A/B Testing

To do this, you split your website audience into two equal groups—one sees version A, and the other sees version B.

After the experiment ends, you analyze which page drove more desired actions—like sign-ups, purchases, page views, etc.—and then use that version going forward.

A/B testing helps boost your website's conversion rate by revealing how changes in content, structure, and design influence user behavior. It also lets you test how your hypotheses influence metrics such as total order value, time spent on specific pages or sections, etc.

Additionally, A/B testing can help resolve internal debates and uncertainties about design and interface decisions by providing clear, data-driven insights into what actually works. With this information, you can optimize your website to ensure it's as easy and intuitive as possible for users to complete their goals.

To improve your website's performance, start by testing hypotheses related to elements that directly affect conversions, such as the main message in the hero section, CTA (call-to-action) buttons, forms, and images.

Before launching a test, take a moment to consider whether it's truly necessary right now, and whether you have the right conditions in place to run it effectively.

To get reliable results, you'll need a sufficient amount of traffic, consistent conversions (such as sign-ups, inquiries, or purchases), and a properly configured web analytics system.

If you're missing any of these, it's best to hold off—otherwise, the test is likely to deliver misleading results. But if everything's in place, you're good to go.

Before initiating testing, identify and clearly formulate the specific hypothesis you aim to validate. A precise hypothesis definition is critical for generating meaningful and actionable insights.

Your hypothesis must be grounded in data rather than assumptions. Base it on quantitative metrics and qualitative observations of user behavior on your website.

Ideally, your hypothesis should be based on the findings and results of thorough research.

• Talk to your users or customers to discover what they value most about your product or service, and what problems it helps them solve.
• Analyze support requests and tickets to identify common pain points. Review your website analytics to detect issues such as high bounce rates or unusually low conversion rates in specific sections.
• Use heatmap and session recording tools like Microsoft Clarity or Hotjar—they can show you exactly how visitors interact with your website and help uncover blockers or weak points in the user journey.

For example, according to your website analytics, visitors rarely click the "Buy Now" button. Maybe it's too small, blends into the background, or is located too far down the page to be easily noticed.

You might have several ideas to test, but it's best to focus on just one hypothesis per experiment. For example, when testing the button, run separate tests for size and color. If you test multiple changes at once, you won't know which one actually made a difference.

Once you've chosen the hypothesis to test, define the criteria to evaluate its success or failure.

These criteria might include: Bounce rate (the percentage of visitors who leave immediately after landing), total time spent on the website, number of leads or sign-ups, number of purchases, average order value, and other relevant metrics.

When choosing metrics, always focus on the core problem you're aiming to solve.

For example, if you sell an online fitness and wellness course and want to boost sales of a specific program by highlighting its section on the page with a different color, then your primary metric should be the sales of that particular program.

It doesn't matter how long visitors stayed on the page, how many inquiries were submitted, how many other programs were purchased, or the overall revenue — the focus should be solely on the sales increase for the highlighted offer.

After choosing metrics, make sure you have connected and properly set up a web analytics service to track all relevant data accurately.

Pick a single element to test that directly supports your hypothesis. For example, if you believe a red button will attract more attention and boost signups, then you should only test the button color, and not its size or font at the same time.

After choosing the element, create a duplicate of your page with the change implemented. Again, keep in mind the rule of thumb: One hypothesis, one change per test.

Estimate in advance how many visitors you'll need for your results to be statistically significant—this helps avoid mistaking random fluctuations for meaningful outcomes that can't be replicated or trusted.

Typically, the larger the expected effect—for example, an increase in conversion percentage—the fewer people you'll need to determine if it's effective.

You don't need to calculate it manually—there are tools for that, like Evan Miller's Sample Size Calculator.