A/B Testing & Personalization

Make more profit through Conversion Optimization

We offer consulting and technical implementation of A/B testing and personalization campaigns. From the idea, the identification of target groups, setting goals and conducting tests, to the implementation and the evaluation of your success, we support you in achieving a successful conversion optimization.

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Loop-Workflow

1. Collect Ideas & Hypotheses

2. Prioritize Ideas & Hypotheses

3. Implement and Execute Campaigns

4. Evaluation & Analysis

A/B TESTING

What is "Personalization"?

Personalization is a strategy in which advertisements, messages and experiences are displayed on a website to visitors with identical or similar characteristics (segments).

What is a null hypothesis?

A/B testing is actually used to examine hypotheses. There are two typical concepts in hypothesis testing: the null hypothesis and the alternative hypothesis. Usually, the null hypothesis indicates that the performance of the two variants A and B are identical, while the alternative hypothesis states that they are not.

How often should I run A/B tests?

There are different opinions regarding this matter, however we recommend continuous testing. You should have a clear goal and enough page visitors, in order to achieve statistical relevance within an acceptable period of time.

Client vs. Server Side Testing?

The first thing that differentiates your A/B testing requirements is client-side or server-side A/B testing. This aspect is often overlooked. Nevertheless, it should be chosen based on your needs.

  • Client-side: commonly used to optimize conversion rates in marketing or funnel, for example by creating page variations directly on the users’ browser.
  • Server-side: when you need to test more in depth in relation to the visual changes, such as products (features) or experience for engagement, retention and more.
How many users do I need for trust-worthy testing?

The wrong interpretation of statistical significance is one of the most frequent and serious mistakes committed in A/B testing. Usually the minimum required traffic is calculated using the following key figures:

  • The conversion rate of our control variation (variation A)
  • Minimum difference between the conversion values of variations
  • Confidence level
  • Statistical “Power”

For a sample calculation please use our runtime calculator on this page.

Do A/B tests have negative effects on SEO?

Many mistakenly think that A/B tests could have a negative impact on SEO. The truth is, that websites rather improve through A/B tests which results in better ranking.

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