To answer this question, first let me answer what is A/B testing, and what is the big deal of doing it?
A/B testing is treating your campaigns not only to generate traffic, leads, and results but also, as an experiment where two different ad versions are tested on an audience. You'll need to create two identical versions except for one variation to perform accurately A/B testing.
The change can be huge or as small as a simple title (headline) variation, an image (even with a minimum difference), a design, a button, or even the target audience itself would do, just pick one variation in the 2 ads.
Make sure to test both ads with the same audience size and budget. The ads should be as identical as possible, except for 1 particular modification that will make us feel highly confident that the results are different because of that modification.
If the variation is on the ad (and not on the audience), you would need to run the first ad version to half of your entire testing audience and the second version to the other half.
Determine for how long you'll be making the A/B testing, and try to make it as long as possible. For example, if it is only one-day A/B testing, it will be a poor test since most likely there won't be sufficient data to determine (with a high level of confidence) conclusions based on the results.
After making the tests, you should determine if the variation had a positive impact, a neutral, or a negative effect on the audience. You should always consider A/B testing for most of your ad decisions to answer one of these questions:
Which ad is better?
Which message will bring more positive reactions?
What design is more attractive to the users?
A/B testing and all data-driven decisions are fundamental to do proper and smart campaign optimization that will be worth doing to reinforce your knowledge based on proven data.
If you have any questions, feel free to ask me at firstname.lastname@example.org.
Also, please feel free to share your thoughts or comments, do you agree or disagree with this point of view?
Thanks for reading!