Can I A/B test a campaign in Loops?

Loops supports campaign workflows and segmentation that let teams test email changes against real audiences and measure results beyond vanity opens.

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Email A/B testing: what to test and how to read results

Q: What is a good sample size for an email A/B test?

Use enough recipients that the expected lift can be detected. Small lists should test bigger changes and judge downstream clicks or conversions rather than tiny open-rate differences.

Q: How long should an email A/B test run?

Run the test long enough to cover the normal response window for that campaign, then avoid peeking and stopping early just because one variant is ahead.

Q: What should I A/B test first?

Start with high-impact variables such as offer, audience, subject line, preview text, call to action, and send timing before testing small design details.

A/B testing email means sending two versions to comparable groups, then keeping the one that performs better on a metric you chose in advance. Done well, it replaces opinion with evidence. Done carelessly, it produces confident numbers that mean nothing.

Resources

What to test

You can only learn something if the two versions differ by one thing. Pick from these, one at a time.

Subject line. The highest-impact test for opens. Try a clear, specific line against a more curious or playful one.
Sender name. A brand name versus a real person, like Chris at Loops, often shifts open rates more than teams expect.
Preview text. The snippet next to the subject in the inbox. It is prime real estate that many senders leave on autopilot.
Send time. Useful, but test it on its own and read results across several sends, since a single day can mislead.
Call to action. Button wording, one CTA versus several, and placement near the top versus the bottom.
Content and layout. Long form versus short, image heavy versus text, single column versus multi.

Test subject lines and sender name first. They gate everything downstream: if the email is not opened, nothing else in it can work.

How to run a test that tells you something

Change one variable. If you swap the subject and the CTA at once, a lift tells you nothing about which one caused it.
Pick the metric before you send. Read subject and sender tests on open rate, and CTA and content tests on clicks or the action you care about. Decide first so you are not tempted to cherry-pick.
Give it enough recipients. As a rough floor, aim for several hundred per variant before a difference is worth trusting, and more when the gap is small.
Let it run long enough. Opens and clicks trickle in for a day or two. Calling a winner an hour after sending rewards whoever is at their desk, not the better email.
Repeat the ones that matter. A single test is a data point. When a finding holds across two or three sends, it becomes a rule.

Mistakes that void your results

Calling it early. The most common error. The early lead often flips once the slower half of your list opens.
Too few recipients. A four percent gap on 80 people is noise. The same gap on 8,000 is a signal.
Testing more than one thing. Two changes, one result, no conclusion.
Measuring the wrong metric. A subject that wins opens but loses clicks is not a winner if clicks are what you sell.
Ignoring deliverability. If one variant lands in spam, you are testing the inbox, not the copy.

Deliverability underpins all of this, so fix authentication and list hygiene first. See email deliverability best practices and why emails go to spam.

How to A/B test in Loops

In Loops, A/B testing lives inside Workflows, using an Experiment node.

Build a workflow and add an Experiment node where you want the split.
Create the variants on each branch, for example two different emails or two subject lines.
Loops splits incoming contacts between the branches so each variant reaches a comparable group.
Read the result on the workflow goal, then route everyone to the winning branch.

Because the test runs inside a live workflow, you measure on real triggered sends rather than a one-off blast, which is the repeatable setup that makes a result trustworthy.