A/B Testing (Email)
The practice of sending two or more variations of an email to different segments of your audience to determine which version performs better. The scientific method applied to email marketing.
A/B Testing Turns Email From Art Into Science
Most email marketers guess what works. A/B testing proves what works. Over time, dozens of small tests compound into dramatically better performance — higher open rates, more replies, more conversions. The companies with the best email metrics are not more creative. They are more disciplined about testing.
What to Test First
Start with subject lines — they have the biggest impact on whether anyone reads your email at all. Then test opening lines, CTAs, and email length. Save design and layout tests for last — they matter less than copy in B2B email.
Running a Proper Test
Change one variable at a time. If you change the subject line AND the CTA, you do not know which change affected performance. Send variations simultaneously to avoid time-of-day bias. Wait for statistical significance before declaring a winner — a 2% difference on 100 sends is not meaningful.
Applying Results
When a test produces a clear winner, apply the learning across all future emails. Document what you learned. Over 12 months of weekly tests, you build a playbook of proven subject line patterns, CTA formats, and message structures specific to your audience. That playbook is a competitive advantage.
Frequently Asked Questions
What should you A/B test in emails?
Test one variable at a time: subject line (biggest impact on open rate), opening line (biggest impact on read-through), CTA (biggest impact on click-through), send time (day and hour), email length, personalization level, and proof elements (stats vs case studies vs testimonials).
How large should your A/B test sample be?
For statistically significant results, each variation needs at least 200-300 recipients for open rate tests and 500-1,000+ for click rate tests. Smaller samples produce unreliable results. If your list is under 1,000, test across multiple sends rather than splitting one send.
