Growth experimentation skill

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