There is no end to what can be A/B tested on your website. By doing testing, you can optimize your website without doing major updates. At iSeatz, we focus on optimizing our clients’ website through three types of test: A/B, multivariate and content.
Below is a look at some of the most common types of web optimization tests.
A/B Testing
A/B testing is the most commonly used type of test for optimizing a website. An A/B test changes one item on a webpage at a time. While running the test, a control version (with nothing changed) and the changed version are running simultaneously. Those two experiences are tested against each other and then the results are analyzed to find if the change was more successful than the current version. This then can trigger little tweaks to optimize the website. The benefit of A/B testing is that companies don’t have to make large investments into a website without knowing if the investment will be successful. These tests are relatively inexpensive compared to other testing methods. Normally the item that is tested is a small optimization such as a button click.
Multivariate Testing
There are other tests including multivariate tests, where you test multiple items on one page at a time page. These tests are more nuanced and labor-intensive. It is also much more difficult to attribute success to the correct change as multiple changes are being tested at one time. Multivariate testing is better for testing larger updates such as design changes.
Content Testing
Content testing is the practice of testing whether or not the written content is appropriate and understood by the audience/users. The main error messages we receive on the website come from the user not filling in, clicking boxes, or clearly understanding the pay with points and other written word instructions. In digital experience, it is important to optimize not just the designs, but also the content to engage the user on each page.
Each type of testing can optimize your website for better performance and better user experience. Testing is most beneficial if it is a constant evolution of optimizations.