Next, variants of the website are created
Posted: Tue Jan 07, 2025 6:27 am
Usability - an appealing interface and a good user experience (UI/UX) - is so important in web design that you should not rely on assumptions when assessing and improving your websites. In order to be able to make evidence-based statements about good usability, A/B testing is the method of choice: two different versions of a website are compared directly with each other. This approach is similar to classic A/B testing, but this usually focuses on economically relevant variables such as the conversion rate.
Like classic A/B testing, A/B testing in the UI/UX area can be complex and time-consuming. AI tools can help here by partially automating testing. What can AI tools do here? The following article deals with this.
How does A/B usability testing work?
As with A/B testing in other contexts, the first step in usability testing is planning. To do this, hypotheses must first be formed: Which changes to the user interface have what effect on usability?
Such a hypothesis could be, for example, "Increasing the font size on the login button will make the user experience on the website more pleasant." You also need to determine the data with which you want to measure or evaluate the "user experience" construct (experts talk about "operationalization"), for example the time test subjects need to complete a task or, of course, their feedback.
to test the macedonia phone data hypothesis. Traditionally, only two variants are used here: A and B, as the term A/B testing suggests. It is also possible to work with more than two variants. However, this requires more test subjects so that more data is generated and the statistical significance of the test is maintained.
Now the tests are carried out: as many test subjects as possible are instructed to carry out certain actions on the website. Their interactions with the website (for example, clicks, time spent, etc.) and their feedback afterwards are recorded.
Laboratory or live testing?
There is an alternative to this testing under laboratory conditions, namely A/B UI/UX testing on the website in productive or live mode: Here, the test subjects are the actual visitors to the site. Half of the traffic is directed to the A version of the website and half to the B version (or a third each for three versions and so on). After the visit, visitors are either asked to provide feedback using an online form, or conclusions are drawn indirectly about the usability of the different versions based on their behavior on the website.
The statistical analysis of this data now provides insights into which version of the website actually offers the better user experience. With the help of this knowledge, permanent changes can be made to the website.
It should also be taken into account that people can react to this in very different ways. This is what user segmentation does: visitors are divided into groups based on certain characteristics, each of which is evaluated separately. These characteristics can be demographic, behavior-based or technological, to name just the most important possibilities. In the area of usability, users are often differentiated according to age (e.g. under 25, 25-54, 55+ years) and access device (PC or laptop vs. smartphone or tablet). Returning users or those with an existing account can also form useful segments of their own. The prerequisite is that there are enough test subjects for each segment, otherwise the statistical significance of the results remains insufficient.
Use of AI in A/B usability testing
A/B usability testing enables data-driven optimization of the user experience on your website - but it is very labor-intensive. On the one hand, you need experts in the backend to create the different versions of the website, and on the other hand, you need a lot of testers on the frontend.
With the help of AI, the process of A/B UI/UX testing can be at least partially automated and thus made more efficient.
There are several options for this:
Like classic A/B testing, A/B testing in the UI/UX area can be complex and time-consuming. AI tools can help here by partially automating testing. What can AI tools do here? The following article deals with this.
How does A/B usability testing work?
As with A/B testing in other contexts, the first step in usability testing is planning. To do this, hypotheses must first be formed: Which changes to the user interface have what effect on usability?
Such a hypothesis could be, for example, "Increasing the font size on the login button will make the user experience on the website more pleasant." You also need to determine the data with which you want to measure or evaluate the "user experience" construct (experts talk about "operationalization"), for example the time test subjects need to complete a task or, of course, their feedback.
to test the macedonia phone data hypothesis. Traditionally, only two variants are used here: A and B, as the term A/B testing suggests. It is also possible to work with more than two variants. However, this requires more test subjects so that more data is generated and the statistical significance of the test is maintained.
Now the tests are carried out: as many test subjects as possible are instructed to carry out certain actions on the website. Their interactions with the website (for example, clicks, time spent, etc.) and their feedback afterwards are recorded.
Laboratory or live testing?
There is an alternative to this testing under laboratory conditions, namely A/B UI/UX testing on the website in productive or live mode: Here, the test subjects are the actual visitors to the site. Half of the traffic is directed to the A version of the website and half to the B version (or a third each for three versions and so on). After the visit, visitors are either asked to provide feedback using an online form, or conclusions are drawn indirectly about the usability of the different versions based on their behavior on the website.
The statistical analysis of this data now provides insights into which version of the website actually offers the better user experience. With the help of this knowledge, permanent changes can be made to the website.
It should also be taken into account that people can react to this in very different ways. This is what user segmentation does: visitors are divided into groups based on certain characteristics, each of which is evaluated separately. These characteristics can be demographic, behavior-based or technological, to name just the most important possibilities. In the area of usability, users are often differentiated according to age (e.g. under 25, 25-54, 55+ years) and access device (PC or laptop vs. smartphone or tablet). Returning users or those with an existing account can also form useful segments of their own. The prerequisite is that there are enough test subjects for each segment, otherwise the statistical significance of the results remains insufficient.
Use of AI in A/B usability testing
A/B usability testing enables data-driven optimization of the user experience on your website - but it is very labor-intensive. On the one hand, you need experts in the backend to create the different versions of the website, and on the other hand, you need a lot of testers on the frontend.
With the help of AI, the process of A/B UI/UX testing can be at least partially automated and thus made more efficient.
There are several options for this: