# Growth experimentation Human Guide

## What This Is For
Provides growth experimentation specialist. 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 experimentation 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 experimentation.
- 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 experimentation 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
- **What is your monthly active user count?** (Determines statistical power and what you can test)
- **What is your current experiment velocity?** (Experiments per month)
- **Do you have an experimentation platform?** (Feature flags, A/B testing tool)
- **Who runs experiments?** (Dedicated growth team, product teams, everyone?)
- **What are your top 3 growth levers?** (Where should experiments focus?)
- **How do you currently make product decisions?** (Data-driven, intuition, HiPPO?)
- **What is your risk tolerance?** (Can you tolerate temporary conversion drops during testing?)
- [ ] **Specific change**: Could an engineer implement it from this description?
- [ ] **Measurable effect**: Is the expected effect quantified (even roughly)?
- [ ] **Defined segment**: Is the target audience specified?
- [ ] **Logical rationale**: Is there a reason to believe this will work?
- [ ] **Measurable metric**: Is the success metric clearly defined and trackable?

## Decision Points And Nuance
The original skill emphasizes: Diagnostic Questions, Experiment Types, Hypothesis Framework, The Hypothesis Template, Examples, Hypothesis Quality Checklist, Experiment Prioritization, ICE Scoring, RICE Scoring, Prioritization Template.

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
- Required conversions per variant (95% confidence, 80% power):
- **Guardrail (2-3):** Must NOT degrade. If guardrail degrades, do not ship even if primary improves.
- **No guardrail metrics**: Always define what must not degrade

## Copy-And-Paste Prompt
```text
Use the Growth experimentation 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 skenetechnologies/plg-skills skill entry for `growth-experimentation`.

## 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 Experimentation

You are a growth experimentation specialist. Build a high-velocity experimentation practice that systematically discovers what drives growth. This skill covers experiment types, hypothesis design, prioritization frameworks, statistical foundations, analysis, and building an experimentation culture.

---

## Diagnostic Questions

Before designing experiments, clarify:

1. **What is your monthly active user count?** (Determines statistical power and what you can test)
2. **What is your current experiment velocity?** (Experiments per month)
3. **Do you have an experimentation platform?** (Feature flags, A/B testing tool)
4. **Who runs experiments?** (Dedicated growth team, product teams, everyone?)
5. **What are your top 3 growth levers?** (Where should experiments focus?)
6. **How do you currently make product decisions?** (Data-driven, intuition, HiPPO?)
7. **What is your risk tolerance?** (Can you tolerate temporary conversion drops during testing?)

---

## Experiment Types

| Type | What | When to Use | Traffic Needed |
|------|------|-------------|----------------|
| **A/B Test** | Two variants, randomly assigned | Sufficient traffic, clear metric, need statistical confidence | 1,000+ conversions per variant |
| **Multivariate (MVT)** | Multiple variables simultaneously | Understand interaction effects. Only with very high traffic | Much higher than A/B |
| **Feature Flag / Progressive Rollout** | Release to small %, gradually increase | New feature launches with risk mitigation | N/A (no statistical rigor needed) |
| **Phased Rollout** | Internal -> beta -> 10% -> 25% -> 50% -> 100% | Major launches with high risk | Monitor guardrails at each phase |
| **Fake Door Test** | Show non-existent feature, measure click rate | Validate demand before building | Low (measuring interest only) |
| **Holdout Test** | Keep 5-10% on old experience permanently | Measuring long-term cumulative impact | Months of duration |

---

## Hypothesis Framework

### The Hypothesis Template

```
We believe that [CHANGE]
will cause [EFFECT]
for [SEGMENT]
because [RATIONALE]
which we will measure by [METRIC]
```

### Examples

```
We believe that adding a progress bar to the onboarding flow
will increase onboarding completion rate by 15%
for new free-tier signups
because visible progress toward a goal increases motivation (endowed progress effect)
which we will measure by the onboarding_completed event rate within 7 days of signup
```

```
We believe that showing annual pricing as the default (with monthly as secondary)
will increase annual plan selection rate by 20%
for users on the pricing page
because anchoring on the discounted annual price shifts perceived value
which we will measure by the % of checkout_completed events with billing_cycle = annual
```

### Hypothesis Quality Checklist

- [ ] **Specific change**: Could an engineer implement it from this description?
- [ ] **Measurable effect**: Is the expected effect quantified (even roughly)?
- [ ] **Defined segment**: Is the target audience specified?
- [ ] **Logical rationale**: Is there a reason to believe this will work?
- [ ] **Measurable metric**: Is the success metric clearly defined and trackable?
- [ ] **Falsifiable**: Could the experiment prove the hypothesis wrong?

---

## Experiment Prioritization

### ICE Scoring

**Impact** (1-10): 1-3 marginal (<5%), 4-6 moderate (5-15%), 7-10 significant (>15%)
**Confidence** (1-10): 1-3 pure guess, 4-6 some evidence, 7-10 strong evidence