What is A/B Testing?
A/B testing is a process in which two variants - version A (control) and version B (variant) - are divided equally between different users as part of a digital experiment in order to statistically determine which variant performs better.
The aim of A/B testing is to check targeted changes under real conditions and objectively measure whether they actually have a positive effect.
Terms such as split testing or bucket testing are often used as synonyms. In essence, they describe the same principle.
These are the benefits of A/B testing for every company
Most digital decisions are made under uncertainty...
Which design converts better? Which message is more convincing? Which function reduces the bounce rate?
A/B testing provides reliable answers to precisely these questions with minimal risk.
The 5 biggest advantages of A/B testing:
Higher conversion rates, through continuous data-based optimization
Lower risk because changes are rolled out in a controlled manner
Better understanding of user behavior, because real user data is the basis for further development
Faster insights, because what really works better becomes significantly more visible
More efficient use of budget & resources because measures are based on demonstrable impact.
Companies that are prepared to experiment regularly create a clear advantage for themselves. Instead of relying on assumptions, they learn what really works through targeted changes.
One example:
If a medium-sized company manages to increase the conversion rate from 3 to 4 percent by optimizing the checkout process, with 50,000 monthly visitors and an average shopping cart value of 100 dollars, this results in an additional turnover of 50,000 dollars per month.
However, A/B testing not only provides opportunities, but also security:
In this way, A/B testing not only enables new ideas to be tested in a targeted manner, but also simultaneously safeguards all changes before they are rolled out widely.
Whether on the design, form, offer or purchase process: changes are tested under real conditions and only implemented if they prove themselves measurably.
This eliminates all uncertainties when making decisions, as every change is checked for its effect beforehand.
Specific examples of successful digital companies also show how A/B testing can work.
In 2009, Google tested a total of 41 variants of the blue tone for link texts in search ads. The aim was to increase the click rate. The difference between the colors was minimal, but the effect was measurable: according to internal estimates, the most successful variant generated around 200 million dollars in additional annual revenue.
Netflix and Booking.com also rely on consistent testing throughout. Booking runs several hundred A/B tests at the same time to validate even the smallest changes before rollout. Netflix experiments with covers, trailer lengths and the presentation of content in order to understand what really appeals to users and to increase dwell time and engagement.
Where is A/B testing used?
A/B testing is useful wherever user behavior can be measured digitally. It is particularly effective in areas where even small improvements can have a major impact on conversions, clicks and ultimately sales.
Typical areas of application are, for example
- Websites or landing pages:
Test lead forms, headlines, booking/registration processes, conversion funnels, call-to-actions, images, layouts with the aim of achieving more clicks, inquiries or conversions. - Online stores:
Optimize price display, checkout process, product placement or filter logic to reduce shopping cart abandonment and increase sales. - Newsletters & email marketing:
Test subject lines, sending times, content or call-to-actions to improve opening and click rates. - Performance Marketing & Ads:
Test different ad texts, images or landing pages, SEO measures to maximize the ROI of campaigns. - Product development & apps:
Optimize functions, menus or user guidance - measurable and risk-free before rollout. - SaaS platforms and dashboards:
Targeted testing of navigation, feature prompts or onboarding to increase usage and better activate users. - UX & Usability
Improve forms, simplify entry points or test new functions in order to measurably optimize usability, user guidance and the overall experience.
How is an A/B test implemented?
A successful A/B test does not follow a random principle. It is based on a clear goal, a well thought-out hypothesis, the creation of a variant and a clean setup. Everything follows a structured process, which we show below:
1. define and set a goal
An A/B test needs a clear goal. Depending on the context, the goal can look different: more clicks on a call-to-action, higher conversion on a landing page / product page or higher interaction with important content or forms.
It is crucial that the goal is not only meaningful, but also measurable and evaluable. This makes it clear later on where real progress has been made and whether a variant has really prevailed in a direct comparison.
2. form a hypothesis
The goal is followed by the hypothesis. It describes the assumption as to which specific change could lead to an improvement.
Ideally, a well-founded hypothesis is based on existing data, user feedback or established best practices and gives the test a clear direction.
Examples:
- If the CTA color is made more eye-catching, the click rate increases.
- If fewer form fields are requested, the conversion increases.
- More specifically: If the visitor is shown a clear before/after effect in the form of 2 images,
then the booking rate increases because the decision as to whether an additional product should be booked is made spontaneously and emotionally.
3. set up test
In the third step, the variant is created and tested against the original version. It is important that both versions only differ in one relevant element. In this way, the effect remains clearly measurable and assignable.
The A/B testing tool ensures that visitors are distributed evenly between the two versions. At the same time, the target and the events must be stored correctly in the web analysis tool so that all relevant data is recorded.
4. start and run the test
As soon as the variants are set up and all tracking points are working, the test can go live. From this moment on, the rule is: observe, but do not intervene. A common mistake is to stop tests too early because initial trends are emerging.
A meaningful test needs enough time and sufficient data. The exact duration depends on the traffic, the desired goal and the expected change. Reliable statements can only be made once a sufficiently large sample has been reached and the significance has been tested.
5. evaluate and implement results
In the end, it is the result that decides: which variant fulfills the previously defined goal better? It is not only the difference itself that counts, but above all whether it is statistically significant. A/B testing tools or external computers help to check the significance correctly.
If the test variant shows a clear advantage, it should be rolled out consistently.
If, on the other hand, the original performs significantly better, this confirms the previous approach and prevents unnecessary investment in a weaker solution.
If the test does not produce a clear winner, this is also a valuable result. It shows that the tested change had no relevant influence and provides information on where further optimization could be useful.
Each test provides insights that can be incorporated directly into the next optimization step. This creates a continuous improvement process that leads to more conversions and better decisions in the long term.
Types of A/B tests
Depending on the objective and setup, there are different types of test that vary in complexity and area of application. The most common is the classic A/B test, in which one element of the variant is specifically changed. You can find other types in the following overview:
Classic A/B test
When we talk about A/B testing, we usually mean precisely this type of test. A so-called control variant A (the original) is compared with a specifically optimized variant B. Both are played equally to measure which version performs statistically significantly better. Both are played out equally in order to measure which version performs statistically significantly better.
Ideal for quick findings with low risk. The big advantage: easy to set up, clear to evaluate and often with a clearly measurable effect.
Multivariate test
In a multivariate test, several elements are changed simultaneously and played out in different combinations - for example, three versions of a headline combined with two images. This results in several versions (for example A, B and C) that are tested in parallel.
The aim is to find out which combination works best. The test shows how different elements influence each other and which combination achieves the strongest effect.
The evaluation is significantly more complex than with a classic A/B test. More traffic is required to obtain reliable results. It is therefore important to check in advance whether there are enough visitors to be able to meaningfully evaluate all combinations.
Split URL test
In the split URL test, a classic A/B test is carried out, but using different URLs, such as testing.de/original and testing.de/variante. Users only see one of the variants at a time.
This method is particularly suitable for comprehensive layout or concept comparisons, for example for redesigns or alternative landing pages.
Technically, the test is usually implemented on the server side, as the user is directed to a variant before the page is loaded. Some testing tools also enable client-side implementation, but this can be problematic if there are major page differences.
A/A Test
In the A/A test, exactly the same variant is played twice. The aim is not optimization, but technical validation. Is the A/B testing tool set up correctly? Is the tracking working correctly? Is the traffic distribution even? Is the user behavior recorded correctly?
With an A/A test, potential sources of error can be identified at an early stage - before a real A/B test starts. It is particularly helpful when a new testing setup is being used for the first time or to ensure that the results are reliable later on.
Multi-Armed Bandit Test
In the multi-armed bandit test, the traffic is not distributed evenly across the variants. Instead, the distribution adapts dynamically. A learning logic recognizes which variant performs better and gradually directs more users to this version.
This saves time and uses existing traffic more efficiently. Compared to the classic A/B test, this approach delivers reliable results more quickly without having to wait a long time for statistical significance.
However, this requires a clear target definition and sufficient traffic. If configured incorrectly, there is a risk of prematurely opting for a seemingly successful variant, even though it is not clearly statistically superior.
Feature Testing
In feature testing, new or revised functions are specifically tested in several variants. For example, this could be different versions of a search function, a navigation menu or a recommendation component. The aim is to find out which version offers the better user experience before a function is rolled out across the board.
Technically, feature flags are often used. They make it possible to direct users to different code versions without separate deployments. Many modern tools also allow no-code setups so that even non-technical teams such as product management or UX can carry out feature tests independently.
The right setup: A/B testing needs these tools
To implement A/B testing professionally, you need two technical foundations: an A/B testing tool and a tracking or web analysis tool.
The A/B testing tool takes over the playout of the variants and ensures that the traffic is cleanly divided between the original version and the test variant. This ensures a fair comparison between the original and the variant.
The tracking or web analysis tool is responsible for recording user behavior and linking it to the respective variant displayed. It measures clicks, completions or other relevant events and thus shows which variant had which effect.
The important thing here is that the assignment must be exactly right and both systems must work together properly.
Which A/B testing tool is most suitable depends on the setup, requirements, your own resources and available budget. In the following overview, we present a selection of common A/B testing tools in comparison:
| Tool | Varify.io® | Optimizely | VWO | AB Tasty |
|---|---|---|---|---|
|
Website |
||||
|
Features |
||||
|
Country of origin |
Germany |
USA |
India |
France |
|
Special features |
Unlimited traffic at a fixed price Test evaluations can be implemented with existing web analysis tools. Developed by CRO experts |
One of the very first A/B testing tools and the largest provider. Today a pure enterprise product with a focus on other areas that go beyond A/B testing. |
Long history and one of the largest providers worldwide. Positioned more in the enterprise segment. Also includes heatmaps and session recordings.
|
Features a range of cool widgets and integrations.
|
|
Transparent pricing |
Yes |
No |
Yes |
No |
|
Billing model |
Flat rate |
Upon request |
Traffic (staggered) |
Upon request |
|
Monthly cancelable? |
Yes |
Not found |
Yes |
Not found |
|
Price for 100,000 users per month |
$129 |
From $665 |
Upon request |
Upon request |
|
Price for 500,000 users per month |
$129 |
$2961 |
Upon request |
Upon request |
|
Free version |
No |
No |
Yes (up to 50000 users/month, limited range of functions) |
No |
|
Test version |
30 days |
Upon request |
30 days |
Upon request |
|
Traffic flat rate? |
Yes |
No |
No |
No |
What A/B testing really brings: Two real examples
Specific projects show how effective A/B testing can be in practice. The following examples come from two different industries, but show one thing in common: small changes with a clear hypothesis have led to measurably better results.
a) Carglass: +28.5 % more bookings through targeted overlay
Carglass is primarily known for repairing and replacing car windows. Less present: The company also offers additional services such as windshield sealing and it is precisely these that should be brought more into focus through targeted A/B testing.
Original (A)
👉 Standard booking process without additional reference to "Protect" (window sealing)
Variant (B)
Initial situation:
The additional service was already bookable, but was rarely actively selected. The aim was to make it visible at the crucial moment without interrupting the booking flow.
Hypothesis:
A short, contextual note directly before the conclusion, clearly formulated and visually supported, increases the likelihood that users will actively add the offer.
Test setup:
The two variants were played out using the A/B testing tool from Varify.io. The only difference was the overlay. The design and process otherwise remained unchanged
Result:
Variant B achieved an uplift of 28.5 % in the booking rate for "Protect". After further iterations, a cumulative uplift of 81.9 % was achieved. In the long term, the booking rate for the additional service rose by +182 % over the course of the year.
b) AlpacaCamping: +33 % more conversions through a small UX change
AlpacaCamping brings travelers together with exceptional pitches on private land. Authenticity and emotion are at the heart of the user experience. But this is precisely where a weak point in the search became apparent.
Original (A)
Variant (B)
Initial situation:
Most users enter via the search page. This only showed a map by default. If you wanted to see more details about a place, you had to actively switch to the list view. Many did not do this and abandoned the search early on.
Goal:
To create more visibility for the parking spaces directly at the entrance, without additional clicks. The list should immediately show users what they can expect.
Hypothesis:
If a specific place is visible immediately upon entry, the emotional connection increases and with it the likelihood of interaction and booking.
Test setup:
The A/B test ran over a period of 16 days with an even distribution of traffic to both variants. A total of over 92,000 users took part. The playout was carried out via Varify.io, the statistical significance was 97.7 percent.
Result:
The variant with a visible preview achieved 33 percent higher conversion rates and 21 percent more users who started the checkout process
The A/B test ran for 16 days with over 92,000 users. The result was statistically significant at 97.7 percent.
Conclusion:
Visual entry points activate faster than a sober map view. Emotion beats location. The preview is now an integral part of the search experience - with a clear impact on conversion.
Statistics in A/B testing: What really matters
A/B testing only provides real insights if the result is statistically reliable. A few percent difference in the result often looks impressive on the dashboard. But is it really better or just a coincidence?
Why significance is crucial:
A test is only completed when you can say with certainty that one variant performs significantly better. In practice, a confidence level of 95 % has established itself as the common standard. This means that there is a maximum probability of five percent that the difference was only due to chance.
You should know these three statistical terms for A/B testing
- Confidence level (significance level): Indicates how certain the result is. A value of 95 % means: The result is confirmed in 95 out of 100 cases.
- Sample size: Shows how many users are required for the result to be meaningful. Too few visitors = no reliable statement.
- Confidence interval: The range in which the actual value lies with a high probability. The narrower the interval, the more precise the result.
What a valid test needs:
- A clear target metric (for example clicks or bookings)
- Even distribution of traffic across the variants
- A sufficiently large user base
- Enough running time (usually several days to weeks)
- A clean evaluation with statistical testing
Want to know how many users you need for your test?
👉 Here's the significance calculator
You don't have to be a statistics expert, but you should understand why significance is so important. If you stop too early or test with too little data, you are making decisions based on chance. And that is the opposite of optimization.
FAQ - Questions & answers about A/B testing
To anchor A/B testing in your company, for example, start with a workshop that demonstrates the value: How can small changes have a big impact?
Build a cross-functional team that is on board from the start to plan and execute the tests. Set common goals and provide a platform that allows everyone to see results in real time.
This is how you create a culture in which data-driven decisions become the norm.
To overcome possible resistance, it is also essential to communicate the potential and significance of this method clearly and convincingly to decision-makers.
Show how A/B testing provides direct insights into user behavior and puts decisions on a solid data basis, leading to more conversions, sales and ultimately better products and services.
We recommend:
- Be aware of possible resistance: Deal with possible skepticism in the team and among decision-makers as well as frequent fear of change.
- Convince people: Demonstrate the ROI and the improvement in user experience.
- Get professional support: Consider bringing in experts to facilitate the integration process with specialist knowledge and best practices.
By combining clear arguments, practical examples and the willingness to invest in professional support, A/B testing can be successfully established as a valuable tool in the company.
A/B testing cracks the surface of what works on your website, but it reaches its limits when it comes to uncovering the deeper whys.
That's why it's important to think outside the box...
Immerse yourself in the world of conversion optimization and behavioral economics. These fields provide you with the tools to not only recognize which changes bring success, but also to understand why.
It's about developing a deeper understanding of your users' needs and motivations and making your website a place that not only works, but also fascinates and engages.
One of the biggest challenges with A/B testing is actually patience. Waiting for significant data can be a real test of patience, because jumping to conclusions could misdirect your optimization strategy.
It is equally important to maintain a balance between the quantity and quality of tests. Too many tests at once could leave you drowning in a flood of data. While too few tests won't reveal the full potential that A/B testing offers for optimizing and understanding user preferences.
The secret lies in making a strategic choice:
By prioritizing tests with the greatest potential for meaningful insights, you maximize the value of each test and avoid data overload.
To carry out A/B tests effectively and in line with SEO practices, the following approach is essential.
First the good news: search engines like Google support and encourage A/B testing. As long as they are implemented correctly, search engine rankings will not be negatively affected.
Here are three basic guidelines that will help:
1. Strictly avoid cloaking: Cloaking, i.e. showing different content to visitors and search engines, can harm your website. It is important that all users, including Googlebot, see the same content. This approach ensures that your A/B tests remain transparent and in line with Google's guidelines, which protects the integrity of your SEO efforts.
2. Use of 302 redirects: For A/B tests that require a redirect from the original URL to a test URL, the use of 302 redirects is preferable to 301 redirects. 302 signals that the redirect is only temporary, ensuring that the original URL remains in the search engine index.
3. Use of the rel="canonical" attribute: To avoid confusion for search engines and to signal which page should be considered the main content, the rel="canonical" attribute should be used on all test URLs that link to the original page. However, this only applies to split URL tests.
By following these guidelines, you can ensure that your A/B tests complement your SEO efforts rather than undermine them. It's key to take full advantage of A/B testing without jeopardizing your search engine rankings.
When choosing an A/B testing platform, you should pay attention to user-friendliness, integration with other tools and the type of data analysis.
A good platform will allow you to easily create, manage and analyze tests without having to become a data scientist. Also make sure that it integrates seamlessly with your existing tech stack.
High-quality platforms can be expensive, so it is important to find good value for money.
Our platform Varify.io® offers a comprehensive solution that not only fulfills the above criteria perfectly, but is also efficient in terms of costs. Even with increasing traffic, prices do not increase due to our traffic flat rate.
Find out more about the functions of our A/B testing platform here!
A/B testing is not just for online marketers...
Product teams can use it to refine features, development teams to improve usability, and content teams to measure the impact of their copy.
The key is for each team to formulate its own hypotheses and carry out tests that are aligned with its objectives. This makes A/B testing a versatile tool that creates value across departmental boundaries.
Top experts for A/B testing & conversion optimization
Get inspiration, network, learn. These personalities are shaping the field of A/B testing and conversion optimization.
Ronny Kohavi
Globally recognized experimentation and A/B testing professional. Led analytics and experimentation teams at Microsoft, Airbnb and Amazon. Co-author of Trustworthy Online Controlled Experiments.
Dan Siroker
Peep Laja
Founder of CXL.com, Speero & Wynter. Renowned A/B Testing & CRO thought leader, publishes weekly research insights and runs the "How to Win" podcast on B2B strategies.
Talia Wolf
Founder of GetUplift, an agency specializing in emotional targeting & conversion strategies. Developer of the Emotional Targeting Framework, which has been helping brands to grow for over 10 years.
Thomas Kraus
Co-founder of Varify.io® and long-standing conversion expert. Develops tailor-made optimization strategies for digital touchpoints and supports companies in putting data-driven decisions into practice.
Steffen Schulz
Co-founder of Varify.io®, building a SaaS product that makes A/B testing affordable and accessible for companies of all sizes. Combines deep expertise in conversion optimization with the goal of democratizing data-driven testing.
André Morys
Founder of KonversionsKRAFT & pioneer in the field of conversion optimization. Developed his own conversion framework, published the book Conversion Optimization. Organizer of the Growth Marketing Summit.
Karl Gilis
Co-founder of AGConsult, a Belgian agency for usability and conversion optimization. Listed by PPC Hero as one of the top 3 conversion experts worldwide and internationally renowned as a speaker.
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