One way you can optimize your campaign is through the use of A/B testing. If you completed the previous courses in this program, you already know what A/B testing is and that it's another form of experiments used often in marketing to make all kinds of optimization decisions. From deciding on design elements in a website to the creative use in an ad, A/B testing can help. Let's go over the basics one more time, this time specifically for advertising. In advertising, A/B testing is the process of comparing two variants of an ad against each other to evaluate which one performs best. A/B testing can help reveal ways you can make your campaign better, while your campaign is running. A/B testing is quite simple, here's how it works. You create two different versions of an ad, version A and version B. Every time there's an opportunity to show your ad, the advertising platform will randomly choose whether it shows version A or version B. The results of your ads are measured against your goal, and the ad that's best at achieving your goal will be declared the winner of your test. A/B testing is used extensively in marketing and advertising. It's a great way to simply and accurately compare different advertising strategies and understand which ad performs the best. There's one very important best practice you should keep in mind when conducting A/B tests. When you create your A and B version of what you're about to test, vary only one variable. What do I mean by that? Let me walk you through an example. Imagine I'm running a campaign to sell jewelry. I have this ad with an image of a ring with a blue stone and a copy of my ad says, "Celebrate memorable moments with a special ring. Check out our new spring collection", and I have a call-to-action button here that says learn more. I can run an A/B test by considering this ads to be my A version and creating a new ad that will be my B version. The best practice in A/B testing is to make the B version differ in only one variable from the A version. In this case, I'm going to vary only the image. Instead of a blue stone ring, I'm going to show a ring with three red stones. I will keep the ad copy the same and I will keep the call-to-action button to say learn more. Why does this matter? Well, now, when I get my results back and I see that ad B won, I'll learn something from my experiment. I'll know that B won because the image was better at helping me achieve my results. Since everything else in the ad was the same, I know the image was the reason my results were better. Now I know that under these circumstances this image is best, so I can keep this as my optimal image. I may decide that I want to test some more. In fact, maybe I could improve the copy, so I can conduct another A/B test. My A version ad has the image of the ring with thee three red stones as I know this is the best image and the copy reads, "Celebrate memorable moments with a special ring. Check out our new spring collection." Just like before, the call-to-action button is learn more. For my B version, I keep the image constant but now my copy reads, "Celebrate memorable moments with a special ring. Receive 10 percent off this week", and the call-to-action is learn more. My test runs and I learned that the B version wins again, so the ideal copy is, "Celebrate memorable moments with a special ring. Receive 10 percent off this week." I know that because that was the only thing that was different between the two ads, so the better results must be because of the copy. Finally, I can test the call-to-action button. I start with the image and the copy that I know is optimal and I keep learn more as the call-to-action in my A version of the ad. For my B version, I keep everything constant but I changed the call-to-action button to shop now. Now, version A wins. Turns out that a learn more button works better for this ad than the shop now button. I know to keep running my campaign with this optimized ad. You may wonder, couldn't I just test the ads that I think will be the best ones and pick the winner without going through the step-by-step testing I just explained? Well, you can, but you'll most likely not end up with the most optimal version of your ad and you won't know which part of your ad contributed to the improved result. Imagine if you had tested the first ad we started with, the one bit of blue stone ring against the final ad we put in our last test with a different image, different copy, and a different call-to-action button, and say in this case ad B won. You wouldn't know why ad B won. Was it because the image was different, the copy was different, or the call-to-action? We would also not have ended up with the most optimal version of the ad, which we learned was this one, with a different call-to-action button. In order to get the most out of your A/B tests, test only one variable at a time. Conducting A/B tests on a regular basis as part of your online advertising strategy is a really good practice and you'll find that most online advertising platforms have the option to run these tests building, so it's usually quite easy to make them a regular part of your campaigns. In our next video, we'll look at how this works on the Facebook platform.