The Power of A/B Testing

Brands are busy targeting consumers to reap the benefits of post Covid-19 online engagement and sales. However, in some cases, converting audiences is not working out. The solution: give A/B testing a shot.

What is A/B Testing?
A/B testing is commonly known as split testing. The simplest way to explain this method is this: you make two versions of the same thing - option A and option B. Then, instead of choosing the one you prefer, leave it to your audience to decide which one works for them. Pitch both to similar audiences (if not identical), and see which one performs better. Think of it as a competition, with your audience voting to select the ultimate winner.

For Example…
The button colour test is probably one of the most cited A/B test examples on the internet. The colour of the main button on the home page of a website was tested in green and red to see the overall effect on conversions to that page. Although many people would have assumed that green would do better (green traffic light, guys?), the result was the opposite. The red button brought in 21% more conversions compared to the green one.

How Can YOU Use It?
A/B testing can be used for literally anything... whether you are trying to test content, visual style or the overall way products are presented. If your content focuses on copywriting, you can play around with your tone of voice and how you address your audience. A casual tone may work for some brands, a formal, informative style for others. If you are not sure which one is right for you, make two versions and put them up on different days to see which one is better received.

If your content is more visual, test different visual styles. I have another example for you here – a friend recently started a photography page on Instagram and posted photos he had taken of places around the world. However, the posts were not generating a lot of likes despite being aesthetically pleasing. So he decided to experiment by reposting an older photo, but with a ‘deep’ quote added to the image as in-visual text. He got a lot more likes for the post with the quote, compared to the one with no in-visual text. Of course, this method can be used for anything else that comes to mind. Just try not to change too many things around in both versions; otherwise you will not know what exactly caused one post to perform better than the other.

A/B testing gives you direct data to work with, for better results. Even simple things such as changing the colour of a single element could make a huge impact. And in case you don’t see any big change, you can always try again by tweaking more things (one at a time) until you start to see positive results.

Go on, give it a try.

Bisma Yusufzai is Deputy Manager, Digital Channels, NBP Fund Management Limited.