A/B Testing & Personalization
You want optimize the user experience of your website and adapt personalized content for your target group?
As an A/B Testing Acency we are happy to advise and support you in the technical implementation of A/B tests and personalization campaigns for a higher conversion rate!
A/B Testing + Personalization = Boost your conversion rate!
A/B testing involves comparing two versions of a website to determine which version performs best with users. The aim is to find out which elements on a website are crucial for increasing the conversion rate and thus for users to make more registrations, downloads or purchases. As an A/B Testing Acency Digital Loop can help you with those tests.
Another method is personalisation, where users are presented with different content and offers on the website depending on their characteristics and search intent. Personalisation campaigns help you to adapt your website to the needs of specific visitor groups.
The combination of A/B testing and personalisation in web optimisation enables you to increase user-friendliness, generate leads and thus increase your sales!
1. Collect Ideas & Hypotheses
2. Prioritize Ideas & Hypotheses
3. Implement and Execute Campaigns
4. Evaluation & Analysis
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Our Services in A/B Testing & Personalization
1. Target Definition
– Generation of hypotheses and identification of test targets
– Selecting the tools that fit your needs best (e.g. Google Optimize, Optimizely)
2. Side Variations
– Creating side variations with consideration of the accurate number of variables for the detailed determination of the cause-effect-relationship.
3. Execution of the A/B test & analysis of the results
– Setting up the measurement process and then collecting the data to test the hypothesis
– Evaluation of the results for the derivation of new measures for your website
Our A/B Testing & Personalization Team
- Full Stack Entwickler
- 6 years of experience in data analytics & market research
- Adobe Analytics certified expert
- 10+ Jahre experience in Digital Analytics, MarTech & Tech SEO
- Google Analytics & Adobe Analytics expert
- 6 Jahre expericience in Digital Analytics, MarTech & Digital Marketing
- Google Analytics expert
Further questions about A/B Testing & Personalization
Which elements are tested during A/B testing?
A/B testing generally tests the following elements: Calls-to-action, headlines, images, text length, forms, menu bars. Our experienced experts will be happy to support you in planning and organizing the testing processes.
Is A/B testing only performed on websites?
Is A/B testing only performed on websites?
In addition to websites, A/B testing can also be performed for emails, PPC ads, and CTA buttons.
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.
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.
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.