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Email Marketing Glossary.

Clear, no-fluff definitions of every email marketing term that matters—from open rates to double opt-in. 

 

A/B Testing

A/B testing (or split testing) is the practice of sending two versions of an email — differing in one element — to see which performs better, so decisions are based on data rather than guesswork.

Also known as: split testing, A/B test

A/B testing replaces "I think" with "we know." By changing one variable and measuring the result, you steadily learn what actually works for your specific audience.

How does email A/B testing work?

You create two versions that differ in a single element — subject line, CTA, send time, image — send each to a portion of your list, and measure which drives more opens, clicks, or conversions.

What can you A/B test?

Subject lines, preheaders, sender names, CTAs, copy, images, layout, send time, and offers. Testing one variable at a time keeps results clear.

A/B testing best practices

Test one variable at a time, use a large enough sample for significance, define your success metric upfront, and apply what you learn to future sends.

Related terms

Send-Time Optimization Email Frequency Multivariate Testing

Related reading from Drip

14 Research-Backed Ways to Split Test Your Subject Line Email A/B Testing For Ecommerce Marketing Mastery

Frequently asked questions

What is A/B testing in email?
A/B testing (or split testing) is sending two versions of an email that differ in one element to see which performs better. It lets you base decisions on real data rather than guesswork.
How does email A/B testing work?
You create two versions differing in a single element — like the subject line or CTA — send each to a portion of your list, and measure which drives more opens, clicks, or conversions. The winner informs future sends.
What can I A/B test in an email?
Subject lines, preheaders, sender names, calls to action, copy, images, layout, send time, and offers. Testing one variable at a time keeps the results clear and actionable.
What are A/B testing best practices?
Test one variable at a time, use a sample large enough for statistical significance, decide your success metric before you start, and apply what you learn to improve future campaigns.
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