MA Newsletter
From Intuition to Evidence: Building Your First Experiment Framework
Hi there,
Most founders start with an intuition—a hunch about what their customers want or how their market works. But the most successful ones quickly transform those hunches into testable hypotheses.
Today, I'm sharing how to build your first experiment framework, inspired by what's working for the fastest-growing startups in our community.
Why Intuition Isn't Enough
A founder in our last cohort once told me: "I was certain customers wanted feature X. We spent three months building it perfectly. When we launched... crickets."
Sound familiar? The problem isn't bad intuition—it's treating intuition as fact rather than a starting point for investigation.
Your First Experiment Framework: The 4-Step Process
Based on our work with companies, here's a simplified framework you can implement this week:
1. Formulate Clear Hypotheses
A good hypothesis follows this structure:
- "We believe [specific action/change]..."
- "Will result in [specific outcome]..."
- "Which we can measure by [specific metric]..."
Example: "We believe that adding social proof to our checkout page will increase conversion rate by at least 15%, which we can measure by comparing the before/after conversion percentage."
Pro tip: Keep a hypothesis log. Document every assumption, no matter how obvious it seems.
2. Design Minimum Viable Tests
The goal isn't perfect data—it's sufficient data to make a better decision than pure intuition.
For each hypothesis, ask:
- What's the simplest way to test this?
- How can we get results in days, not weeks?
- What's the minimum sample size needed?
Example: Instead of rebuilding your entire checkout flow, create two variants with a tool like Unbounce and split traffic 50/50 for just 100 conversions.
3. Set Clear Success Criteria
Before running any test, document:
- What outcome would validate this hypothesis?
- What outcome would invalidate it?
- What's the threshold for "inconclusive"?
Example: Success = 15%+ improvement with 90% statistical significance. Failure = Less than 5% improvement. Inconclusive = Anything between.
4. Build a Prioritization System
Not all tests are created equal. Use this simple formula:
- Potential Impact (1-10) × Confidence (10-100%) ÷ Effort (days)
Example: A high-impact landing page test (8) with moderate confidence (50%) requiring 2 days of work would score: 8 × 0.5 ÷ 2 = 2.0
Run your highest-scoring tests first.
Putting It Into Practice: Your 1-Week Challenge
This week, I challenge you to:
- List your top 5 business assumptions
- Convert them into properly structured hypotheses
- Design a minimum viable test for your highest-priority hypothesis
- Document your success criteria
- Run the test, regardless of how imperfect it seems
Reply to this email with your hypothesis, and I'll personally give you feedback on your test design.
What Tools Do You Need?
The good news? You can start with almost nothing:
- Google Forms for surveys
- Unbounce/Carrd for landing page tests
- Airtable for tracking experiments
- Loom for quick user testing videos
Learning From Your Community
Last week, a founder in our community converted a major assumption about parent purchasing triggers into a hypothesis, tested it with a simple email campaign, and discovered his messaging had been focusing on the wrong benefits entirely.
This discovery boosted his conversion rate by 36%.
What assumptions are you ready to test?
Next week, we'll cover "The Minimum Viable Test: How to Validate Ideas in 5 Days or Less" with step-by-step templates you can use immediately.
Until then,
Alessandro Marianantoni
founder/director at M Accelerator (by MEDIARS)
P.S. If you want help designing your first experiment, join our Founders Studio session next Tuesday at 9 am PT where we'll workshop your hypotheses live.
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