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Split tests are likely the best example of A/B testing as it removes some of the cons we see in sequential and geolocation testing. The problem is that true A/B testing is also the hardest to come by. ab testing examples - screenshot of google ads ad rotation optimization options Platforms like Google Ads and Meta Ads have long done away with rotating variables evenly. For example, both platforms have AI-powered machine learning that will pretty .


Much always favor one ad variant over another based on the desired outcome of the campaign or ad set. The same is true for bid strategies. If you’re testing manual versus auto-bidding, or one CPA target versus another, those two Iceland Phone Number campaigns are likely not going to enter the auction on the same foot. One will be prioritized over the other and you’ll have an unbalanced test. ab testing examples - google ads experiment screenshot This is where experiments in Google Ads and split testing in Facebook Ads can come in. By using these tools, you can set up tests to focus on single (or multiple) variables and give them each a fair shot in the auction.



Here are a couple of videos that walk you through Google Ads Experiments and Facebook Ads A/B testing. Measuring success across different PPC A/B testing examples Now that we know how we’re going to test, we need to get more specific on the PPC metrics we’re going to use to determine success. Unfortunately, I’m not willing to accept “perform better” as a good answer. First, we have to decide what our main KPI is. Is it your Google Ads cost per lead? Conversion rate? Click-through rate? Impression share? This will rely entirely on your hypothesis and which A/B testing example you choose to implement. Pick the stat that will best reflect a success or failure for your test. (Don’t worry, this isn’t the only metric we’d focus on. More on that in a