform Loop Supply joins → Attracts demand → Demand attracts more supply → More selection attracts more demand Key metric: Liquidity (% of listings that transact) Leverage point: Solve chicken-and-egg: seed supply first, constrain geography to build density 6. Community Loop Expert users help newbies → Newbies become power users → Power users help next wave → Community grows Examples: Stack Overflow, Reddit, Discord servers Key metric: Weekly active contributors Leverage point: Gamification (reputation, badges, privileges for top contributors)
Conversion Rate Benchmarks
Funnel StepMedianGoodExcellentLanding page → Signup2-3%5-8%10%+Signup → Activation20-30%40-50%60%+Free → Paid2-3%5-7%10%+Trial → Paid10-15%20-30%40%+Annual → Renewal70-80%85-90%92%+
Landing Page Optimization Checklist
Hero headline matches ad/source copy (message match) Clear value proposition in ≤10 words Social proof above the fold (logos, numbers, testimonials) ONE primary CTA (not 3 competing buttons) CTA button text is action-specific ("Start free trial" not "Submit") Mobile-first design (60%+ of traffic is mobile) Page loads in <3 seconds (every second = 7% conversion drop) Remove navigation (landing page ≠ homepage) Include objection handling (FAQ, guarantee, security badges) Exit-intent popup with alternate offer
High-Impact CRO Experiments (ordered by typical lift)
Headline copy (10-30% lift potential) — Test problem-focused vs benefit-focused vs social-proof CTA button (5-20% lift) — Test color, copy, size, position Social proof type (5-15% lift) — Test logos vs testimonials vs numbers vs case studies Form length (10-25% lift) — Test fewer fields, progressive profiling Page layout (5-15% lift) — Test long-form vs short-form, video vs text Pricing display (10-30% lift) — Test anchoring, default selection, feature comparison Trust signals (3-10% lift) — Test guarantees, security badges, review scores
Lifecycle Email Sequences
Welcome Sequence (Days 0-14) welcome_sequence: - day: 0 trigger: "Signup" subject: "Welcome — here's your quick win" content: "One specific action to get value in <5 minutes" cta: "Do [aha action] now" - day: 1 trigger: "Has NOT completed aha action" subject: "[First name], you're 1 step away" content: "Show what they'll get once they complete the action" cta: "Complete setup" - day: 3 trigger: "Still not activated" subject: "How [similar company] uses [Product]" content: "Case study / use case matching their profile" cta: "Try this approach" - day: 7 trigger: "Not activated" subject: "Need help? Reply to this email" content: "Personal note from founder. Offer 1:1 call" cta: "Reply or book call" - day: 14 trigger: "Still not activated" subject: "Last chance: your [Product] account" content: "We'll archive your account in 7 days. Here's what you're missing" cta: "Reactivate" Re-engagement Sequence (for churned/dormant users) reengagement: - trigger: "14 days inactive" subject: "We miss you — here's what's new" content: "Top 3 new features/improvements since they left" - trigger: "30 days inactive" subject: "[First name], [specific value they got] is waiting" content: "Reference their actual usage data. Show what they've built" - trigger: "60 days inactive" subject: "Should we close your account?" content: "FOMO trigger. Offer win-back discount (20-30% off)" - trigger: "90 days inactive" subject: "Feedback request (we'll shut up after this)" content: "Why did you leave? 3-question survey. Offer incentive"
Push Notification Strategy
Rules: Max 3-5/week (more = uninstall) Only send when you can show value (not "We miss you!") Personalize: "Your report is ready" > "Check out new features" A/B test timing: morning vs evening, weekday vs weekend Let users choose notification categories
Churn Prediction Signals
Build an early warning system. Track these leading indicators: SignalTimeframeRisk LevelLogin frequency drops 50%+Week over week🟡 MediumKey feature usage stops7 days🟡 MediumSupport ticket unresolved >48hRolling🟡 MediumNo logins for 14+ daysRolling🔴 HighBilling failure (payment method expired)Event🔴 HighExport/download of all dataEvent🔴 CriticalAdmin user leaves companyEvent🔴 Critical Response playbook: Trigger automated outreach at 🟡, human outreach at 🔴.
When to Scale a Channel
scale_criteria: channel: "[name]" ready_when: - "CAC is <1/3 of LTV" - "Conversion rates are stable for 4+ weeks" - "Process is documented and repeatable" - "Can increase spend 50% without CAC rising >20%" warning_signs: - "CAC rising >20% month-over-month" - "Conversion rates declining" - "Quality of leads/users dropping (lower activation rate)" - "Creative fatigue (CTR declining)"
Scaling Playbook
Automate first — Before hiring, automate everything possible (email sequences, ad management, content scheduling) Document SOPs — Every process needs a playbook before delegation Hire specialists, not generalists — At scale, you need a paid ads person, not a "growth person" Build dashboards before scaling — If you can't measure it in real-time, you can't scale it safely 10% rule — Increase budget/volume by max 10-20%/week. Sudden jumps break things
International Expansion Checklist
Localize landing pages (not just translate — adapt) Research local competitors and positioning Adjust pricing for purchasing power (PPP) Local payment methods (not just Stripe) Support in local timezone and language Comply with local regulations (GDPR, data residency) Test demand before committing (run ads in target language first)
Solo/Small Team (1-3 people)
Growth Lead (you) ├── Runs experiments (2-3/week) ├── Manages 1-2 channels ├── Analyzes data weekly └── Writes copy/creates content Focus: Find ONE channel that works. Don't spread thin.
Growth Team (4-10 people)
Head of Growth ├── Acquisition Lead → paid, SEO, partnerships ├── Product/Growth Engineer → experiments, features, A/B tests ├── Lifecycle/CRM → emails, notifications, retention └── Data Analyst → metrics, cohorts, experiment analysis
Growth Meeting Cadence
MeetingFrequencyDurationPurposeExperiment standup2x/week15 minStatus of running experimentsMetrics reviewWeekly30 minNSM, funnel metrics, cohort reviewExperiment planningWeekly45 minPrioritize next week's experiments (ICE scoring)Growth strategyMonthly90 minChannel performance, resource allocation, quarterly goals
Analytics Stack (Minimum Viable)
analytics_stack: product_analytics: "Mixpanel or Amplitude or PostHog (free tier)" web_analytics: "Google Analytics 4 + Google Tag Manager" attribution: "UTM parameters (mandatory on ALL links)" ab_testing: "PostHog or GrowthBook (free) or Optimizely (paid)" email: "Customer.io or Resend or SendGrid" crm: "HubSpot (free) or Pipedrive" session_recording: "Hotjar or FullStory (free tier)" surveys: "Typeform or native in-app"
UTM Convention
utm_source: [platform] — google, linkedin, twitter, email, partner-name utm_medium: [type] — cpc, social, email, referral, organic utm_campaign: [campaign-name] — q1-launch, black-friday, webinar-series utm_content: [variant] — hero-cta, sidebar-banner, email-v2 utm_term: [keyword] — only for paid search Rule: Every external link gets UTMs. No exceptions. Untracked traffic = wasted budget.
Event Tracking Plan
Track these events minimum: required_events: acquisition: - "page_view (with UTM params)" - "signup_started" - "signup_completed" activation: - "onboarding_step_completed (step_number)" - "first_key_action" - "aha_moment_reached" engagement: - "feature_used (feature_name)" - "session_started" - "session_duration" revenue: - "plan_selected (plan_name, price)" - "payment_completed (amount, plan)" - "upgrade (from_plan, to_plan)" - "churn (reason)" referral: - "referral_link_shared (method)" - "referral_link_clicked" - "referred_signup" - "referred_activated"
The 10 Growth Killers
Scaling before PMF — Spending on acquisition when retention is broken = burning money Vanity metrics addiction — Signups, downloads, pageviews mean nothing without activation + retention Copying without context — "Dropbox did referrals" doesn't mean you should. Understand WHY it worked for THEM Too many channels too soon — Master ONE before adding another. Spread thin = learn nothing Peeking at A/B tests — Stopping tests early inflates false positives 3-5x. Run to completion Optimizing pennies — CRO on a page getting 100 visits/month is pointless. Get traffic first Ignoring retention — Acquiring users you can't keep is literally the most expensive thing you can do Over-automating before understanding — Automate processes you've done manually 50+ times. Not before Growth hacks without strategy — One-off tactics without a system = random acts of marketing Not documenting experiments — If you don't log it, you'll repeat failures and forget successes
When Growth Stalls
Diagnostic checklist: Has the channel saturated? (CAC up >30% in 3 months) Has the product changed? (New features breaking existing flows) Has the market shifted? (New competitor, regulation, trend change) Has the team burned out? (Experiment velocity dropped) Is it seasonal? (Compare to same period last year) Are you measuring the right thing? (NSM still reflects actual value?)
B2B vs B2C Growth Differences
DimensionB2BB2CSales cycleWeeks-monthsMinutes-daysDecision makers3-7 people1 personChannelsLinkedIn, content, events, outboundSocial, SEO, paid, viralPricingValue-based, negotiatedFixed, transparentRetention driverSwitching cost, integration depthHabit, engagementReferral mechanicsCase studies, introductionsIn-product, social sharing
Two-Sided Marketplace Growth
Chicken-and-egg solution order: Seed supply manually (scrape, import, do it yourself) Constrain geography (one city/niche first) Offer supply-side tools for free (even without demand) Build just enough demand to show supply it works Let organic flywheel take over before expanding geography
PLG (Product-Led Growth) Specifics
plg_metrics: free_to_paid: "Target: 3-5% (freemium) or 15-25% (free trial)" time_to_value: "Target: <5 minutes" expansion_rate: "Target: >120% NDR" self_serve_ratio: "Target: >80% of revenue from self-serve" pql_rate: "Target: 20-40% of active free users qualify" Product Qualified Lead (PQL) definition: User who has reached activation AND shows buying signals (hits usage limit, views pricing page, invites team members).
Growth with Zero Budget
Build in public (Twitter/LinkedIn) — share metrics, learnings, behind-the-scenes Launch on 5 platforms: Product Hunt, HN, Reddit, Indie Hackers, relevant Discords Write 1 SEO article/week targeting long-tail keywords Offer free tool that solves a related problem → funnel to main product Cold DM 10 potential users/day — ask for feedback, not sales Partner with complementary products for cross-promotion Answer questions on Quora/Reddit/forums where your ICP hangs out
14. Weekly Growth Review Template
weekly_review: period: "Week of [DATE]" north_star_metric: current: "[X]" target: "[X]" trend: "up|down|flat" wow_change: "+X%" funnel_metrics: acquisition: "[visitors/signups]" activation: "[activated/total signups] = X%" retention: "[week 1 retention] = X%" revenue: "[$MRR] | [new paying] | [churned]" referral: "[K-factor] | [referral signups]" experiments: completed: - name: "[experiment]" result: "won|lost|inconclusive" impact: "[metric change]" next_step: "[ship|iterate|kill]" running: - name: "[experiment]" progress: "[X/Y days complete]" early_signal: "[trending positive|neutral|negative]" launching_next_week: - name: "[experiment]" ice_score: "[X]" hypothesis: "[statement]" channels: - name: "[channel]" spend: "$[X]" cac: "$[X]" volume: "[X] new users" quality: "[activation rate of users from this channel]" top_learning: "[Single most important thing learned this week]" biggest_risk: "[What could derail growth next month?]" focus_next_week: "[1-2 priorities]"
15. Natural Language Commands
Use these to activate specific workflows: CommandAction"Run growth audit"Execute 8-dimension health scorecard"Define north star"Walk through NSM selection framework"Score this experiment"ICE scoring + experiment template"Analyze my funnel"Map funnel stages with conversion rates"Design referral program"Complete referral program template"Evaluate this channel"Channel scoring matrix"Build growth loop"Design self-reinforcing growth loop"Optimize this page"Landing page CRO checklist"Plan retention emails"Generate lifecycle email sequences"Weekly growth review"Fill in weekly review template"Diagnose growth stall"Run diagnostic checklist"Scale this channel"Scaling readiness assessment
Category context
Long-tail utilities that do not fit the current primary taxonomy cleanly.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md