A/B Testing Helps You Make Confident Decisions Based on Real Behavior
A/B testing is a way to compare two versions of something to see which one performs better. It is used most often in comparing what elements increase conversion or engagement on a webpage, email campaign, or app interface.
To run an A/B test, start with the current version of whatever you want to improve. This could be a webpage, subject line, or sponsored ad. That’s version A.
Then, create a second version (version B) where you change just one thing. The test does not work if you change multiple elements between version A and B, as you won’t be able to tell which element change caused which results.
Both versions are then shown to separate, randomly selected groups of users at the same time. By comparing how each group behaves, you can see which version gets better results based on number of clicks, signups, purchases, etc.
This testing method is used in marketing, product development, and improving user experience because it uses insight from actual user behavior to justify decisions.
Why A/B Testing Matters
Instead of guessing what “might” work best, A/B testing shows you exactly what users respond to so you can make decisions based on those results.
When you test your theories with real users, you get an idea of what is proven to work before making final decisions. This could be something small like adjusting a subject line, or something more prominent like changing the layout of a landing page. Based on the outcome of the A/B test, you’re able to confidently move forward with the version that performs better.
This is especially useful for teams that want to improve without slowing down operations. A/B tests can be performed without losing any momentum on the product or campaign you’re promoting.
Even if the improvements feel small at first, the cumulative impact of frequent and thoughtful tests leads to better performance across all channels.
How A/B Testing Works
A typical A/B test follows a simple structure:
- Start with a clear goal or question
- Choose one element to change (a button label, subject line, image, etc.)
- Create two versions: A (original) and B (with the single change)
- Split your audience randomly so each version gets a fair test
- Measure results based on one success metric
Keeping the test focused is important. If you change too many things at once, you won’t know which one made the difference. And if you measure too many outcomes at once, you risk losing track of what matters.
Even if it seems inefficient, the most useful A/B tests answer one question at a time and help you make one better decision.
How to Run an A/B Test Without Overthinking It
Running an A/B test doesn't have to be complicated or require a full shift in your daily operations. Most modern marketing and website platforms include tools that let you run simple tests with minimal setup.
If you're testing an email, platforms like HubSpot or Mailchimp allow you to test subject lines or content variations directly in the campaign builder. For websites or landing pages, tools like VWO, Optimizely, or Convert make it easy to create test versions and split traffic without needing to code.
Starting with a clear question is what sets a test up for success. Some examples of A/B test questions could be:
- Does a shorter subject line increase email opens?
- Does a different headline lead to more article views?
- Does changing the placement of a call-to-action button increase form submissions?
Once you know what element you are testing and have created your two versions, split your audience evenly and let the test run. Don’t change other elements during the test, and be sure to allow ample time before reviewing the results.
What’s a Good Sample Size for an A/B Test?
The more data gathered during an A/B test, the more accurate your results will be. If too few people see each version, you can’t be sure the results aren’t random.
For email campaigns, each version should go to at least a few hundred recipients. For landing pages or on-site tests, it’s best to wait until each version has received hundreds or even thousands of views.
If you don’t have much traffic, don’t waste time testing tiny details. Instead, focus on testing larger changes like page layout where even a smaller sample size can show significant results.
More data leads to stronger conclusions. But meaningful insights are still possible when the change being tested is significant enough to create a visible shift in behavior.
How Long Should an A/B Test Run?
Every A/B test needs to be given enough time to make sure the results are accurate and meaningful. Ending a test too early because you think you see which version is performing better can lead to false confidence.
Most tests should run for at least seven days, even if your traffic or email volume is high. This gives you time to account for natural fluctuations in user behavior. A test that runs only on weekdays might give you different results than one that spans a full week, including weekends.
If your test has already reached a large sample size and the performance gap stays consistent over time, that’s a good signal. But don’t rush! A better approach is to set a minimum sample size and a minimum run time before the test starts, then wait until both are met before making the final call.
Can You A/B Test Emails and Landing Pages at the Same Time?
Running multiple A/B tests at once is a great way to test many elements at once, but only if the tests are independent of each other.
For example, you might run a subject line test in your email and a headline test on the article that the email links to. Just make sure the message in version A of the email matches version A of the landing page, and so on.
If the tests are too closely related, it becomes hard to tell which one had more impact. Keep things simple and isolate your tests as much as possible to get accurate results.
What Tools Should You Use for A/B Testing?
If you're working on a website or landing page, platforms like Optimizely, VWO, or Convert make it easy to launch and manage A/B tests. These tools often come with visual editors so you can create variations without writing code.
The best tool for A/B testing is the one that already exists in the platform you use.
If you’re planning mass email campaigns, most marketing platforms already support native A/B testing. HubSpot, Mailchimp, Campaign Monitor, and Klaviyo all let you test subject lines, content blocks, or send times within the same email campaign.
For paid advertising, tools like Google Ads and Meta Ads include built-in support for split testing. You can compare different creative choices, headline text, or targeting criteria to see what gets the best response.
You don’t need to overthink the tools—just use the ones that make testing easier in your existing workflow.
How Do You Know If a Result Is Statistically Significant?
Statistical significance tells you whether the results of the change are reliable or random. Most testing tools will tell you when your test has reached significance, based on how many people saw each version and how big the performance gap is between version A and B.
If your testing tool doesn’t calculate significance for you, there are free calculators online to measure your results. Just enter how many people saw each version and how many completed the action you were hoping for like clicking a CTA, opening an email, etc.
But statistical significance doesn’t necessarily define success; a test result can be statistically valid and still not worth acting on in the long run. Before you roll out a change, ask whether the difference is big enough to matter to your overall goals. If the gain is small or the change will create problems at scale, it might not be worth implementing.
Use statistical significance to support better decisions, not to justify every minor tweak.
Need Help Testing the Right Elements?
FMK Agency helps teams test elements accurately to ensure meaningful improvement, based on actual user behavior. From helping you build the best possible A/B test, to using the results in improving lead generation efforts, we help you build the confidence to improve.
Let’s discover what your users respond to most.