Skill: 1kalin/afrexai-growth-engine (version 1.1.0)

# 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: | Dimension | Weight | Score (1-5) | Weighted | |-----------|--------|-------------|----------| | Product-Market Fit | 3x | __ | __ | | Activation Rate | 3x | __ | __ | | Retention (Week 4) | 3x | __ | __ | | Referral/Virality | 2x | __ | __ | | Revenue per User | 2x | __ | __ | | Channel Diversity | 1x | __ | __ | | Experiment Velocity | 2x | __ | __ | | Data Infrastructure | 1x | __ | __ | **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: ```yaml 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. --- ## 2. North Star Metric — Pick ONE Number ### Selection Framework Your North Star Metric (NSM) must pass all 4 tests: 1. **Revenue proxy** — More of this metric = more revenue (eventually) 2. **User value** — Captures the moment users get value 3. **Measurable** — Can track daily/weekly with existing tools 4. **Influenceable** — Team actions can move it within 2-4 weeks ### NSM Examples by Business Type | Business Type | NSM | Why | |---------------|-----|-----| | SaaS (B2B) | Weekly Active Teams | Teams = sticky, revenue follows | | Marketplace | Weekly Transactions | Both sides getting value | | Subscription Media | Weekly Reading Time | Engagement predicts retention | | E-commerce | Weekly Repeat Purchases | Retention > acquisition | | Social/Community | Daily Active Users posting | Creators drive content loop | | Dev Tools | Weekly API Calls | Usage = integration depth | | Fintech | Weekly $ Managed | Trust + 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 ``` --- ## 3. Experimentation Engine — The Core Growth Loop ### ICE Scoring Framework Every experiment gets scored before running: | Dimension | Score 1-10 | Definition | |-----------|-----------|------------| | **Impact** | __ | 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 ```yaml 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 | Stage | Experiments/Week | Focus | |-------|-----------------|-------| | Pre-PMF | 5-10 | Product experiments (features, UX, messaging) | | Early Growth | 3-5 | Activation + retention experiments | | Scaling | 5-10 | Channel + conversion experiments | | Mature | 10-20 | Micro-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. AARRR Funnel — Stage-by-Stage Playbooks ### 4.1 Acquisition — Getting Users In #### Channel Evaluation Matrix Score each channel before investing: ```yaml 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):** 1. **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 2. **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 3. **Referral/Word-of-Mouth** - Target: Existing happy users - Playbook: See Section 5 (Viral Mechanics) below - Killer metric: K-factor (viral coefficient) 4. **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 5. **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? 6. **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):** 7. **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) 8. **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? 9. **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 10. **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):** 11. **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 12. **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:** 1. Brainstorm all 12+ channels 2. Rank by ICE score 3. Test top 3 with minimum viable spend ($500-1K each, 2 weeks) 4. Double down on the ONE winner 5. Don't diversify until that channel is saturated (CAC rising >30% month-over-month) ### 4.2 Activation — The "Aha Moment" #### Define Your Aha Moment ```yaml 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 ```yaml 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 Type | Good Week-4 | Great Week-4 | Week-12 Floor | |-------------|-------------|--------------|---------------| | SaaS (B2B) | 30% | 50%+ | 20%+ | | Consumer App | 15% | 25%+ | 10%+ | | Marketplace | 20% | 35%+ | 15%+ | | Gaming | 10% | 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:** 1. **Content loop:** User creates content → others consume → creator gets feedback → creates more 2. **Social loop:** User invites friend → friend joins → both get value → invite more 3. **Data loop:** User adds data → product gets smarter → better recommendations → user adds more 4. **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 Model | Growth Impact | Best For | |---------------|--------------|----------| | Freemium | High viral potential, low conversion (2-5%) | Network effects, large TAM | | Free trial | Higher conversion (10-25%), time pressure | Clear aha moment within trial | | Usage-based | Natural expansion, low barrier | API/infrastructure, measurable value | | Flat rate | Simple, predictable, easy to sell | Simple product, single persona | | Per-seat | Expansion revenue, team adoption incentive | Collaboration 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 ```yaml 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. --- ## 5. Viral Mechanics — Engineering Word-of-Mouth ### 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 ```yaml 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: 1. **Branded outputs:** Add subtle watermark/badge ("Made with [Product]") to exports, reports, shares 2. **Public profiles/pages:** User-created content that's publicly accessible (SEO + social sharing) 3. **Embed widgets:** Let users embed product functionality on their sites 4. **Template marketplace:** User-created templates others can discover and use 5. **Leaderboards/badges:** Shareable achievements that demonstrate status --- ## 6. Growth Loops — Self-Reinforcing Systems ### 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) --- ## 7. Funnel Optimization — CRO Playbook ### Conversion Rate Benchmarks | Funnel Step | Median | Good | Excellent | |-------------|--------|------|-----------| | Landing page → Signup | 2-3% | 5-8% | 10%+ | | Signup → Activation | 20-30% | 40-50% | 60%+ | | Free → Paid | 2-3% | 5-7% | 10%+ | | Trial → Paid | 10-15% | 20-30% | 40%+ | | Annual → Renewal | 70-80% | 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) 1. **Headline copy** (10-30% lift potential) — Test problem-focused vs benefit-focused vs social-proof 2. **CTA button** (5-20% lift) — Test color, copy, size, position 3. **Social proof type** (5-15% lift) — Test logos vs testimonials vs numbers vs case studies 4. **Form length** (10-25% lift) — Test fewer fields, progressive profiling 5. **Page layout** (5-15% lift) — Test long-form vs short-form, video vs text 6. **Pricing display** (10-30% lift) — Test anchoring, default selection, feature comparison 7. **Trust signals** (3-10% lift) — Test guarantees, security badges, review scores --- ## 8. Retention & Re-engagement — Keeping Users ### Lifecycle Email Sequences #### Welcome Sequence (Days 0-14) ```yaml 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) ```yaml 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: | Signal | Timeframe | Risk Level | |--------|-----------|------------| | Login frequency drops 50%+ | Week over week | 🟡 Medium | | Key feature usage stops | 7 days | 🟡 Medium | | Support ticket unresolved >48h | Rolling | 🟡 Medium | | No logins for 14+ days | Rolling | 🔴 High | | Billing failure (payment method expired) | Event | 🔴 High | | Export/download of all data | Event | 🔴 Critical | | Admin user leaves company | Event | 🔴 Critical | **Response playbook:** Trigger automated outreach at 🟡, human outreach at 🔴. --- ## 9. Scaling — From Working to 10x ### When to Scale a Channel ```yaml 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 1. **Automate first** — Before hiring, automate everything possible (email sequences, ad management, content scheduling) 2. **Document SOPs** — Every process needs a playbook before delegation 3. **Hire specialists, not generalists** — At scale, you need a paid ads person, not a "growth person" 4. **Build dashboards before scaling** — If you can't measure it in real-time, you can't scale it safely 5. **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) --- ## 10. Growth Team Structure ### 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 | Meeting | Frequency | Duration | Purpose | |---------|-----------|----------|---------| | Experiment standup | 2x/week | 15 min | Status of running experiments | | Metrics review | Weekly | 30 min | NSM, funnel metrics, cohort review | | Experiment planning | Weekly | 45 min | Prioritize next week's experiments (ICE scoring) | | Growth strategy | Monthly | 90 min | Channel performance, resource allocation, quarterly goals | --- ## 11. Growth Toolkit — Technical Setup ### Analytics Stack (Minimum Viable) ```yaml 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: ```yaml 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" ``` --- ## 12. Anti-Patterns & Common Mistakes ### The 10 Growth Killers 1. **Scaling before PMF** — Spending on acquisition when retention is broken = burning money 2. **Vanity metrics addiction** — Signups, downloads, pageviews mean nothing without activation + retention 3. **Copying without context** — "Dropbox did referrals" doesn't mean you should. Understand WHY it worked for THEM 4. **Too many channels too soon** — Master ONE before adding another. Spread thin = learn nothing 5. **Peeking at A/B tests** — Stopping tests early inflates false positives 3-5x. Run to completion 6. **Optimizing pennies** — CRO on a page getting 100 visits/month is pointless. Get traffic first 7. **Ignoring retention** — Acquiring users you can't keep is literally the most expensive thing you can do 8. **Over-automating before understanding** — Automate processes you've done manually 50+ times. Not before 9. **Growth hacks without strategy** — One-off tactics without a system = random acts of marketing 10. **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?) --- ## 13. Edge Cases & Special Situations ### B2B vs B2C Growth Differences | Dimension | B2B | B2C | |-----------|-----|-----| | Sales cycle | Weeks-months | Minutes-days | | Decision makers | 3-7 people | 1 person | | Channels | LinkedIn, content, events, outbound | Social, SEO, paid, viral | | Pricing | Value-based, negotiated | Fixed, transparent | | Retention driver | Switching cost, integration depth | Habit, engagement | | Referral mechanics | Case studies, introductions | In-product, social sharing | ### Two-Sided Marketplace Growth Chicken-and-egg solution order: 1. Seed supply manually (scrape, import, do it yourself) 2. Constrain geography (one city/niche first) 3. Offer supply-side tools for free (even without demand) 4. Build just enough demand to show supply it works 5. Let organic flywheel take over before expanding geography ### PLG (Product-Led Growth) Specifics ```yaml 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 1. Build in public (Twitter/LinkedIn) — share metrics, learnings, behind-the-scenes 2. Launch on 5 platforms: Product Hunt, HN, Reddit, Indie Hackers, relevant Discords 3. Write 1 SEO article/week targeting long-tail keywords 4. Offer free tool that solves a related problem → funnel to main product 5. Cold DM 10 potential users/day — ask for feedback, not sales 6. Partner with complementary products for cross-promotion 7. Answer questions on Quora/Reddit/forums where your ICP hangs out --- ## 14. Weekly Growth Review Template ```yaml 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: | Command | Action | |---------|--------| | "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 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)