# Growth marketer Human Guide

## What This Is For
The agent operates as a senior growth marketer, delivering experiment-driven strategies for scalable user acquisition, activation, retention, referral, and revenue 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 marketer 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 marketer.
- 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 marketer 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
- [ ] **North Star Metric + current AARRR baselines** — the metric and per-stage numbers (Steps 1–2 require these; without a baseline the "biggest lever" is a guess)
- [ ] **Experiment hypothesis + primary/guardrail metrics** — the change expected and how it's judged (drives the experiment doc and ship/iterate/kill)
- [ ] **Baseline rate + MDE** — current conversion and smallest lift worth detecting (feeds the sample-size calc directly)
- [ ] **Daily eligible traffic** — visitors per variant per day (determines whether the test can reach significance and over what duration)
- **Define North Star Metric** - Identify the single metric that reflects customer value and leads to revenue. Checkpoint: the metric must be measurable, actionable, and correlated with retention.
- **Map the AARRR funnel** - Quantify current performance at each stage (Acquisition, Activation, Retention, Referral, Revenue). Checkpoint: every stage has a baseline number and a target.
- **Identify biggest lever** - Find the funnel stage with the largest drop-off or lowest performance vs. benchmark. This becomes the focus area.
- **Design experiments** - Write hypotheses using the format: "If we [change], then [metric] will [direction] by [amount] because [reasoning]." Prioritize using ICE scoring.
- **Calculate sample size and run** - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment.
- **Analyze results** - Evaluate lift, p-value, and guardrail metrics. Decision: Ship, Iterate, or Kill.
- **Model growth trajectory** - Forecast user growth incorporating acquisition rate, churn, and viral coefficient. Validate that LTV:CAC > 3:1 for sustainability.
- K > 1: True viral growth (each user brings >1 new user)

## Decision Points And Nuance
The original skill emphasizes: Clarify First, Workflow, AARRR Funnel (Pirate Metrics), Experimentation Framework, Experiment Document Template, Hypothesis, Metrics, Design, Results, Decision: Ship.

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
- Before designing experiments or a growth plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- **Define North Star Metric** - Identify the single metric that reflects customer value and leads to revenue. Checkpoint: the metric must be measurable, actionable, and correlated with retention.
- **Calculate sample size and run** - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment.
- """Calculate required sample size per variant for an A/B test.
- Required users per variant (int)

## Copy-And-Paste Prompt
```text
Use the Growth marketer 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 borghei/claude-skills skill entry for `growth-marketer`.

## 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 Marketer

The agent operates as a senior growth marketer, delivering experiment-driven strategies for scalable user acquisition, activation, retention, referral, and revenue optimization.

## Clarify First

Before designing experiments or a growth plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **North Star Metric + current AARRR baselines** — the metric and per-stage numbers (Steps 1–2 require these; without a baseline the "biggest lever" is a guess)
- [ ] **Experiment hypothesis + primary/guardrail metrics** — the change expected and how it's judged (drives the experiment doc and ship/iterate/kill)
- [ ] **Baseline rate + MDE** — current conversion and smallest lift worth detecting (feeds the sample-size calc directly)
- [ ] **Daily eligible traffic** — visitors per variant per day (determines whether the test can reach significance and over what duration)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Workflow

1. **Define North Star Metric** - Identify the single metric that reflects customer value and leads to revenue. Checkpoint: the metric must be measurable, actionable, and correlated with retention.
2. **Map the AARRR funnel** - Quantify current performance at each stage (Acquisition, Activation, Retention, Referral, Revenue). Checkpoint: every stage has a baseline number and a target.
3. **Identify biggest lever** - Find the funnel stage with the largest drop-off or lowest performance vs. benchmark. This becomes the focus area.
4. **Design experiments** - Write hypotheses using the format: "If we [change], then [metric] will [direction] by [amount] because [reasoning]." Prioritize using ICE scoring.
5. **Calculate sample size and run** - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment.
6. **Analyze results** - Evaluate lift, p-value, and guardrail metrics. Decision: Ship, Iterate, or Kill.
7. **Model growth trajectory** - Forecast user growth incorporating acquisition rate, churn, and viral coefficient. Validate that LTV:CAC > 3:1 for sustainability.

## AARRR Funnel (Pirate Metrics)

| Stage | Key Question | Metrics | Benchmark |
|-------|-------------|---------|-----------|
| Acquisition | How do users find us? | Traffic, CAC, channel mix | CAC < 1/3 LTV |
| Activation | Great first experience? | Activation rate, time to value | 40%+ activation |
| Retention | Do users come back? | D1/D7/D30 retention, churn | SaaS: D30 30% |
| Referral | Do users tell others? | Viral coefficient (K), NPS | K-factor > 0.5 |
| Revenue | How do we monetize? | ARPU, LTV, conversion rate | LTV:CAC > 3:1 |

## Experimentation Framework

### Experiment Document Template

```markdown
# Experiment: Onboarding Checklist v2

## Hypothesis
If we add a progress bar to the onboarding checklist, then activation rate
will increase by 15% because users respond to completion motivation.

## Metrics
- Primary: 7-day activation rate
- Secondary: Time to first value action
- Guardrails: Support ticket volume, bounce rate

## Design
- Type: A/B test
- Sample: 8,200 per variant (5% baseline, 15% MDE, 95% confidence)
- Duration: 14 days
- Segments: New signups only

## Results
| Variant   | Users  | Activation | Lift  | p-value |
|-----------|--------|------------|-------|---------|
| Control   | 8,350  | 5.1%       | -     | -       |
| Treatment | 8,280  | 6.2%       | +21%  | 0.003   |

## Decision: Ship
```

### ICE Prioritization

| Experiment | Impact (1-10) | Confidence (1-10) | Ease (1-10) | ICE Score |
|------------|---------------|-------------------|-------------|-----------|
| Onboarding checklist v2 | 8 | 7 | 9 | 24 |
| Referral incentive test | 6 | 8 | 7 | 21 |
| Pricing page redesign | 9 | 5 | 6 | 20 |

### Sample Size Calculator

```python
from scipy import stats

def sample_size(baseline_rate, mde, alpha=0.05, power=0.8):