A/B Testing

A/B testing applies the scientific method to marketing decisions through controlled experiments. Instead of guessing what works, you split traffic between two versions and let data determine the winner.

The Process

Form a hypothesis ("Version B will convert better") → Design experiment (split traffic 50/50) → Run until statistical significance reached → Analyze results → Implement winner → Form new hypothesis. This creates a continuous improvement cycle where every test builds your understanding of what resonates with your audience.

What You Can Test

Creative: headlines, images, CTAs, copy length, value propositions

Targeting: audience breadth, behavioral onchain targeting segments, recency windows, geographic focus

Landing pages: hero messaging, form length, trust signals, page structure

Campaign settings: CPA targets, budget pacing, dayparting

Design Principles

Test one variable at a time. Changing headline, button color, and copy simultaneously makes it impossible to identify what drove results.

Ensure adequate sample size: 100+ conversions per variation minimum, at least one week of runtime, 95% confidence threshold.

Run variations simultaneously. Sequential testing (Version A this week, B next week) introduces timing bias from market conditions, day-of-week effects, or external events.

Common Pitfalls

• Testing multiple variables creates analytical confusion
• Calling winners prematurely (random noise masquerades as signal)
• Ignoring practical significance (2% lift may not justify implementation complexity)
• Testing without hypotheses (randomness without learning)
• Failing to document results (institutional knowledge loss)