A/B Testing: What It Is, How It Works, and How to Run Better Experiments
A/B testing is one of the most useful ways to improve a landing page, website, email, or ad without relying only on opinion.
Instead of making a change and hoping it performs better, you compare two versions against each other and let real user behaviour show which one is more effective.
That makes A/B testing an important part of conversion rate optimization. It helps teams reduce risk, understand what influences customer decisions, and improve performance over time.
In this guide, we explain what A/B testing is, how it works, what you can test, and how to run experiments that produce more reliable results.
What Is A/B Testing?
A/B testing, also called split testing, is an experiment where two versions of the same asset are shown to different groups of users.
Version A is usually the original or control, while Version B contains the change you want to test. The goal is to measure which version performs better against a specific outcome, such as:
- Form submissions
- Purchases
- Click-through rate
- Booked calls
- Add-to-cart actions
- Email sign-ups
Unbounce describes A/B testing as a way to compare variants and use actual customer behaviour to determine which performs better.
How Does A/B Testing Work?
In a standard A/B test, traffic is divided between two variants.
For example, 50% of visitors may see Version A and the other 50% may see Version B.
The important point is that visitors are assigned consistently. A returning visitor should normally continue seeing the same variant rather than switching between versions during the test.
This protects the integrity of the experiment and helps make the comparison more reliable.
What Is a Control and a Variant?
The control is the existing version of the page or asset.
The variant is the modified version you are testing against it.
For example, your current landing page may use the headline:
“Grow Your Business With Better Marketing.”
Your variant could use:
“Generate More Qualified Leads With Conversion-Focused Campaigns.”
If everything else remains the same, the test can help you understand whether the new headline influences conversion behaviour.
Why Should You Run A/B Tests?
The main benefit of A/B testing is that it replaces guesswork with evidence.
Marketing teams often have several opinions about which headline, layout, image, or CTA will perform better. Testing allows those ideas to be evaluated using actual user behaviour.
It also reduces the risk of rolling out a change that damages performance. Instead of replacing your current page completely, you can test the new idea against the existing version first.
If the new version performs worse, you have learned something without exposing all of your traffic to the change.
A/B Testing Can Improve Conversion Rates
Conversion rate optimization is one of the most common reasons businesses run A/B tests. Even a relatively small improvement can become valuable when a page receives significant traffic.
For example, if a landing page generates 1,000 leads per month, improving the conversion rate by a few percentage points can produce additional leads without increasing advertising spend. (See our lead generation strategies for more ways to grow qualified leads.)
That is one reason CRO focuses so heavily on improving the experience after traffic arrives.
A/B Testing Can Improve ROI
Most businesses spend money to generate traffic.
That traffic may come from Google Ads, Meta Ads, SEO, email, social media, or affiliate campaigns.
If an A/B test improves the percentage of visitors who convert, the same traffic can produce more business value. This can improve overall return on marketing investment without needing to increase traffic volume.
A/B Testing Helps You Understand Customers
Not every experiment needs to produce a winner to be useful. A failed test can still reveal something about customer behaviour.
For example, you may discover that a shorter form does not improve lead volume, but moving the form higher on mobile does.
Or you may learn that visitors respond more strongly to a specific benefit than to your general brand message. Over time, these insights can improve your wider marketing strategy.
What Can You A/B Test?
Almost any important customer-facing element can be tested. On a landing page, this might include:
- Headlines
- Subheadings
- CTA text
- Button placement
- Form length
- Hero images
- Testimonials
- Social proof
- Page layout
- Pricing presentation
- Trust elements
- Offers
You can also test ads, email subject lines, ecommerce product pages, checkout flows, and other conversion-focused experiences.
The best test ideas usually come from actual user problems rather than random design preferences.
Start With a Clear Hypothesis
A strong A/B test begins with a hypothesis. A hypothesis explains what you believe will happen and why.
For example:
“Reducing the lead form from eight fields to four will increase submissions because users will perceive the form as requiring less effort.”
This is stronger than simply saying:
“Let’s test a shorter form.”
A good hypothesis gives the experiment a clear reason and makes the result easier to interpret.
Use Data to Find Test Ideas
Do not choose test ideas only because someone on the team prefers a different design.
Look at available evidence.
Useful sources can include:
- Google Analytics
- Heatmaps
- Session recordings
- Form analytics
- User surveys
- Customer interviews
- Sales feedback
- Search queries
- Customer support questions
For example, if users repeatedly abandon a long form, form length may be worth testing. If users click a non-clickable element, that may reveal a usability issue. Research should guide experimentation.
Prioritize High-Impact Tests
Not every page element deserves the same level of attention.
Testing a small button colour may produce less value than testing the offer, headline, form, pricing model, or page structure.
Focus first on elements that can realistically influence a customer’s decision. A strong value proposition test can change how users understand the entire offer. A minor visual adjustment may have much less impact.
The more meaningful the change, the more useful the experiment can become.
A/B Testing vs A/B/n Testing
A/B testing usually compares two variants.
A/B/n testing compares three or more versions at the same time. For example, you might test three different headlines instead of only two.
This can be useful when you have several strong ideas, but every additional variant divides your traffic.
That means you may need more visitors and more conversions before the results become reliable.
A/B Testing vs Multivariate Testing
Multivariate testing evaluates several changing elements simultaneously.
For example, you might test different combinations of headlines, images, forms, and CTA buttons.
This can reveal interactions between page elements, but it usually requires much more traffic.
For many businesses, especially those with limited traffic, simple A/B tests are easier to interpret and more practical to run.
Choose the Right Conversion Metric
Every test should have a primary metric. (Our guide to CRO metrics and KPIs can help you choose.)
- For a landing page, that might be form submissions.
- For ecommerce, it might be purchases.
- For an email campaign, it could be a click-through rate.
Avoid changing the success metric after seeing the results.
If you begin the test trying to improve purchases but later declare a winner because one variant generated more clicks, the experiment becomes less reliable. Define success before the test starts.
Split Traffic Fairly
For a simple new A/B test, a 50/50 traffic split is common. Each version receives a similar opportunity to perform.
However, there may be cases where businesses send less traffic to a new challenger, particularly when the current version is already established and the new idea carries more risk.
Unbounce allows manual traffic allocation between variants, but equal weighting is often the simplest approach when you are starting from a neutral position.
Do Not Stop the Test Too Early
One of the most common A/B testing mistakes is declaring a winner too soon. A variant may appear to perform significantly better during the first few days, then move closer to the control as more data arrives.
Traffic can also behave differently across weekdays, weekends, campaign periods, or buying cycles.
Unbounce specifically warns against relying on confidence alone as a stopping rule. Sample size and representative business cycles also matter when deciding whether a test is complete.
Understand Statistical Significance
Statistical significance helps estimate whether the observed difference between variants is likely to be real rather than random variation.
Many testing platforms calculate confidence automatically. However, a high confidence percentage does not necessarily mean the test is ready to stop.
A small sample can produce misleading results.
You still need enough traffic, conversions, and time for the experiment to represent normal user behaviour. Statistical significance should be used as part of the decision, not as the only decision.
Avoid Testing Too Many Changes at Once
Suppose Version B has a new headline, new image, shorter form, different CTA, and completely redesigned layout.
If it performs better, which change caused the improvement?
You may not know. For learning-focused experimentation, test one main concept at a time.
Larger redesign tests can still be useful, but treat them as broader experiments rather than expecting them to explain which individual element made the difference.
Keep Traffic Sources Consistent
The type of traffic entering the test can influence results.
Visitors from Google Search may behave differently from users arriving from Instagram or cold Meta Ads.
If one variant receives mostly high-intent traffic and another receives low-intent traffic, the comparison becomes less meaningful.
Randomized testing tools help distribute traffic fairly. You should also monitor major campaign or targeting changes while the test is running.
Test the Entire Customer Journey
A landing page conversion does not always represent the final business result. Imagine Version B generates 30% more leads.
That sounds successful. But if those additional leads are poor quality and rarely become customers, the test may not actually improve the business.
Whenever possible, connect experiments with downstream metrics such as:
- Qualified leads
- Booked calls
- Sales
- Revenue
- Customer acquisition cost
- Average order value
The closer the metric is to business value, the more useful the experiment becomes.
Document Your Experiments
Keep a record of every test.
Include the hypothesis, screenshots, start and end dates, traffic allocation, primary metric, result, and what you learned.
Over time, this becomes an experimentation library. It prevents teams from repeating failed ideas and helps identify patterns about what customers respond to.
A strong CRO program builds knowledge, not just winning pages.
Common A/B Testing Mistakes
Several mistakes can make test results unreliable.
Common problems include stopping tests too early, running too many variants with limited traffic, changing the page during the experiment, testing without a clear hypothesis, and focusing on insignificant elements.
Another common problem is testing when traffic is simply too low. If a page receives only a few hundred visitors per month, it may take a long time to detect meaningful differences.
In that situation, qualitative research and larger strategic changes may provide more value than constant small experiments.
Frequently Asked Questions About A/B Testing
What Does A/B Testing Mean?
A/B testing compares two versions of a page, ad, email, or other asset to determine which performs better against a defined goal.
What Is an Example of A/B Testing?
A business might show half of its landing page visitors a short form and the other half a longer form. It can then compare which version generates more qualified leads.
How Long Should an A/B Test Run?
There is no universal duration. The test should run long enough to gather sufficient traffic and conversions while covering representative customer behaviour and business cycles.
Is A/B Testing the Same as Split Testing?
The terms are often used interchangeably. Some marketers use “split testing” for larger page changes, but both generally describe comparing different versions against one another.
What Is a Good A/B Test Result?
A useful result is one that provides reliable evidence about your hypothesis. That could be a clear winner, no meaningful difference, or even a losing variant that teaches you something important about customer behaviour.
Final Thoughts on A/B Testing
A/B testing is most valuable when it becomes a structured learning process rather than a collection of random experiments.
Start with customer research, identify a meaningful problem, create a clear hypothesis, and choose a metric connected to business value. Then run the test long enough to collect reliable data.
Some experiments will win and others will fail. Both outcomes can be useful if they help you understand your audience and make the next decision with more evidence than the last one.