A/B Testing Your Emails: What to Test and How to Measure Results

Most email marketers think they know what works. They have opinions about subject line length, the best time to send, and whether emojis help or hurt. But opinions are not data. A/B testing replaces guesswork with evidence, and the teams that commit to systematic testing consistently outperform those that rely on intuition.

A/B testing, at its core, is simple: you create two versions of an email that differ in one specific element, send each version to a statistically significant subset of your audience, and measure which one performs better. The winning version is then sent to the rest of the list. The power of this approach compounds over time as each test builds on the learnings of the last.

What to Test First

Not all email elements have equal impact. Start with the variables that influence whether your email gets opened at all: the subject line and the sender name. Subject line testing is the highest-leverage activity in email optimization. Test length (short versus long), tone (formal versus casual), specificity (vague versus detailed), and structure (question versus statement). Even small improvements in open rates create multiplicative gains in downstream metrics.

Once you have optimized your subject lines, move to the body of the email. Test the opening line, the length of the message, the placement and wording of your call to action, and the use of social proof or urgency. In outreach emails, test personalization depth — does referencing a specific company initiative outperform a more general industry observation? These tests reveal what resonates with your specific audience, which may differ significantly from industry benchmarks.

Measuring What Matters

The most common mistake in email A/B testing is optimizing for the wrong metric. Open rates matter for subject line tests, but for body copy and CTA tests, you need to look at click-through rates and reply rates. For sales outreach, the ultimate metric is meetings booked or deals closed, not just engagement. Always define your success metric before running the test, not after.

Statistical significance is equally important. A test where version A got a 22% open rate and version B got a 23% open rate on a sample of 200 emails tells you almost nothing. You need a large enough sample size and a meaningful difference to draw reliable conclusions. As a rule of thumb, aim for at least 1,000 recipients per variant and a difference of at least 2-3 percentage points before declaring a winner.

Building a Testing Culture

The most successful email programs treat testing as a continuous process, not an occasional project. They maintain a testing backlog, run at least one test per week, and document their findings in a shared knowledge base. Over the course of a year, this cadence produces dozens of validated insights that collectively drive major performance improvements. XMagnet's built-in A/B testing tools make it easy to set up, run, and analyze tests without requiring a data science background, lowering the barrier to entry for teams of any size.

The bottom line is this: every email you send without testing is a missed opportunity to learn something about your audience. The data is there for the taking. You just have to be willing to look.