Growth Hacker

Growth Hacker Skill

Domain knowledge for growth strategy, funnel optimization, and scalable user acquisition. Feeds into: Composer Group C (revenue/channels), AI Chat growth strategy mode, market-research edge function.

AARRR Pirate Metrics Framework

Every growth strategy maps to one of these five stages. Diagnose which stage is broken before optimizing.

Growth Channel Selection

Channel Prioritization (ICE Score)

Score each channel 1-10 on three dimensions, then rank by average:

• Impact: If this works, how much growth does it drive?
• Confidence: How sure are we this will work for our ICP?
• Ease: How quickly can we test this with minimal resources?

Channel Categories by Stage

Viral Loop Design

A viral loop is a closed cycle where existing users bring new users through product usage (not marketing).

Loop structure: User experiences value -> Shares/invites -> New user joins -> Experiences value -> Repeats

Types of viral loops:

1. Inherent: Product requires others (Slack, Figma, Zoom)
2. Collaborative: Sharing improves experience (Google Docs, Notion)
3. Incentivized: Referral rewards (Dropbox extra storage, Uber credits)
4. Social proof: Usage visible to others (Spotify wrapped, Strava activities)

Viral coefficient formula: K = invites_per_user x conversion_rate_per_invite

• K < 0.3: No meaningful virality
• K = 0.3-0.7: Amplifies other channels
• K > 1.0: Self-sustaining viral growth (rare)

CAC / LTV Analysis

Unit Economics Health Check

CAC Reduction Tactics

1. Improve activation: Reducing signup-to-value friction is the highest-ROI growth lever
2. Increase organic share: Content, SEO, community reduce blended CAC
3. Optimize paid channels: Kill underperformers weekly, reallocate to top 20% of campaigns
4. Build referral loops: Even a K-factor of 0.2 reduces effective CAC by 20%
5. Shorten sales cycle: For B2B, reducing cycle time = reducing CAC (fewer touches per deal)

Funnel Optimization

Conversion Benchmarks (SaaS)

Experiment Velocity

Run 10+ growth experiments per month. Structure each as:

Hypothesis: [Changing X] will [improve Y metric] by [Z%]
Metric: [Primary metric to measure]
Duration: [1-2 weeks minimum for statistical significance]
Sample size: [Minimum needed for confidence]
Success criteria: [Specific threshold]

Winner rate target: 30% of experiments show statistically significant positive results. If your winner rate is above 50%, your experiments are not ambitious enough.

North Star Metric

Every startup needs one metric that captures the core value delivered to users. All growth activity ladders up to this.

Gemini Output Schema -- ICE Channel Chips

When the Growth Strategy chat mode recommends channels, use this structured output for Gemini's responseJsonSchema:

{
  "type": "object",
  "properties": {
    "channels": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "channel": { "type": "string", "description": "Channel name (e.g., Content Marketing, LinkedIn Outreach)" },
          "impact": { "type": "integer", "minimum": 1, "maximum": 10 },
          "confidence": { "type": "integer", "minimum": 1, "maximum": 10 },
          "ease": { "type": "integer", "minimum": 1, "maximum": 10 },
          "ice_score": { "type": "number", "description": "Average of impact, confidence, ease" },
          "recommendation": { "type": "string", "description": "One-line actionable recommendation" },
          "stage_fit": { "type": "string", "enum": ["pre-pmf", "early-traction", "growth", "scale"] }
        },
        "required": ["channel", "impact", "confidence", "ease", "ice_score", "recommendation"]
      }
    },
    "aarrr_stage": {
      "type": "string",
      "enum": ["acquisition", "activation", "retention", "revenue", "referral"],
      "description": "Which AARRR stage is the weakest and should be prioritized"
    },
    "north_star_metric": {
      "type": "string",
      "description": "Recommended north star metric for this startup"
    }
  }
}

ICE Chip Rendering

Frontend renders each channel as a chip with:

• Channel name (bold)
• ICE score (colored: green >=7, amber 4-6, red <4)
• One-line recommendation on hover/expand

StartupAI Integration Points

• Composer Group C: Revenue model, channels, and unit economics draw from CAC/LTV analysis and channel selection frameworks above
• AI Chat growth mode: When founders ask about growth, reference AARRR stage diagnosis, channel ICE scoring, and experiment velocity
• market-research EF: Channel benchmarks and viral coefficient data inform market sizing and go-to-market sections
• Validator scoring: Growth dimension evaluates whether the startup has identified scalable acquisition channels with viable unit economics