A/B testing, also sometimes known as split testing, is a method of comparing two versions of a website to determine which one performs better. It’s a nice and simple way to test changes to your webpage against the current design and to figure out which change produces better results. By using A/B Testing you can easily measure the impact of the changes on user behaviors, and to keep the variant which provides the best results therefore improving your websites overall conversion rates.
What is A/B Testing?
In an A/B test, you take a webpage or user experience and modify it to create a second version of the same page. This change can be as simple as a single headline or button, or be a complete redesign of the page. Then, half of your traffic is shown the original version of the page (known as the control), and half are shown the modified version of the page (the variation).
The engagement of users with each version is measured and collected in an analytics dashboard such as Simple A/B Test, and is analysed for statistical significance. You can then determine whether changing the experience had a positive, negative, or no effect on visitor behavior.
Why is A/B Testing Important?
A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the results. This allows them to construct hypotheses, and to better understand why certain elements of their experiences impact user behavior. It is entirely possible for a new design to perform worse than the existing design, and in which case you would not want to release that change to your visitors.
More than just answering a one-off question or settling a design disagreement, A/B testing can be used consistently to continually improve a given experience, improving a single goal like conversion rate over time.
How Does A/B Testing Work?
At its core, A/B testing is about comparing two things (version A and version B) and deciding which is better. It’s a direct way to measure the impact of various design changes on user behavior. But how exactly does this process work?
- Identify a Goal: Your goal is the metric that you plan to improve through A/B testing. It could be anything from click-through rate, conversion rate, or time spent on a page.
- Generate a Hypothesis: Once you’ve identified a goal, you can generate A/B testing ideas and hypotheses for why you think they will be better than the current version.
- Create Variations: Using your A/B testing software, make the desired changes to an element of your website or mobile app experience. This might be changing the color of a button, swapping the order of elements on the page, hiding navigation elements, or something entirely custom.
- Run the Experiment: Next, you start your experiment and wait for visitors to participate! At this point, visitors to your site or app are randomly assigned to either the control or variation of your experience. Their interaction with each experience is measured, counted, and compared to determine how each performs.
- Analyse the Results: Once your experiment is complete, it’s time to analyse the results. Your A/B testing software will present the data from the experiment and show you the difference between how the two versions of your page performed.
Example of A/B Testing
Let’s say you have an e-commerce website and you want to increase the number of sign-ups. Your sign-up button is currently blue and located at the top of the page. You hypothesise that changing the button to green and moving it to the center of the page will increase sign-ups.
To test your hypothesis, you create a second version of your webpage that looks exactly the same as the original with one key difference: the sign-up button is green and located in the center of the page. You then split your website traffic between these two versions and measure the number of sign-ups each receives.
After a set period, you analyse the results. If the green button resulted in more sign-ups, you can confidently make this change to your website. If there was no difference or the blue button performed better, you know not to implement the green button.
Now you have a good understanding of what AB testing is, here’s a few more related blog posts which will help you implement your testing strategy further: