What are A/B tests?

In the world of digital marketing, optimization is the key to success. To ensure that marketing measures, websites and advertisements achieve their goals, experts use a tried and tested method: A/B testing, also known as split testing. This method is considered an essential tool that helps companies to drive conversion rate optimization and thus also contribute to a better user experience (UX ).

What is A/B testing?

Basics of A/B testing

An A/B test is a process in which two versions of a website, landing page, ad or email are compared to find out which version performs better. Part of the traffic is directed to version A (the control group) and another part to version B (the variant or variation). The results are then analyzed to determine which version has a higher click-through rate (CTR), conversion rate or another relevant success criterion.

The process steps of an A/B test:

  1. Goal definition: First of all, it is important to clearly define the goal of the test. What should be optimized? For example, do you want to optimize the landing page or increase the number of newsletter subscriptions?
  2. Hypothesizing: Based on data analysis and user experience best practices, hypotheses can be made as to why a certain change could lead to an improvement.
  3. Development of the test variants: To carry out the A/B test, two or more versions of the test object are created, whereby the variants should only differ in one or a few elements.
  4. Carrying out the test: The traffic is randomly divided between the different test versions. Analysis tools are used to monitor user interactions with each version.
  5. Evaluation: After the test period, the collected data is analyzed to see which version achieved the best results.
  6. Implementation: If a variant performs significantly better, it is used regularly and can continue to be optimized.

Why are A/B tests so valuable?

A/B testing makes it possible to make data-driven decisions and provides insights into how certain changes influence user behavior. Instead of relying on assumptions, A/B tests can be used to determine whether a change has a positive or negative effect. This reduces the risk of wrong decisions and wasted resources.

In addition, A/B tests contribute to conversion rate optimization through the targeted testing of individual elements (such as fonts, images, call-to-actions, etc.). Small changes can sometimes have a big impact on the user experience and consequently on the conversion rate. A/B tests make these correlations visible.

Methodology and significance of statistical significance

When conducting A/B tests, it is crucial to understand statistical significance. Statistical significance indicates whether the differences between the test variants can actually be attributed to the changes and are not due to chance. To determine this, statistical tests based on probability calculations are used.

In order to obtain usable results, a sufficiently large sample size and an appropriate test duration are required. Tests should not be terminated too early in order to take into account seasonal fluctuations, days of the week and other external factors.

Multivariate tests as an extension

Multivariate tests increase the complexity of A/B tests. Several variables are changed and tested at the same time. For example, if you want to know which combination of headline, image and button is most effective, a multivariate test is a good option. However, these tests require significantly more traffic in order to deliver statistically significant results.

Creating a test: practical steps for implementation

If you want to create an A/B test, start as follows:

  1. Identification of the test element: Select an element or a property of the website or campaign that you want to test. This could be, for example, the color of a button or the wording of a call-to-action.
  2. Design the variants: Design two or more versions (A, B, possibly C, D…) of the element that differ in the chosen aspect.
  3. Selection of the test group: Determine the target group. A specific target group can be
    define a specific target group
    based on previous behavior can be very helpful.
  4. Technical implementation: Use a tool for A/B testing that allows you to distribute traffic to the different versions of your site and measure performance.
  5. Measurement and analysis: Define clear KPIs (Key Performance Indicators) to measure the success of the tested versions. The
    conversion rate
    is often a key indicator.
  6. Evaluation: Compare the results to determine whether there is a statistically significant difference between the versions. Take action based on your findings.

Expertise and trustworthiness in A/B tests

It is important that A/B tests are carried out by people with the relevant expertise. A sound knowledge of
E-A-T (Expertise, Authoritativeness, Trustworthiness)
principles is crucial for creating high-quality content in the context of A/B tests. Providers with this expertise can not only deliver more accurate test results, but also provide insights into the user experience and user behavior, resulting in a trustworthy and authoritative appearance.

Best practices for successful A/B tests

To make A/B testing as effective as possible, the following best practices should be followed:

  • Change only one variable: Each test should focus on a single change in order to draw accurate conclusions about its effectiveness.
  • Sufficient test duration: Give the test enough time to collect statistically relevant results.
  • Thorough planning: Develop a content strategy
    content strategy
    that goes hand in hand with the objectives of the A/B test.
  • User centricity: Understand the context of the
    buyer’s journey
    to interpret the test results in terms of the overall customer experience.
  • Regular implementation: A/B testing should be an ongoing process that constantly provides new insights and can be adapted.
  • Use of testimonials: Make use of
    as social proof on your landing page to further increase the conversion rate.

It is also helpful to understand the concept of hypothesis testing. In a hypothesis test, an assumption (hypothesis) about a certain characteristic or behavior in the test is set against the “null hypothesis” in order to achieve significant results.

Growth marketing and A/B testing

In connection with
growth marketing
A/B tests play a key role. The aim is not only to increase the conversion rate in the short term, but to achieve steady growth. A/B testing is used to fine-tune all elements of a marketing campaign or website to continuously improve the user experience and drive growth.


A/B tests are a powerful tool for data acquisition and optimization of digital products and marketing strategies. They enable companies to make informed decisions and systematically improve the user experience and conversion rate. Continuous testing and customization can have a significant impact on the success of a product or service and should therefore be part of any digital marketing strategy.