A newsletter editor was convinced that emoji in subject lines would boost open rates. They ran an A/B test: same email, one subject line with emoji, one without. The emoji version won by 15%. Victory! They added emoji to every subject line going forward.
Six months later, open rates had declined to below where they started. The novelty had worn off, and now every subject line looked the same. The test was valid; the conclusion was wrong. They'd optimized for a single test rather than long-term performance.
A/B testing is powerful but requires discipline. Done well, it drives continuous improvement. Done poorly, it generates misleading data and false confidence.
How email A/B testing works
The concept is simple: create two versions of an email that differ in one specific way. Send version A to a random subset of your list and version B to another random subset. Measure which performs better on your chosen metric.
The randomization is crucial. If you send version A to your most engaged subscribers and version B to everyone else, you're not testing the email—you're testing the audience. Random assignment ensures the only systematic difference between groups is the email variation.
Sample size matters for statistical validity. Testing on 100 subscribers might show version A winning 55% to 45%, but that difference could easily be random chance. Testing on 10,000 subscribers with the same percentages is much more likely to reflect a real difference.
Statistical significance tells you whether the observed difference is likely real or likely random. Most testing tools calculate this automatically. Don't declare a winner until you have statistical significance—typically 95% confidence.
What to test
Different email elements have different impact potential and testing complexity.
Subject lines are the most commonly tested element because they're easy to vary and have clear impact on open rates. Test length (short vs long), tone (urgent vs casual), personalization (name vs no name), questions vs statements, and specific word choices.
Send time affects when people see and engage with your email. Test different days of the week and times of day. But remember that optimal timing varies by audience—what works for B2B might not work for B2C.
Sender name and address influence whether emails get opened. Test company name vs personal name, different team members, or variations in how you present yourself.
Preview text (the snippet shown after the subject line) is often overlooked but affects open rates. Test different preview text approaches—extending the subject line, providing additional context, or creating curiosity.
Email content includes layout, copy length, imagery, and overall design. These are harder to test because changes are often interconnected, but content tests can reveal what resonates with your audience.
Call-to-action elements—button text, color, placement, number of CTAs—directly affect click rates. Test different CTA approaches to optimize for action.
Personalization beyond the subject line—personalized content blocks, product recommendations, dynamic sections—can significantly impact engagement but requires more sophisticated testing infrastructure.
Testing methodology
Good testing methodology ensures your results are valid and actionable.
Test one variable at a time. If you change the subject line AND the send time AND the CTA, you won't know which change caused any difference in results. Isolate variables to understand causation.
Define your success metric before testing. Are you optimizing for opens, clicks, conversions, or revenue? Different metrics might favor different variations. Know what you're optimizing for.
Calculate required sample size before starting. Online calculators can help—input your baseline conversion rate, the minimum difference you want to detect, and your desired confidence level. If you don't have enough subscribers for statistical significance, the test isn't worth running.
Run tests to completion. Don't peek at results and stop early when one version is "winning." Early results are often misleading. Let tests run until they reach statistical significance or your predetermined end date.
Document everything. Record what you tested, the hypothesis, the results, and what you learned. This institutional knowledge prevents repeating tests and enables building on past learnings.
Common testing mistakes
Several pitfalls undermine A/B testing effectiveness.
Insufficient sample size is the most common mistake. Testing on small segments produces results that look meaningful but are actually random noise. Either test on larger segments or accept that you can't A/B test effectively at your scale.
Testing too many things at once makes results uninterpretable. Multivariate testing (testing multiple variables simultaneously) is valid but requires much larger sample sizes and more sophisticated analysis.
Stopping tests early based on preliminary results leads to false conclusions. Statistical significance requires sufficient data. Early leads often reverse as more data comes in.
Ignoring practical significance alongside statistical significance is a subtle error. A test might show a statistically significant 0.5% improvement in open rates. That's real, but is it meaningful for your business? Focus on differences that actually matter.
Over-optimizing for one metric at the expense of others can backfire. Maximizing open rates with clickbait subject lines might hurt click rates and brand perception. Consider the full picture.
Generalizing from single tests is risky. One test showed emoji improved opens. That doesn't mean emoji always improve opens for all audiences in all contexts. Replicate important findings before making permanent changes.
Testing infrastructure
Effective A/B testing requires appropriate tools and processes.
Most email platforms include basic A/B testing features. You can typically test subject lines and send times with automatic winner selection. This covers the most common use cases.
Advanced testing might require additional tools. Testing content variations, personalization strategies, or complex multivariate designs might exceed your email platform's capabilities.
Statistical analysis tools help interpret results beyond simple winner/loser determinations. Understanding confidence intervals, effect sizes, and statistical power improves decision-making.
A testing calendar prevents conflicts and ensures systematic coverage. Plan what you'll test each month, ensuring you're continuously learning without running conflicting tests.
A hypothesis log tracks what you've tested and learned. Over time, this becomes a valuable knowledge base about what works for your audience.
Beyond simple A/B tests
As your testing program matures, consider more sophisticated approaches.
Multivariate testing tests multiple variables simultaneously, identifying not just which variations win but how variables interact. This requires larger sample sizes but can accelerate learning.
Bandit testing (or multi-armed bandit) dynamically allocates more traffic to winning variations during the test. This reduces the "cost" of testing by limiting exposure to losing variations, but makes statistical interpretation more complex.
Holdout groups measure the incremental impact of email overall. Send to most of your list but hold out a random subset. Compare conversion rates between groups to measure email's true lift.
Sequential testing allows for valid early stopping by adjusting statistical thresholds. This is more complex than fixed-sample testing but can reduce test duration when effects are large.
Personalization testing compares personalized content to generic content, or different personalization strategies to each other. This tests not just what content works but whether personalization itself adds value.
Building a testing culture
Sustainable testing requires organizational commitment.
Make testing routine, not exceptional. Every campaign is an opportunity to learn something. Build testing into your standard workflow rather than treating it as a special project.
Share results broadly. Testing insights benefit the whole organization. Regular reporting on test results builds support for the testing program and spreads learnings.
Accept that most tests won't show significant differences. That's not failure—it's learning that the variable you tested doesn't matter much. Focus testing efforts on variables likely to have meaningful impact.
Balance testing with execution. Testing everything all the time creates complexity and slows down your email program. Test strategically on high-impact elements while maintaining efficient execution on routine sends.
Frequently asked questions
How long should I run an A/B test?
Until you reach statistical significance or a predetermined maximum duration (often 24-48 hours for email). Don't stop early based on preliminary results. Use sample size calculators to estimate required duration before starting.
What's a good sample size for email A/B testing?
It depends on your baseline metrics and the effect size you want to detect. Generally, you need thousands of recipients per variation for reliable results. Use online calculators with your specific numbers.
Should I test on my whole list or a subset?
Common practice is to test on a subset (say 20% split between A and B), then send the winner to the remaining 80%. This limits exposure to the losing variation while still getting valid results.
How do I know if my test result is statistically significant?
Most email platforms calculate this automatically. If doing manual analysis, use a statistical significance calculator with your sample sizes and conversion rates. Aim for 95% confidence before declaring a winner.