A/B Testing: What It Is, Benefits, and How to Do It

A/B testing is a method of comparing two versions of a webpage, email, or app to see which one performs better with real users. Instead of guessing which headline or button color will work best, you show each version to a separate group and let the data decide.

In this blog, we will show you what A/B testing is, the types you’ll run into, its main benefits, and a step-by-step process you can use for your next test.

What is A/B testing?

A/B testing is a controlled experiment that shows two versions of something to different groups of users to see which one drives better results.

Here’s how the mechanics work:

• Control (Version A): The current, live version already in use.
• Variant (Version B): The new version being tested against it.
• Random split: Visitors are divided randomly between the two versions so neither group is skewed toward a particular type of user.
• Success metric: A specific, measurable goal, such as clicks, sign-ups, or purchases, decides which version wins.

A/B testing is also called split testing or bucket testing. The terms are used interchangeably across most marketing and product teams, though split testing sometimes refers specifically to sending traffic to two entirely separate URLs rather than two versions of the same page.

Marketers, product managers, and UX researchers all rely on A/B testing because it replaces opinion with evidence. Instead of debating which idea is better in a meeting, you run both and measure what actually happens.

Here’s a simple example. An online retailer wants to know if changing the checkout button text from “Complete Order” to “Get My Order” increases purchases. Half of the site’s visitors see version A, the original button text. The other half see version B, the new wording. After two weeks, the retailer compares completed purchases between the two groups to see which button text actually performed better, rather than guessing based on internal preference.

What are the types of A/B testing?

A/B testing includes three main approaches: classic A/B testing, split URL testing, and multivariate testing. Each differs in what changes and how much traffic it needs.

Classic A/B testing

Classic A/B testing compares two versions of the same page with one or two elements changed, like a headline or a call-to-action button. Everything else on the page stays identical, so any difference in performance can be traced directly to that one change.

This is the most common type of test, and the easiest to set up. A retailer testing two different subject lines for the same email campaign is running a classic A/B test.

Split URL testing

Split URL testing compares two completely separate page designs hosted at different URLs. It’s useful when a redesign is too different from the original to build as a simple variant of the same page, such as a full landing page overhaul.

Traffic is split between the two URLs the same way it would be for a single-page test. The tradeoff is more setup work, since two full pages need to be built and maintained instead of one page with a small tweak.

Multivariate testing

Multivariate testing changes several elements at once and measures every possible combination, such as testing three headlines against two images at the same time. This reveals not just which single element performs best, but how elements interact with each other.

Multivariate testing requires significantly more traffic than the other two types, since the total number of combinations grows quickly with each added element. Sites without high traffic volumes often see multivariate tests take too long to reach a reliable conclusion.

Here’s a quick side-by-side comparison of the three:

Most teams start with classic A/B testing because it needs less traffic and gives a clear, direct answer about what worked.

What are the benefits of A/B testing?

A/B testing helps teams make website and product decisions based on real user behavior instead of internal opinions.

• Continuous improvement: Teams can test one change at a time, measure the effect, and refine headlines, images, forms, and page layout over time. Small, consistent gains compound into significant improvements over a year of testing.
• Fewer visitor pain points: Combined with tools like heat maps and analytics, A/B testing helps identify where users get confused or stuck, then confirms whether a fix actually works, rather than assuming a redesign helped.
• Lower cart abandonment: E-commerce teams can test checkout steps, shipping cost displays, and product imagery to find what keeps shoppers from leaving mid-purchase. Even small checkout friction, like an unexpected shipping fee, can be isolated and tested directly.
• Higher conversion rates: Testing call-to-action placement, wording, and page layout systematically improves how many visitors complete a goal, whether that’s a purchase, a form submission, or a sign-up.
• Better return on existing traffic: Since traffic acquisition is expensive, small tested improvements increase conversions without spending more on new visitors, making testing one of the most valuable activities a marketing team can invest in.

Each of these benefits compounds over time. A single test rarely transforms a business, but a consistent testing habit does.

How do you conduct an A/B test?

Running an A/B test takes six main steps, from identifying what to test to analyzing the results.

1. Collect data
   Use analytics, heat maps, or session recordings to find high-traffic pages with room to improve, like high bounce rates or low conversion.

2. Choose one variable to test
   Isolate a single element, such as a headline or button placement, so any change in results can be traced back to that one thing.

3. Set a clear goal and audience
   Decide what you’re measuring, like clicks or completed purchases, and make sure your test groups are split evenly and randomly.

4. Build your hypothesis
   State what you expect to happen and why, based on your data, not a guess.

5. Create your variant
   Build the new version using your A/B testing software or visual editor, and quality-check it before launch.

6. Run the test and analyze results
   Let the test run long enough to reach statistical significance, then compare performance between the control and the variant.

Randomization matters at every step of this process. If your test groups aren’t split randomly, outside factors like traffic source or time of day can skew your results before you even start measuring the variable you care about.

Ending a test early is one of the most common mistakes teams make. A variant might look like it’s winning after two days, but small sample sizes produce noisy results that can flip within a week. Waiting until your test reaches statistical significance, meaning the difference between versions is unlikely to be due to random chance, protects you from acting on a false signal.

A worked example

Here’s how the six steps play out in practice. A SaaS company notices its pricing page has a high bounce rate. The team isolates the monthly-versus-annual toggle as the variable to test, sets “clicked Start Trial” as the goal, and hypothesizes that defaulting the toggle to annual billing will increase trial starts because it surfaces the lower effective price first. They build the variant, split traffic evenly for three weeks, and find the annual-default version increased trial starts by 9 percent, a large enough gap to be statistically significant.

What should you test first?

Not every element is worth testing. The elements most likely to move the needle are the ones tied directly to your main conversion goal.

High-impact elements to prioritize:

• Page layout and content hierarchy
• Call-to-action wording and placement
• Checkout flow and form length
• Headlines and value propositions

Lower-impact elements to deprioritize:

• Button color alone, without a wording or placement change
• Minor font adjustments
• Small spacing tweaks with no clear hypothesis behind them

Testing the color of a button in isolation rarely produces a meaningful result. Testing a full call-to-action, including its wording and placement, is far more likely to move your conversion rate in a measurable way.

Consider a software company whose free trial sign-up rate has stalled. Testing the sign-up button’s color alone is unlikely to change much, since color rarely affects a visitor’s decision to commit to a trial. Testing the surrounding value proposition, such as adding “No credit card required” next to the button, addresses an actual objection and is far more likely to move the sign-up rate.

Getting more value from your test results

A/B testing only works when a test actually reaches a valid conclusion, and that happens less often than most teams expect. Research published in Harvard Business Review found that at companies like Google and Bing, only 10 to 20 percent of controlled experiments generate a positive result. That’s a normal part of the process, not a sign that testing isn’t working.

Treating every test as a learning opportunity, win or lose, is what makes a testing program valuable over time. Platforms like QuestionPro support this by making it straightforward to build test variants, randomly assign respondents, and export results for deeper statistical analysis. Pairing A/B tests with broader customer feedback and surveys gives you both the what and the why behind user behavior. For sites with enough traffic to support deeper analysis, tools like ANOVA testing and other quantitative data analysis software help confirm whether your results are statistically real. Combining these with dedicated website optimization tools rounds out a complete testing workflow.

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