# Growth experimenter Human Guide

## What This Is For
Run systematic experiments to grow faster through data-driven optimization. It gives the agent a clearer input/output frame for growth marketing: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Growth experimenter agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for growth experimenter.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Growth experimenter guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Increase your hit rate through better hypotheses
- If 40% sign up but only 10% are active at D30 → Fix retention
- If 80% are active but only 5% pay → Fix monetization
- If 2% visitors sign up but 60% convert to paid → Get more traffic
- "Only 2% of homepage visitors sign up" (specific metric)
- "50% of trials don't complete onboarding" (clear drop-off)
- "Users who invite teammates have 3x retention, but only 10% invite" (known behavior)
- "We need more growth" (too vague)
- "Conversion is bad" (no baseline)
- "Users don't understand the product" (not measurable)
- [ ] Random assignment (50/50 split)
- [ ] Same time period (no day-of-week effects)

## Decision Points And Nuance
The original skill emphasizes: Core Philosophy, Growth Model (AARRR / Pirate Metrics), Experiment Framework, Identify the Problem, Form a Hypothesis, Design the Experiment, Run the Experiment, Analyze Results, Scale What Works, Growth Experiments by Stage.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- "50% of trials don't complete onboarding" (clear drop-off)
- "Users don't understand the product" (not measurable)
- Current: Users must create project from scratch
- Actions: Keep 14-day trial, don't test shorter
- ❌ **P-hacking**: Don't cherry-pick favorable metrics
- ❌ **Seasonal effects**: Don't test during holidays/anomalies
- [ ] Calculate required sample sizes

## Copy-And-Paste Prompt
```text
Use the Growth experimenter human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the daffy0208/ai-dev-standards skill entry for `growth-experimenter`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Growth Experimenter

Run systematic experiments to grow faster through data-driven optimization.

## Core Philosophy

**Growth = Experimentation Velocity × Win Rate × Impact per Win**

- Run more experiments
- Increase your hit rate through better hypotheses
- Focus on high-impact areas

## Growth Model (AARRR / Pirate Metrics)

```
Acquisition → Activation → Retention → Revenue → Referral
    ↓             ↓            ↓          ↓         ↓
  Traffic      Sign Up      Day 30    Upgrade   Invites
  100%          40%          50%        20%       10%

Example: 10,000 visitors/month
→ 4,000 signups (40%)
→ 2,000 active at D30 (50%)
→ 400 paying (20%)
→ 40 referrals (10%)

Improve ANY metric by 10% = 10% more customers
```

**Where to focus first**: The leakiest bucket

- If 40% sign up but only 10% are active at D30 → Fix retention
- If 80% are active but only 5% pay → Fix monetization
- If 2% visitors sign up but 60% convert to paid → Get more traffic

## Experiment Framework

### 1. Identify the Problem

**Good problem statements**:

- "Only 2% of homepage visitors sign up" (specific metric)
- "50% of trials don't complete onboarding" (clear drop-off)
- "Users who invite teammates have 3x retention, but only 10% invite" (known behavior)

**Bad problem statements**:

- "We need more growth" (too vague)
- "Conversion is bad" (no baseline)
- "Users don't understand the product" (not measurable)

### 2. Form a Hypothesis

**Hypothesis template**:

```
We believe that [change]
will result in [outcome]
because [reason/evidence]
```

**Examples**:

```markdown
✅ Good:
We believe that adding social proof (testimonials) to the pricing page
will increase trial signups by 10%
because visitors currently have low trust and need validation.

✅ Good:
We believe that sending a Slack notification when user completes setup
will increase D7 activation by 20%
because users forget to come back after initial signup.

❌ Bad:
We believe that changing the button color will improve conversions
(no reason why)

❌ Bad:
We believe that improving the product will increase retention