What can you A/B test on your website?

You can A/B test almost any element on your website, from headlines and button colours to entire page layouts and checkout flows. The most effective tests focus on elements that directly influence whether a visitor takes a desired action, such as clicking, signing up, or buying. This article walks through the most common questions marketers have about website A/B testing, so you can run smarter experiments and make better decisions with your data.

Which website elements have the biggest impact when tested?

The website elements with the biggest impact on A/B test results are those that appear early in the user journey or directly precede a conversion action. Headlines, calls to action, hero images, form layouts, and pricing presentation consistently produce measurable differences because they influence the decision a visitor makes at a critical moment.

Here are the elements most likely to move the needle in a test:

• Headlines and subheadings — the first thing visitors read, and a major driver of whether they stay or leave
• Call-to-action buttons — including the text, colour, size, and placement
• Hero sections — the combination of image, headline, and primary CTA above the fold
• Form length and field order — fewer fields typically improve completion rates, but not always
• Pricing page layout — how plans are presented, what is highlighted, and where the CTA appears
• Social proof placement — testimonials, logos, or review counts near decision points
• Navigation structure — particularly on high-traffic pages where users frequently drop off

Elements buried deep in a low-traffic page will produce inconclusive results simply because there are not enough visitors to generate statistical significance. Always prioritise testing elements that sit on high-traffic pages and are close to the conversion point.

What’s the difference between A/B testing and multivariate testing?

A/B testing compares two versions of a single element, for example, two different headlines on the same page. Multivariate testing tests multiple elements simultaneously to find the best-performing combination. A/B testing is simpler and requires less traffic; multivariate testing reveals how elements interact with each other but demands significantly more visitors to reach valid conclusions.

If you are testing one change at a time, A/B testing is almost always the right choice. It is faster to set up, easier to interpret, and produces actionable results with a smaller audience. Multivariate testing makes sense when you have high traffic volumes and want to understand whether, for example, a specific headline paired with a specific image outperforms all other combinations.

For most marketing teams, A/B testing is the practical starting point. Multivariate testing is a more advanced technique best reserved for pages with thousands of daily visitors and a clear hypothesis about how multiple elements interact.

How do you choose what to A/B test first on your website?

Start with the page or element that has the most visitors and the most room for improvement. A useful framework is to look at your analytics and identify where visitors drop off, where conversion rates are lower than expected, or where users show signs of confusion, such as high exit rates on pages that should be engaging.

A practical prioritisation approach:

1. Identify high-traffic, high-value pages — your homepage, landing pages, and checkout flow are good starting points
2. Find the biggest drop-off points — use heatmaps or scroll data to understand where attention fades
3. Form a clear hypothesis — for example, “Changing the CTA from ‘Submit’ to ‘Get my free guide’ will increase form completions because it is more specific and benefit-led”
4. Estimate the potential impact — a small improvement on a high-traffic page is worth more than a large improvement on a page nobody visits
5. Test one variable at a time — so you know exactly what caused the result

Avoid testing for the sake of testing. Every experiment should start with a question and a reason to believe the change will make a difference.

How long should you run a website A/B test?

You should run an A/B test for at least one to two full business cycles, which is typically a minimum of two weeks, regardless of how quickly one variant appears to be winning. Ending a test too early is one of the most common mistakes in conversion optimisation, and it leads to false conclusions based on statistical noise rather than genuine differences in behaviour.

The right test duration depends on three factors:

• Your traffic volume — lower traffic means you need more time to collect enough data
• Your baseline conversion rate — the lower your current rate, the longer it takes to detect a meaningful change
• The size of the effect you are looking for — a 30% improvement is easier to detect than a 5% improvement

Most A/B testing tools will calculate the required sample size before you start. Use these calculators to set a realistic end date rather than checking results daily and stopping when something looks promising. Aim for at least 95% statistical confidence before declaring a winner.

Can you A/B test landing pages, checkout flows, and forms?

Yes, landing pages, checkout flows, and forms are among the most valuable areas to A/B test on a website. These are high-intent pages where visitors are actively considering an action, which means even small improvements can have a significant impact on conversion rates and revenue.

Each area has its own testing priorities:

• Landing pages — test the headline, hero image, value proposition, CTA text, and page length. Visitors arriving from a specific campaign or ad have a particular expectation, and aligning the page to that expectation is often the biggest lever
• Checkout flows — test the number of steps, the placement of trust signals, field labels, error messages, and whether a guest checkout option reduces abandonment
• Forms — test the number of fields, the order of questions, the button text, and whether adding or removing a privacy note affects completion rates

Checkout testing in particular can be sensitive because interruptions to the purchase process can frustrate users. Keep variants close to the control and make incremental changes rather than redesigning the entire flow at once.

What results should you look for after an A/B test?

After an A/B test, the primary result to look for is a statistically significant difference in your primary conversion metric, such as click-through rate, form completion rate, or purchase rate. Beyond that headline figure, look at secondary metrics to make sure the winning variant did not improve one measure while harming another.

A complete post-test review should include:

• Primary metric — did the variant outperform the control on the goal you set at the start?
• Statistical significance — is the result reliable enough to act on, ideally at 95% confidence or above?
• Secondary metrics — did bounce rate, time on page, or downstream conversions change in a way that supports or contradicts the primary result?
• Segment breakdowns — did the variant perform differently for mobile vs desktop users, new vs returning visitors, or different traffic sources?
• What you learned — even a losing variant teaches you something about your audience’s preferences

Document every test, including those where the control wins. Over time, this record becomes a valuable asset that guides future decisions and prevents your team from repeating experiments that have already been run.

What are the most common A/B testing mistakes on websites?

The most common A/B testing mistakes are ending tests too early, testing too many changes at once, and running tests without a clear hypothesis. These errors lead to unreliable conclusions that can send your optimisation efforts in the wrong direction.

Other frequent mistakes include:

• Ignoring statistical significance — acting on results before you have enough data to be confident they are real
• Testing low-traffic pages — you will never collect enough visitors to reach a valid conclusion
• Running too many tests simultaneously — if multiple tests overlap on the same page, results can contaminate each other
• Not accounting for seasonality — a test run during a sale period or a public holiday may not reflect normal user behaviour
• Changing the test mid-run — any adjustment after a test has started invalidates the data collected up to that point
• Treating a test result as permanent truth — audience behaviour changes over time, and a result that held in one period may not hold six months later

The discipline of A/B testing is as much about process as it is about technology. A well-run test with a simple hypothesis will teach you more than a rushed test with a complex setup.

How Spotler helps with A/B testing and website personalisation

We built A/B testing directly into our website personalisation tooling for B2B, so you can test and personalise in the same workflow rather than juggling separate platforms. With Spotler Website Personalisation, part of Spotler Activate, you can:

• Run A/B tests on personalised content blocks, overlays, and page sections to find out which variant works best for each audience segment
• Personalise the visitor experience based on company data, behaviour, and stage in the buying journey, so your tests reflect real audience differences rather than a single average
• Automatically align website content with your email campaigns, so visitors arriving from a campaign see a consistent message
• Build enriched visitor profiles in the background, feeding smarter segmentation into your email automation and other channels
• Use built-in templates and content blocks to set up tests quickly, without needing developer support

Everything connects seamlessly with our CDP, email marketing automation, and the wider Spotler Marketing Cloud for B2B. If you want to move beyond basic split testing and start delivering personalised experiences that are continuously optimised, speak to our team about what Spotler Website Personalisation can do for your website.