A/B Testing involves way to compare two versions of a single variable like two versions of a single webpage. Version A is the current version (the ‘Control’), while version B is the modified page (the ‘Treatment’). By running both pages simultaneously, you can easily see which one generates more sales or sign-ups.
Running A/B tests is the only way to optimize a website with certainty. For the most well-known digital brands, testing is a constant process that guides their web development. However, since the introduction of tools like Optimizely and VWO in 2010, A/B testing has also become an important part of digital marketing.
Testing has one major advantage over alternative ways of optimizing a website: it is based on real users. Whilst UX design, best-practice guidelines and customer journey analysis can provide hints and suggestions, real-world testing offers certainty.
- E-commerce websites use it to strengthen their conversion funnel
- Saas websites use it to improve their home page and enhance their sign-up process
- Lead generation websites use it to optimize their landing pages.
In order to perform effective tests, you need a “hypothesis”, a way to edit your site and a tool to record the results. Your hypothesis is simply your idea about how to improve your webpage. This might be changing the location of a call-to-action, the layout of a page, or even the colour of a button. 
A/B testing software monitors and records the effect of the changes on your visitors’ behaviour. It divides traffic between the ‘treatment’ and the ‘control’ and measures the different responses. The most sophisticated tools even send more visitors to the best-performing page. That way, you don’t lost out on customers whilst your test is running.
Once your site has received enough visits, your software will declare a winner. However, there is another important step to make before the changes can be made permanent. Analysing the statistical significance of your data is a crucial part of the A/B testing process
Split testing is the same as A/B testing, except the two pages, A and B, are assigned their own URLs. This makes the loading speed of the pages faster, and allows for more extensive changes. However, it can also be a more complicated process and there is a larger possibility of contaminated data.