#A/B testing, also known as #split-run testing or #buckettesting, is the process that starts with a hypothesis that makes you believe that doing a small change to your ad will help you to improve the results.

You have the control ad (A) and the one with a variation (B), and you compare them at the same time, changing only one variable between them to see which one gets the better performance. For example, a higher click rate. It is a #userexperience research methodology vastly used in this online world that surrounds us and the backbone of almost every successful app, webpage, #adcampaign, etc…That you can think of!

Let’s say an organization puts up two ads-- both ads are pretty much the same, but if you were to look closely you will see that the variation ad has a different #CTA (call to action) button, “sign up” instead of “order now”. The idea of A/B testing comes from the hypothesis that maybe that small change (that could also be the text, the image, the link description, or the headline) can affect positively or negatively your #brand and if that’s so, how exactly.

Many tools can help you to compare the results and show you which one has a better performance than the other like crazyegg, #google analytics & #google optimize, optimizely, and many others.

If you are using Facebook manager only, you will anyways see which one has a better click rate, conversion rate, and few other metrics that perhaps is enough for you if, as mentioned in the previous example, you are just running ads on #Facebook or #Instagram.

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