A/B Testing
An experimental method in which two variants (A and B) of a webpage, app or feature are tested in parallel to determine the better version based on data.
A/B testing is one of the most effective methods for improving digital products based on data. Instead of relying on gut feeling or opinions, controlled experiments deliver hard facts about which variant performs better. Whether conversion rate, click-through rate or user satisfaction – A/B tests make the difference measurable. Companies like Google, Amazon and Booking.com run thousands of A/B tests every year to continuously optimize their products.
What is A/B Testing?
A/B testing (also called split testing) is an experimental method in which two variants of a digital element are served in parallel to different user groups. Variant A is the control group (current version), variant B is the test variant with a targeted change. Statistical evaluation of the results (e.g. conversion rate, dwell time, bounce rate) determines which version performs significantly better. Statistical significance is crucial: only when enough data points have been collected and the result is confirmed with at least 95% confidence is the test considered meaningful. Multivariate tests (MVT) extend the principle to multiple simultaneous changes.
How does A/B Testing work?
First a hypothesis is formulated, e.g. 'A green CTA button increases conversion rate by 10%'. Then two variants are created: A (red button) and B (green button). A testing tool splits incoming traffic randomly and evenly between both variants. During the test run, the defined KPIs are measured for both groups. After reaching the required sample size, the result is evaluated statistically. If variant B shows a significant improvement, it is rolled out to all users.
Practical Examples
An online shop tests two different product page layouts: large product image on the left vs. image gallery on top – and measures the impact on add-to-cart rate.
A SaaS company compares two pricing page variants: monthly vs. annual price display as default – and identifies the version with higher conversion.
A news platform tests different headlines for the same article and measures which variant generates more clicks and longer reading time.
A financial services provider tests two different onboarding flows for account opening and measures completion rate in both variants.
An e-learning platform compares video tutorials vs. interactive step-by-step guides as onboarding for new users.
Typical Use Cases
Conversion optimization: Systematically improve landing pages, CTA buttons, forms and checkout processes
UX design validation: Test new design drafts with real users before full rollout
Email marketing: Optimize subject lines, send times and content for higher open and click rates
Feature rollout: Test new features gradually on user segments and measure impact on metrics
Pricing: Test different price models and discount strategies to maximize revenue
Advantages and Disadvantages
Advantages
- Data-driven decisions instead of gut feeling – significantly reduces the risk of wrong decisions
- Measurable results with statistical significance provide a solid basis for optimization
- Fast iteration: Small changes can be tested and validated in a short time
- Low risk: Changes are only served to a subset of users before going global
- Cumulative improvement: Many small, tested optimizations add up to major gains over time
Disadvantages
- Requires sufficient traffic – with few visitors it takes weeks or months for significant results
- Only one variable per test: Multivariate tests are much more complex to set up and evaluate
- Risk of misinterpretation with too short test duration or faulty statistical evaluation
- Not suitable for fundamental strategic decisions or completely new product concepts
Frequently Asked Questions about A/B Testing
How long should an A/B test run?
Which tools are suitable for A/B testing?
What is the difference between A/B testing and multivariate testing?
Related Terms
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