growth-experimentation

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
compatibility	Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose
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

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