A/B testing is research that allows you to evaluate two variants of a webpage, for example, and find out which of them is more effective.
Almost any content and settings can be tested:
To conduct it, you need to divide the incoming traffic into two equal parts and one half of the visitors show version A, and the other version B.
After the end of the experiment, you need to check which page has brought more targeted actions, for example, registrations, purchases or page views and use it in the future.
A/B testing can help increase website conversion. After all, thanks to it, you will be able to find out how changes in design affect the behavior of the audience. You can also check how your hypotheses affect the number of orders, time spent on the service and other metrics.
A/B testing helps to understand what changes on the site can bring you profit. As well, testing helps to resolve disputes and assumptions of your colleagues about the design and interface of the service. With the help of the test you will find out the really working version. In addition, you can customize your digital product, so that, your users can easily and conveniently perform targeted actions on it.
A/B testing is not a way to increase conversion. A/B testing is a way to test your hypotheses. And nothing else. You have various hypotheses, and it is A/B testing, that allows you to confirm whether your hypotheses will work out well or not. That’s all. It will never solve all the problems of your business. It can only help to make the right decisions, as each of your hypotheses will be confirmed by data.
To improve the performance of the site, you first need to test the hypotheses regarding the elements that affect the conversion. These include calls to action, buttons, forms, and images.
But before you begin the test, consider whether you really need it and whether you have everything to implement it.
For the test results to be correct, you need a sufficiently large amount of traffic, regular conversions (registrations, bids, purchases) and a streamlined web analytics system.
If you do not have all this, then it is better not to carry out testing, because it will not give reliable results. If everything is available, feel free to proceed to the test.
1) Formulate a hypothesis