What is A/B Testing?

A/B testing compares two versions of a page, email or ad by showing each to a similar audience, to learn which one performs better on a chosen goal.

Most marketing decisions start with opinions. The founder likes the blue button. The designer prefers a shorter headline. The sales team wants more text. Everyone is confident, and nobody really knows. A/B testing replaces that guessing with evidence.

In simple words, A/B testing (also called split testing) means showing two versions of something to similar groups of people at the same time, then measuring which one gets better results. Version A is usually the current one. Version B has one change.

How an A/B test works

Pick one goal, such as sign-ups, purchases or clicks.
Write a clear idea: "A headline that mentions free delivery will get more orders."
Create version B with that one change.
Split traffic randomly between A and B.
Run the test long enough to collect a reliable amount of data.
Choose the winner, apply it and plan the next test.

A worked example

Suppose an online course platform tests two headlines on its landing page. Version A gets 5,000 visitors and 150 sign-ups, a 3 percent conversion rate. Version B gets 5,000 visitors and 180 sign-ups, a 3.6 percent conversion rate. In real terms, that is 30 more sign-ups from the same number of visitors.

The difference is 0.6 percentage points. Relative to version A, that is a 20 percent improvement, because 0.6 ÷ 3 = 0.2. Before declaring a winner, the team checks that the result is statistically significant, which most testing tools calculate for you. A gap like this can still appear by chance, so the tool's verdict matters more than the headline number.

What you can test

Headlines and page copy
The call to action text, color or position
Form length
Images versus short videos
Pricing layout and offers
Email subject lines and send times
Ad creatives and descriptions

For emails, remember that open rate is now inflated by privacy features, so judge subject line tests by clicks or sales where you can. AI tools can now suggest dozens of headline or subject line ideas in seconds. That is handy, but each idea still needs real visitors to prove itself.

Rules that keep tests honest

Change one thing at a time, unless you have the traffic for more complex tests.
Do not stop early just because one version leads after two days.
Run full weeks so weekday and weekend behavior are both included.
Decide the goal before the test, not after you see the numbers.
Write down every result, including losing tests. A failed idea still teaches you something about your customers.

A/B testing also has limits. A small website with a few hundred visitors a month may need many months to reach a clear result. In that case, the better approach is to use customer feedback, recordings and expert reviews to make sensible improvements, and save formal tests for higher-traffic pages. A/B testing is one part of conversion rate optimization, not all of it.

Simple takeaway

A/B testing turns "I think" into "we know". Test one meaningful change, give it enough time, and let your customers' behavior make the decision.