jmsktm/growth

ID8GROWTH - Growth Engine

Purpose

Scale your launched product through systematic experimentation. Growth is not magic—it's methodology.

Philosophy: Retention beats acquisition. One channel mastered beats five attempted. Data over intuition.

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When to Use

• Product is launched and has initial users
• User needs to grow user base
• User asks "how do I get more users?"
• User wants to improve retention
• User needs help with analytics
• User wants to optimize conversion
• Project is in LAUNCHING or GROWING state

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Commands

/growth <project-slug>

Run full growth analysis and planning.

Process:

1. BASELINE - Understand current metrics
2. MODEL - Map growth mechanics
3. DIAGNOSE - Find bottlenecks
4. HYPOTHESIZE - Generate experiments
5. PRIORITIZE - ICE scoring
6. EXECUTE - Run experiments
7. LEARN - Analyze and iterate

/growth metrics

Audit current analytics and define key metrics.

/growth funnel

Analyze conversion funnel and identify drop-offs.

/growth experiment <hypothesis>

Design a specific growth experiment.

/growth retention

Deep dive on retention and engagement.

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Growth Philosophy

Solo Builder Reality

| What Works | What Doesn't | |------------|--------------| | Focused effort on one channel | Spray-and-pray multi-channel | | Retention optimization | Endless acquisition | | Organic/content marketing | Expensive paid acquisition | | Personal touch | Automated spam | | Slow compounding | Viral hacks |

Growth Priorities

Stage 1: Pre-PMF (< 100 users)

• Focus: Finding users who love it
• Metric: Qualitative feedback, NPS
• Don't worry about: Scale

Stage 2: Early Traction (100-1000 users)

• Focus: Retention and activation
• Metric: Day 1/7/30 retention
• Don't worry about: Growth rate

Stage 3: Growth (1000+ users)

• Focus: Scalable acquisition
• Metric: CAC, LTV, growth rate
• Now optimize: Everything

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Process Detail

Phase 1: BASELINE

Establish current state:

| Metric | Value | Source | |--------|-------|--------| | Total users | {N} | Database | | Active users (DAU/WAU/MAU) | {N} | Analytics | | Activation rate | {%} | Funnel | | Retention (D1/D7/D30) | {%} | Cohort | | Conversion (free→paid) | {%} | Funnel | | Revenue (MRR/ARR) | ${X} | Payments | | NPS | {score} | Survey |

If no tracking:

• Set up analytics first
• Use analytics-tracking skill
• Minimum: Sign-ups, activation, retention

Phase 2: MODEL

Map your growth mechanics:

ACQUISITION
How do users find you?
├── Organic search
├── Social/content
├── Referrals
├── Paid (if any)
└── Direct

ACTIVATION
What's the "aha moment"?
├── First action completed
├── Value received
└── Setup finished

RETENTION
Why do they come back?
├── Core value loop
├── Notifications
├── Habit formation
└── New content/features

REVENUE
How do you monetize?
├── Subscription
├── Usage-based
├── One-time
└── Freemium conversion

REFERRAL
How do they spread it?
├── Word of mouth
├── Built-in sharing
├── Incentivized referral
└── Social proof

Phase 3: DIAGNOSE

Find the bottleneck:

| Stage | Benchmark | Your Rate | Status | |-------|-----------|-----------|--------| | Visitor → Sign-up | 2-5% | {%} | {OK/LOW} | | Sign-up → Activated | 20-40% | {%} | {OK/LOW} | | Activated → Day 7 | 20-30% | {%} | {OK/LOW} | | Day 7 → Day 30 | 50-70% | {%} | {OK/LOW} | | Free → Paid | 2-5% | {%} | {OK/LOW} |

Diagnosis framework:

1. Compare to benchmarks
2. Identify biggest drop-off
3. That's your focus

Phase 4: HYPOTHESIZE

Generate experiment ideas:

For each bottleneck, generate 3-5 hypotheses:

If we [change]
Then [metric] will [improve/increase/decrease]
Because [reasoning]

Example:

If we add an onboarding checklist
Then activation rate will increase by 20%
Because users will know what to do next

Phase 5: PRIORITIZE

ICE Scoring:

| Experiment | Impact | Confidence | Ease | Score | |------------|--------|------------|------|-------| | {exp 1} | {1-10} | {1-10} | {1-10} | {avg} | | {exp 2} | {1-10} | {1-10} | {1-10} | {avg} |

Definitions:

• Impact: How much will this move the metric?
• Confidence: How sure are we it will work?
• Ease: How easy is it to implement?

Rule: Do highest ICE score first.

Phase 6: EXECUTE

For each experiment:

1. Define hypothesis clearly
2. Define success metric
3. Define sample size needed
4. Implement change
5. Run for sufficient time
6. Analyze results
7. Document learnings

Minimum experiment duration:

• High traffic: 1-2 weeks
• Low traffic: 2-4 weeks
• Statistical significance matters

Phase 7: LEARN

After each experiment:

| Question | Answer | |----------|--------| | Did it work? | {Yes/No/Inconclusive} | | What was the lift? | {X}% | | Why did it work/fail? | {reasoning} | | What did we learn? | {insight} | | What's next? | {next experiment} |

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Framework References

Growth Loops

frameworks/growth-loops.md - Viral, content, flywheel mechanics

Analytics

frameworks/analytics.md - Metrics, tracking, dashboards

Acquisition

frameworks/acquisition.md - Channels, CAC, scale

Retention

frameworks/retention.md - Engagement, churn, habit

Optimization

frameworks/optimization.md - A/B testing, CRO

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Output Templates

Growth Model

templates/growth-model.md - Growth strategy document

Metrics Dashboard

templates/metrics-dashboard.md - KPI tracking structure

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Tool Integration

MCPs

Supabase:

• Query user data for analysis
• Cohort analysis
• Funnel tracking

Perplexity:

• Research growth tactics
• Find benchmarks
• Competitor analysis

Skills

analytics-tracking:

• Set up tracking
• Define events
• Create dashboards

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Handoff

After completing growth analysis:

1. Save outputs:

   • Growth model → docs/GROWTH_MODEL.md
   • Metrics → docs/METRICS.md

2. Log to tracker:

   /tracker log {project-slug} "GROWTH: Analysis complete. Focus: {bottleneck}. Top experiment: {experiment}."
   

3. Update state:

   /tracker update {project-slug} GROWING
   

4. Next steps:

   • Execute top-priority experiments
   • Review results weekly
   • When stable, transition to ops

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Key Metrics Cheat Sheet

AARRR Funnel

| Stage | What to Track | |-------|---------------| | Acquisition | Traffic, channels, CAC | | Activation | Sign-up rate, onboarding completion | | Retention | DAU/MAU, D1/D7/D30, churn | | Revenue | MRR, ARPU, LTV | | Referral | K-factor, invite rate |

Benchmarks

| Metric | Poor | OK | Good | Great | |--------|------|----|----- |-------| | D1 retention | <10% | 10-20% | 20-30% | >30% | | D7 retention | <5% | 5-10% | 10-20% | >20% | | D30 retention | <2% | 2-5% | 5-10% | >10% | | Free→Paid | <1% | 1-2% | 2-5% | >5% | | NPS | <0 | 0-30 | 30-50 | >50 |

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Anti-Patterns

| Anti-Pattern | Why Bad | Do Instead | |--------------|---------|------------| | Vanity metrics | Don't drive business | Focus on actionable metrics | | Too many experiments | No learnings | One experiment at a time | | No hypothesis | Can't learn | Always have clear hypothesis | | Short experiments | Inconclusive | Run to significance | | Ignoring retention | Leaky bucket | Fix retention first | | Copying others | Context matters | Adapt to your situation |

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Quality Checks

Before finalizing growth plan:

• [ ] Baseline metrics established
• [ ] Biggest bottleneck identified
• [ ] Hypotheses are testable
• [ ] Experiments are prioritized
• [ ] Success metrics defined
• [ ] Realistic timeline set
• [ ] Learning process planned