When it comes to Facebook marketing, you’ll see A/B testing being talked about a lot, as it’s very common and a good option to use. It allows you to test different variables like your ad creatives, audience, or placement to determine which strategy performs best and improves future campaigns.
So you’ve chosen a variable you want to test and have decided what metrics to measure. The next step is to divide your budget equally and then assign measurements between each variation of the variable you have chosen.
My own experiment:
Here I want to discuss my own A/B testing experience. It’s really useful for all those who are newly involved in digital marketing. I recently experimented with one of our client’s “Shugah” engagement campaigns where I tried to get to know which kind of audience responded well to our campaign. The awesome thing about “Shugah” is that it is a very versatile client of ours that allows us to use a variety of images and videos to share your content and promote their business. So I’ll stick with that audience for future results. As I know that the audience responds well to video, I set up two different A/B testing campaigns.
I created two ad sets for testing between a broad audience and an interest-based audience using the same creatives: one is a video ad and the other one is a static post, and I ran a campaign for two days each. When my testing was completed, they gave me the results of my testing. I got a clear image of my future campaigns because now I know what kind of audience I have and what variables they can react to, what kinds of things hook them up regarding our client’s business.
Some marketers shy away from A/B testing, often because of a misunderstanding about the purpose and value of the testing method. I’d like to suggest that every marketer do experiments on A/B testing. It will help you make smarter decisions and generate more leads. Keep in mind that you may use this option as well, regardless of whether you are testing informally, e.g., by turning on and off ad sets or campaigns manually, since this can lead to inefficient delivery and unreliable results. A/B testing helps ensure your audiences will be evenly split and statistically comparable, while informal testing cannot guarantee the same.