v1.0.0 to v1.0.0

---

name: growth-experimentation

description: >-

Build a growth experimentation system — ICE scoring, growth sprints, experiment

design, statistical significance, and learning repositories. Use when building

an experimentation program, running growth sprints, prioritizing tests, or

establishing a data-driven growth culture. Triggers on: "experimentation",

"growth experiments", "A/B testing program", "ICE scoring", "growth sprint",

"experiment design", "test velocity", or any growth experimentation request.

license: MIT

metadata:

version: "1.0.0"

author: LeadMagic

category: analytics

tags: [experimentation, growth, testing, ice, sprints]

frameworks: [Sean Ellis Hacking Growth, Brian Balfour Reforge, Andrew Chen Growth, ICE Scoring]

---

# Growth Experimentation

## Overview

The companies with the highest growth rates don't have better ideas — they

have better systems for testing ideas. A high-velocity experimentation system

runs 15-30 experiments per month across acquisition, activation, retention,

and monetization. Most experiments fail. That's by design. The team that

learns fastest from each failure wins.

## When to Use

- "Build an experimentation program"

- "Set up growth sprints"

- "Prioritize experiments with ICE"

- "Increase our test velocity"

- "Create a learning repository"

## Authoritative Foundations

- **Sean Ellis & Morgan Brown (Hacking Growth)** — coined "growth hacking."

North Star Metric. Growth experimentation loop: analyze → ideate →

prioritize → test → learn.

- **Brian Balfour (Reforge, ex-HubSpot VP Growth)** — increasing HubSpot's

experiment velocity from 5 to 20/week produced 3x growth rate improvement.

Four Fits Framework: Market-Product, Product-Channel, Channel-Model,

Model-Market.

- **Andrew Chen (a16z, ex-Uber Growth)** — The Cold Start Problem. Growth

teams at scale.

- **Fareed Mosavat (Reforge, ex-Slack Growth)** — experimentation systems.

## Step-by-Step Process

### Phase 1: Set the North Star Metric

One metric that captures core value delivery. If this moves up, the business

is healthier. All experiments ladder to this metric.

### Phase 2: ICE Scoring

Score every experiment idea 1-10 on Impact, Confidence, Ease. Average the

three. Prioritize by ICE score. Re-score weekly as new data arrives.

### Phase 3: Growth Sprint Cadence

Weekly cycle: idea generation (Monday), prioritization (Tuesday), build

(Wed-Thu), launch (Fri), analyze (Mon). 2-week sprints for complex tests.

AI compresses cycle: a single growth marketer with AI can test 10 variants

in time it used to take to build one.

### Phase 4: Experiment Design

Every experiment: hypothesis, success metric, minimum detectable effect,

required sample size, maximum duration. Document everything — winners

and losers. Build a searchable learning repository.

### Phase 5: 4 Layers of Experiments

1. Channel/tactic assessment — test how channels impact conversions

2. Offer optimization — pricing, packaging, trial length

3. Message personalization — copy and creative by segment

4. AI-powered — autonomous experiment generation, prediction, optimization

## Output Format

Experimentation system with North Star Metric definition, ICE backlog,

sprint calendar, experiment design template, and learning repository structure.

## Quality Check

Before delivering, verify:

- [ ] All required sections are complete

- [ ] Output matches the user's stated need

- [ ] Named frameworks are cited for key recommendations

- [ ] No vague claims — every recommendation has a specific action

- [ ] Deliverable is ready for operational use, not just conceptual

## Common Pitfalls

1. **Tests too large** — redesigning entire onboarding (4 weeks to build)

loses to testing a single screen change (2 days). Small tests = fast

learning.

2. **No learning repository** — running 50 experiments without documenting

learnings is running the same test twice. Document everything.

3. **Statistical ignorance** — calling a test at 70% confidence produces

false positives. Wait for 95%+ confidence.

4. **Winner's bias** — only shipping winners without understanding losers

means you don't know why things work.

## Execution Artifacts

This skill includes lightweight artifacts the agent can load on demand:

- `references/framework-notes.md` — named frameworks, citation anchors, and operating assumptions

- `templates/output-template.md` — copy-paste deliverable structure for the user

- `scripts/check-output.py` — local checklist validator for required sections

Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.

## Implementation Depth

Use this section when the user asks for a finished asset, not a high-level explanation.

### Diagnostic Questions

1. What is the primary motion: founder-led, sales-led, product-led, partner-led, or lifecycle-led?

2. Which ICP tier is the output for: small business, mid-market, enterprise, or mixed?

3. What proof is available today: customer stories, usage data, third-party validation, screenshots, or none?

4. What system will execute the work: CRM, sequencer, warehouse, support desk, product analytics, or manual workflow?

5. What decision will the user make from this output: launch, prioritize, route, rewrite, score, coach, or measure?

### Framework Application

Map the recommendation explicitly to the named frameworks in this skill:

- Sean Ellis Hacking Growth: apply only the part that directly improves the requested deliverable.

- Brian Balfour Reforge: apply only the part that directly improves the requested deliverable.

- Andrew Chen Growth: apply only the part that directly improves the requested deliverable.

- ICE Scoring: apply only the part that directly improves the requested deliverable.

### Deliverable Standard

A strong output from this skill includes:

- A crisp diagnosis of the current situation

- A recommended path with tradeoffs, not a generic list

- A concrete artifact the user can use immediately: table, script, checklist, scorecard, sequence, dashboard spec, or implementation plan

- A measurement plan with leading and lagging indicators

- Risks and edge cases called out before execution

### Adaptation Rules

- For small business: reduce complexity, shorten time-to-value, and prioritize owner/operator clarity.

- For mid-market: include workflow ownership, handoffs, integrations, and enablement assets.

- For enterprise: include governance, risk, procurement, stakeholder mapping, and proof requirements.

## Related Skills

- **a-b-testing**: Statistical framework for individual tests

- **gtm-metrics**: Growth metrics and dashboard design