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.

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

Commands

`/growth <project-slug>`

Run full growth analysis and planning.

Process:

BASELINE - Understand current metrics
MODEL - Map growth mechanics
DIAGNOSE - Find bottlenecks
HYPOTHESIZE - Generate experiments
PRIORITIZE - ICE scoring
EXECUTE - Run experiments
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.

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

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:

Compare to benchmarks
Identify biggest drop-off
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:

Define hypothesis clearly
Define success metric
Define sample size needed
Implement change
Run for sufficient time
Analyze results
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} |

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

Output Templates

Growth Model

templates/growth-model.md - Growth strategy document

Metrics Dashboard

templates/metrics-dashboard.md - KPI tracking structure

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

Handoff

After completing growth analysis:

Save outputs:
- Growth model → docs/GROWTH_MODEL.md
- Metrics → docs/METRICS.md

Log to tracker:

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

Update state:
```
/tracker update {project-slug} GROWING
```
Next steps:
- Execute top-priority experiments
- Review results weekly
- When stable, transition to ops

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 |

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 |

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