How to Do A/B Testing on Your Website

A/B testing is the practice of showing two versions of a webpage to different visitors at the same time and measuring which one leads to more of a desired outcome — a form submission, a purchase, a click. One version (A) is your current page; the other (B) is a variation with a single change. By comparing the results, you can make decisions based on evidence rather than opinion.

Opinions about what works on a website are plentiful and often wrong. Designers, business owners, and marketing teams routinely disagree about whether a red button outperforms a green one, whether long copy converts better than short, or whether showing price upfront increases or decreases enquiries. A/B testing resolves these arguments with data from real visitors behaving naturally.

What to Test First

The highest-impact elements to test are those that directly influence a visitor’s decision to convert. Your headline is the single most important thing on a landing page — it is what most people read before deciding whether to continue. Testing a different headline can produce dramatic changes in conversion rate. Other high-value test elements include your call-to-action button (text, colour, and placement), your hero image, the length and format of your enquiry form, and your main value proposition statement.

Avoid testing trivial changes — testing whether to use bold text on one sentence, for example, is unlikely to produce a meaningful difference and wastes your traffic. Prioritise tests based on how much traffic the page receives (more traffic means faster results), how important the page is to your conversion funnel, and how big the potential impact of the change might be.

Setting Up and Running a Test

You need an A/B testing tool to run tests properly. Options range from free (Microsoft Clarity has some experiment features; Google's Optimize has been deprecated but alternatives exist) to mid-range (VWO, AB Tasty, Convert) and enterprise (Optimizely). Many tools allow you to create a variation without touching your website code, using a visual editor to change text, swap images, or rearrange elements.

Before launching a test, decide on your primary metric — the number you are trying to move. Set up the test to run until you reach statistical significance, which typically means at least 95 per cent confidence that the difference between A and B is real and not due to random variation. Most testing tools calculate this for you and tell you when the test has reached significance. Stopping a test early because one version appears to be winning is one of the most common mistakes in CRO — results often flip as more data comes in.

Reading and Acting on Results

When a test reaches statistical significance with a clear winner, implement the winning variation as your new default. Document what you tested, what the result was, and by how much the metric changed. This record becomes your knowledge base over time — you stop re-testing things you already know and build on previous insights.

Not every test produces a winner. A result of "no statistically significant difference" is still useful — it tells you the change did not matter, and you can move on. A losing variation is also valuable: understanding why something did not work often sparks the hypothesis for the next, better test. A/B testing is most powerful when it is treated as an ongoing programme rather than a one-off exercise.