A-B testing is commonly referred to in marketing circles, but what does it mean and how can it benefit your business?
What is A-B Testing?
According to Digital Marketing Magazine, A-B testing is also known as split testing, and it brings scientific methodology to marketing whilst removing any guesswork. A-B testing provides data-backed decisions which can be implemented across many types of communications, whilst taking into account a wide range of variables.
Why do Testing?
Although it requires time and effort to implement A-B testing, the results can prove really worthwhile to your business. By understanding which promotions, offers or marketing material are performing better than others, you can concentrate on the most effective options, to yield the best results for your company.
With scientific data at your fingertips, you also have solid reasons to back-up why one strategy is better than another, without relying on guesswork or simply having a hunch.
What Can You Test?
The beauty of A-B testing is that you can pretty much test anything, either on-site or off-site. You might want to see how effective a sales email is performing, or advertising copy, for example. Consider those elements or variables on your website that might have the biggest impact on your business, such as your call to action, your copy headlines, product descriptions or any graphics used.
A-B testing can take up a lot of time, and you need to do it long enough for the results to be accurate and meaningful. For this reason, you might wish to consult testing experts such as an AdWords specialist like http://www.elevateuk.com/ppc-management/. This not only saves you time, but ensures that all the right processes and procedures are carried out by those in the know.
It’s also important that before you even begin testing, you have a clear idea in your mind of what you want to achieve from the process, and what results you are looking for. Knowledge of your baseline results (ie. those that you are already achieving) is vital to make a meaningful comparison.
Bear in mind that when you are testing one strategy against another, it needs to be running at exactly the same time, so that you can take into account similar variables. It is impossible to factor in changing variables if you test and compare two things on different days, for instance.