How to Do AB Testing on Your Website: With 3 Easy Examples
Wondering how to do AB testing on website, why you need it, and what the benefits of AB testing are? You'll find out in this article. But first, let's see these two CTA buttons. Which one do you…
Wondering how to do AB testing on website, why you need it, and what the benefits of AB testing are? You'll find out in this article. But first, let's see these two CTA buttons.
Which one do you think will work better on your website? You can't just make a personal guess or assumption, though, you need to experiment with these two buttons. This experiment is called A/B testing.
What is A/B testing on website?
A/B testing or split testing is an experiment that you can use in web design and marketing where you try different variations to see which one works best. You can test only one element of a webpage, multiple elements, or even the whole page.
It allows you to see which variation works best to help you achieve your goal: increasing click-through rates, conversions, sales, engagement, or any other measurable metric.
What are the benefits of A/B testing?
There are several benefits of AB testing on website, mainly by providing objective, quantitative data about the variations you test. This helps you make informed decisions instead of just making wild, likely biased guesses.
Here are some key benefits of A/B testing on website:
Optimized user experience
AB testing on website's design, layout, and content, you can identify which elements resonate best with your audience, leading to a more user-friendly and engaging experience.
Increased conversions
Better UX leads to higher conversion rates: more sign-ups, sales, downloads, or other desired actions. Among all the benefits of AB testing, this one might produce the most tangible impact.
Reduced bounce rates
In addition to better conversions, split testing can help you identify and address factors that contribute to high bounce rates, keeping visitors on your site longer.
Better personalization
A/B testing can enable you to create personalized experiences for different segments of your audience, tailoring your website to match the preferences and behaviors of specific user groups.
Cost-effective
Both on website optimization and marketing campaigns, split tests allow you to allocate resources effectively by focusing on changes that have the most significant impact. This one might also be one of the best benefits of AB testing you can enjoy.
How to do AB testing on website
After discovering the benefits of AB testing for your website, let's explore the step-by-step guide.
Pick a single component you wish to test
You can choose to test the CTA button (like the example above), headline, image, page layout, or the whole page content. You do you.
How to do it
- Open Microsoft Clarity heatmaps and session recordings to find where visitors hesitate, rage-click, or drop off.
- In Google Analytics, sort pages by traffic and exit rate to surface the high-traffic pages worth testing first.
- Pick ONE element on that page (CTA, headline, hero, form) and write down why changing it should move a specific metric.
See a worked example
As an example, say your pricing page pulls the most traffic but a Microsoft Clarity heatmap shows most visitors never scroll to the CTA below the fold. That points you toward testing CTA placement rather than button color, because the placement is what is plausibly costing you sign-ups. This is illustrative, not a measured result.
Tools to use
Microsoft Clarity for free heatmaps and session recordings that show where visitors hesitate or drop off, and Google Analytics to find the high-traffic, high-exit pages worth testing first.
Steal our AI prompt
Here is behavior data for my [describe page, e.g. pricing page]: [paste heatmap notes, scroll depth, exit rate, and top-clicked elements]. Act as a CRO analyst. Rank the 3 page elements most likely to be costing me conversions, and for each write one testable hypothesis in the form "Changing X to Y will increase [metric] because [reason]."
Create two to three variations of it
We'd say two is the optimum number, then after you know which one works best, you can conduct a second test. You can do three variations at once in the first test if that makes more sense, but don't go beyond that because you'd spread it out too thin.
How to do it
- Write the hypothesis plainly: the change, the expected effect, and the single metric it should move.
- Design 2 variations (3 at most) that are genuinely different in angle, not cosmetic tweaks.
- Plug your baseline conversion rate and the smallest lift worth detecting into a sample-size calculator to learn how much traffic each variation needs.
- Build the variations in a visual editor like VWO so you are not editing production code by hand.
See a worked example
As an example, for a CTA test you might pit "Get my free audit" against "See my results", two different value framings, rather than "Submit" versus "Submit now", which is too small a change to reliably detect on modest traffic. Sizing the test first tells you whether your traffic can even measure a difference that small.
Tools to use
VWO to build A/B variations in a visual editor without touching production code, and Evan's Awesome A/B Tools to size the test from your baseline rate and minimum detectable effect before you build.
Steal our AI prompt
My control [element, e.g. CTA button] currently reads or does: [paste]. My hypothesis is: [paste hypothesis]. Generate 2 genuinely distinct variations (different angle, not cosmetic), each with the exact copy or design change and the reason it might beat the control. Keep the brand tone [describe]. Then list the one metric each variation should move.
Make alternate pages, test, and review
Create an alternate page for each variation and test each one on the same amount of visitors, from the same group or segment, and within the same period of time. Monitor the progress for up to three months, then you can do a second test if needed. If not, go ahead and stick to the best variation until you find the need to test and optimize again.
Try these three steps, and you'll be able to see the benefits of AB testing on your UX and business outcomes.
How to do it
- Split traffic evenly across variations from the same audience over the same window, and do not peek and stop early.
- Let it run to the sample size you calculated, often a few weeks and up to the three months noted above.
- Paste the visitor and conversion counts into a significance calculator to confirm the result is real, not noise.
- If no variation clears significance, keep the control and iterate on a fresh hypothesis instead of forcing a winner.
See a worked example
As an example, imagine variation B shows a 12% lift after two weeks. Dropping the visitor and conversion counts into a significance calculator might return only 78% confidence, below the 95% bar, a reminder to keep it running rather than shipping a result that could still flip. Numbers here are illustrative.
Tools to use
A/B Test Guide's calculator and Convert's significance calculator to confirm a result is real before you call a winner.
Steal our AI prompt
Here are my A/B test results. Control: [paste visitors] visitors, [paste conversions] conversions. Variation B: [paste visitors] visitors, [paste conversions] conversions. Explain whether the difference is statistically significant at 95% confidence, what the confidence interval implies, and whether I should call a winner, keep running, or stop. Do not overstate certainty.
Examples of A/B testing on website
Below are some basic examples of A/B testing on website, just to give you the idea. Feel free to experiment with more diverse variations and elements, and enjoy more benefits of AB testing.
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Hero banner. Alternative A has a slider for the hero banner, while alternative B uses a single static image.
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CTA button placement. Button A hovers on the top menu, Button B floats on the side, and option C has multiple buttons in between the content.
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Headline copy. The first variation uses title case on its headline, while the second one opts for sentence case.
You can't guess which version wins. You have to test it.
Design better, more personalized UX with A/B testing
The many benefits of AB testing empower you to make strategic decisions that lead to better user experiences, higher conversions, and improved business outcomes.
One honest caveat before you start: most A/B tests don't produce a clear winner, and many never reach statistical significance. When one analyst pooled 115 published A/B tests, the average lift was just under 4 percent, and most lacked the power to detect even that (Georgi Georgiev, Analytics-Toolkit, 2018). That is why testing works best inside a conversion system that decides what is worth testing in the first place, instead of testing at random.
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