domains / marketing / growth-experimentation / growth-engine

growth-engine

Skill card

combined

The two skills share the name 'growth-engine' but describe fundamentally different procedures. The first (ericosiU) is a technical, code-driven framework for running A/B tests, logging data, and performing statistical analysis (bootstrap CI, Mann-Whitney U) to promote winning marketing variants. The second (sickn33) is a strategic content guide for a specific product ('Auri'), focusing on Pirate Metrics (AARRR), SEO/ASO optimization, and viral loop design. They do not reproduce or shorten each other; they address different aspects of growth (experimental validation vs. strategic planning/optimization).

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Combined skill

# Growth Engine

This skill covers two complementary approaches to growth: statistical experimentation and strategic optimization.

## 1. Experimental Framework (A/B Testing)
Use this section when you need to validate hypotheses with data.

### Workflow
1. **Check Playbook**: Run `python3 experiment-engine.py playbook --agent <agent_name>` to apply proven best practices.
2. **Create Experiment**: 
   ```bash
   python3 experiment-engine.py create \
     --agent <agent_name> \
     --hypothesis "Hypothesis" \
     --variable "variable" \
     --variants '["A", "B"]' \
     --metric "metric" \
     --cycle-hours 24
   ```
3. **Log Data**: After publishing, log metrics:
   ```bash
   python3 experiment-engine.py log \
     --agent <agent_name> \
     --experiment-id <ID> \
     --variant "A" \
     --metrics '{"metric": 100}'
   ```
4. **Score**: Periodically check for significance:
   ```bash
   python3 experiment-engine.py score --agent <agent_name> --experiment-id <ID>
   ```
   *Winners (p < 0.05 AND ≥ 15% lift) auto-promote to the playbook.*
5. **Suggest**: Use `python3 experiment-engine.py suggest --agent <agent_name>` for next steps.

## 2. Strategic Optimization (AARRR & SEO)
Use this section for high-level growth strategy and channel optimization.

### Pirate Metrics (AARRR)
- **Acquisition**: Define channels (SEO, PR, Influencers). Target: 10k visitors/mo.
- **Activation**: Ensure users experience value quickly. Target: 60% complete first action in 24h.
- **Retention**: Monitor D7/D30/D90 retention. Target: D7=30%, D30=15%.
- **Revenue**: Track MRR/ARPU. Target: 8% trial-to-paid conversion.
- **Referral**: Implement viral loops. Target: K-factor > 0.3.

### SEO & ASO Checklist
- **Meta Tags**: Optimize `<title>` and `<meta name="description">` for high-intent keywords.
- **Schema**: Implement `SoftwareApplication` JSON-LD for rich snippets.
- **ASO**: Optimize app store listings with clear value propositions and example phrases.

### Viral Loops
- **Word-of-Mouth**: Encourage sharing of impressive results.
- **Share Insights**: Add "Share" buttons for generated content.
- **Referral Program**: Incentivize referrals (e.g., free Pro months).

### K-Factor Calculation
```python
def calculate_k_factor(percent_who_invite, invites_per_user, conversion_rate):
    k = percent_who_invite * invites_per_user * conversion_rate
    # Interpret k: >=1 Viral, >=0.5 Good, >=0.2 Ok, <0.2 Low
    return k
```