A B testing is a controlled experiment where you compare two variants of a message or experience to see which performs better. In email, A B testing can be used to test subject lines, send times, content layout, CTAs, and personalization. A B testing helps teams make decisions based on measured outcomes rather than intuition.
What You Can Test in Email Programs
Email testing can target both copy and structure. Many teams run tests inside an email campaign to improve opens and clicks. Tests can also focus on engagement behavior captured through click tracking. For example, you might test two CTA button labels, or two different hero layouts, and measure downstream conversions rather than only click rate.
Designing Tests That Produce Meaningful Results
Good tests require a clear hypothesis, a single primary metric, and enough volume to detect a meaningful difference. Avoid testing too many changes at once because you will not know what caused the result. Define the evaluation window and ensure that segments are randomized. Testing should also consider operational stability. Pair experiments with monitoring so you can detect if a variant increases error rates, slows template rendering, or triggers deliverability problems.
A B Testing in Automation and Workflows
A B testing is not limited to campaigns. You can test onboarding sequences, lifecycle messaging, and transactional variants by integrating tests into an email workflow. Be careful with automation experiments because user state and timing can introduce bias. Keep variant assignment consistent, track cohort behavior over time, and avoid changing the experiment mid-flight unless you have a strong reason.
A B Testing and Topol
Topol supports structured email template workflows that make it easier to create controlled variants and keep output predictable, helping teams run cleaner A B tests without introducing rendering differences that skew results. Learn more at Topol or sign up at Topol signup.