The 4-Experiments/Month Planner

Teams running fewer than 4 experiments per month learn too slowly to compete. Teams running more than 8 burn out and ship sloppy tests. After managing 2,000+ experiments across our portfolio, we've found the sweet spot: 4 experiments monthly, in 2-week cycles. Here's the exact playbook.

Decision Latency and Opportunity Cost

Every week you don't test is a week you don't learn. We've tracked decision latency across 200+ product teams:

• Average time from idea to test: 6 weeks
• Average time from test to decision: 3 weeks
• Total learning cycle: 9 weeks

At this pace, teams run 5-6 experiments per year. Market leaders run 48+.

The cost compounds:

• Slow teams: 6 learnings/year
• Fast teams: 48 learnings/year
• Learning advantage: 8x

Two-Week Cycles & Sample-Size Sanity

The biggest experiment killer? Waiting for statistical significance that never comes. Here's our 2-week cycle framework that balances speed with statistical rigor:

Week 1: Design & Deploy

Days 1-3: Hypothesis & Design

• Problem statement (1 sentence)
• Hypothesis (If we X, then Y because Z)
• Success metrics (primary + guardrails)
• Quick mockup or prototype

Days 4-5: Technical Setup

• Implement variant(s)
• QA test all paths
• Set up analytics
• Deploy to 10% traffic

Week 2: Run & Read

Days 6-12: Active Experiment

• Monitor daily for errors
• Check sample size progress
• Watch for guardrail metrics
• Document observations

Days 13-14: Analysis & Decision

• Statistical significance check
• Practical significance evaluation
• Go/no-go decision
• Next experiment planning

Experiment Backlog Grooming

A healthy experiment backlog fuels consistent velocity. We use the ICE framework with a twist:

Impact (1-10): Potential lift to primary metric Confidence (1-10): Strength of hypothesis based on data Effort (1-10): Developer days required (inverted - 10 = easy)

Our twist: Multiply by Learning Value (1-3)

• 3 = Validates core assumption
• 2 = Optimizes known winner
• 1 = Incremental improvement

Experiment Pipeline

Guardrails and Ethics

Speed without safety leads to bad decisions. Every experiment needs guardrails:

Technical Guardrails

• Error rate <0.1% increase
• Page load <100ms increase
• Crash rate unchanged
• Analytics firing correctly

Business Guardrails

• Revenue per user ±5%
• Support tickets <20% increase
• Churn rate unchanged
• NPS ±5 points

Ethical Guardrails

• No dark patterns
• Clear value exchange
• Reversible actions
• Transparent pricing

Template Walkthrough

Here's the exact template we use for every experiment:

## Experiment: [Name]

**Owner**: [PM/Engineer] **Duration**: [Start] - [End] **Traffic**: [X%]

### Problem

[1-2 sentences on what users struggle with]

### Hypothesis

If we [change], Then [metric] will [increase/decrease] by [X%] Because [reasoning based on user
behavior]

### Variants

- Control: [Current state]
- Variant A: [Change description]
- (Variant B: [If multivariate])

### Success Metrics

- Primary: [Single metric + target]
- Guardrails: [3-4 metrics to not harm]

### Results

- Sample size: [Actual N per variant]
- Primary metric: [Lift % + confidence interval]
- Statistical significance: [Yes/No + p-value]
- Decision: [Ship/Iterate/Kill]

### Learnings

[2-3 bullets on what we learned about users]

### Next Steps

[Follow-up experiments or implementation plan]

Real Playbook: B2B SaaS Case Study

Let's walk through a real month of experiments from a portfolio company:

Week 1-2: Signup Simplification

Hypothesis: Reducing signup from 5 fields to 2 will increase completion by 25% Result: 31% increase in signups, shipped to 100% Learning: Users will give more info post-signup if value is clear

Week 3-4: Onboarding Video

Hypothesis: 2-minute video will increase activation by 20% Result: 3% increase, not significant, killed Learning: Users want to explore, not watch

Week 5-6: Trial Extension Offer

Hypothesis: Offering 7-day extension will increase conversion 15% Result: 22% increase in conversion, shipped Learning: Users need more time to get buy-in

Week 7-8: Annual Pricing Default

Hypothesis: Defaulting to annual will increase revenue per user 30% Result: 18% increase in revenue, 5% decrease in conversions, shipped Learning: Trade-off acceptable for unit economics

Month Summary: 4 experiments, 3 shipped, 52% revenue increase

Scaling Your Experiment Velocity

Once you hit 4 experiments monthly, scale carefully:

Month 1-3: Foundation (4 experiments/month)

• Focus on highest-impact areas
• Build experiment muscle memory
• Establish review rituals
• Document everything

Month 4-6: Acceleration (6 experiments/month)

• Add dedicated analyst resource
• Build reusable test components
• Automate results dashboards
• Expand to multiple surfaces

Month 7+: Optimization (8 experiments/month max)

• Dedicated growth team
• Parallel track experiments
• Machine learning for analysis
• Cross-team learning systems

Common Pitfalls and Fixes

Pitfall 1: Analysis Paralysis

• Symptom: 3+ weeks to analyze results
• Fix: Pre-commit to decision criteria

Pitfall 2: Testing Everything

• Symptom: Testing button colors while checkout is broken
• Fix: ICE scoring with Learning multiplier

Pitfall 3: Lone Wolf Testing

• Symptom: Only growth team knows results
• Fix: Weekly all-hands learning share

Pitfall 4: Success Theater

• Symptom: Only positive results shared
• Fix: Celebrate learning, not just wins

Your 30-Day Rollout Plan

Ready to hit 4 experiments per month? Here's your week-by-week plan:

Week 1: Foundation

• Set up basic analytics
• Create experiment template
• Score first 10 ideas
• Run one simple test

Week 2: Process

• Document first results
• Share learnings broadly
• Set up weekly review
• Queue next 2 tests

Week 3: Velocity

• Run 2 parallel tests
• Automate analysis dashboard
• Build experiment backlog
• Assign clear owners

Week 4: Scale

• Hit 4 experiments
• Review month's learnings
• Plan next month's tests
• Celebrate progress

Now Do This

Transform your learning velocity in the next 30 days:

Ready to organize your experiments? Our Experiment Planner helps you track ideas, calculate sample sizes, and monitor results in one place. For setting the right success metrics, check out our guide on latency budgets that actually stick.

Want to ensure quality while moving fast? Our acceptance criteria guide shows how to define "done" for experiments.