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A/B testing is a method of experimental statistics used to measure the effectiveness of changes to a website or app. It compares the effectiveness of a "control group" (using the original version of the website or app) with the effectiveness of a "test group" (using the changed version of the website or app). This comparison can provide information on whether the changed version actually performs better than the original version.

An example: An online shop wants to know whether a changed version of its product page can improve the conversion rate. For this purpose, the product page is created in two different variants: Variant A is the original version of the product page, variant B contains some changes such as a different arrangement of the product images or a changed font. A part of the visitors of the online shop is then redirected to the original variant A and the other part to the changed variant B. Afterwards, it is analysed which variant has the higher conversion rate.

When A/B Testing makes sense

A/B Testing is particularly suitable when it is a question of finding out whether a certain change actually has a positive effect. It is a good method, for example, to measure the effectiveness of marketing campaigns or changes to a website or app.

An example: A company wants to know whether changing the colour of a call-to-action button increases the click-through rate. To do this, an A/B test is conducted in which one part of the visitors sees the original colour of the button and another part sees the changed colour. By comparing the click rates, the company can see whether the changed colour actually increased the click rate.

When A/B Testing is not useful

A/B Testing is not always the best way to evaluate changes to a website or app.

If the goals and results are not clearly defined or it is a qualitative change that is difficult to quantify, A/B Testing may be less useful. It can also be difficult to get reliable results if the number of visitors to a website or app is very low.

An example: A company wants to know if a new chatbot feature increases customer satisfaction. However, it is difficult to measure customer satisfaction quantitatively and there may only be a very small number of visitors to the company's website or app. In this case, it may make more sense to find out what customers think using surveys or interviews rather than A/B testing.

Summary

Overall, A/B Testing can be a useful tool to find out if changes to a website or app are actually positive, but careful consideration should be given to whether it is useful in a particular case and how the results are interpreted. In any case, it is important to set clear goals and outcomes in advance and to plan and execute the method carefully.