7 Common A/B Testing on Facebook Mistakes to Avoid in 2022

Pay-per-click

A/B testing on Facebook is an incredibly common practice for any digital marketing expert today. Given how widely used it is, it can be easy for anyone to assume that you’ve already mastered the art of split testing your pay per click (PPC) ad campaigns.

But it’s just as easy to make rookie mistakes with any A/B test as it is to run one, even if you are an experienced PPC management professional. When you’re running comparisons for ad campaigns, you’ll want to make expert moves to bypass any disastrous mistakes.

Learn how to do A/B testing on Facebook Ads the right way. Take a look at this guide to discover the seven rookie moves you can avoid in your PPC ad campaigns today.

Common mistakes in A/B testing ads on Facebook

Having the best A/B testing tools won’t benefit your efforts if your method isn’t sound and you’re making these mistakes:

1. Having an unrealistic hypothesis.

First off: a hypothesis is a specific, realistic, and data-driven question that can be answered by your intended split test.

This hypothesis underpins everything you do when you’re optimizing your Facebook Ads, which is why having an unrealistic hypothesis can be disastrous for any Facebook marketing campaign. 

An unrealistic hypothesis may be a question that is chosen randomly, without regard for your campaign’s objectives or historical data of your campaign performance. It can also be a problem that is so unspecific that it demands too many big answers from your experiment in just one go.

A specific, realistic, and data-driven hypothesis will help you understand what you can improve in your current campaign process. It should pose a problem or question that can be answered by your current split test. 

Therefore, this hypothesis must be based on your campaign’s objectives, obstacles, and past data. Past data can include things like web analytics, customer feedback, and even heuristic evaluations.

Avoid the rookie mistake of underpinning your entire comparison on an unrealistic hypothesis. If you do this, you run the risk of conducting a convoluted process that will waste a lot of time and lead you nowhere.

2. Running too many comparisons at once.

Another common mistake digital marketers make is that they tend to run too many comparisons at once in a single split test.

There’s a reason why it’s called A/B testing. After all, you’re simply pitting two variables – a control variable, or a “version A,” against a challenger variable, or a “version B” – against each other.

If you plan on testing more than two variables, then that’s what you call a multivariate test, which we won’t talk about right now.

For now, it’s important that you remember to define a specific control variable and challenger variable for an A/B split test. Specificity in this regard can be a difference in the copies of the test variables while they share the same visual aesthetic. In aiming to be as specific as possible, there can be no doubt about which elements specifically worked and didn’t work in the campaign you’re testing.

Moreover, this specificity will provide you with more insights on how to improve your Facebook Ads and its succeeding iterations.

3. Testing with the wrong audiences.

Here’s something you need to remember about A/B testing Facebook Ads: you need to conduct your testing process on the right audiences to get the most relevant results.

This can go in either two ways:

1. If you’re just starting out, you’ll initially have to invest resources in several large and broad sample audiences to get statistically significant insights on who your potential customers are. Or, on the other hand;
2. You can also reach audiences that are similar to your existing customers through Look-alike Audiences.

While this may seem daunting to any digital marketer, Facebook really recommends your audience to be large and broad enough to support your research needs. This prevents under-delivery with your results from statistically insignificant sample sizes.

This also means that you can’t overlap your audiences when running simultaneous A/B processes. If you’re running split tests on two separate Facebook campaigns, then you need to use very different audiences to avoid contaminated results and other campaign delivery problems.

Though this may seem costly and time consuming, it’s actually more cost effective than wasting time on several experiments that return irrelevant results. By investing in the right audiences, you’ll gain insights that’ll help you optimize your campaign more effectively.

4. Running it too short or too long.

When planning anything digital marketing, you always need to account for time. One mistake that digital marketers make is to run a split test with the wrong duration in mind, leading to unreliable results and wasted resources in the long run.

For example, you might be tempted to run a split test too short because you don’t want to “waste” time and money on a single comparison. But by running an experiment too short, you’ll produce inconclusive results that will definitely be a waste of resources overall.

If you run an experiment for too long, then you’ll obviously use up your resources ineffectively as well. Depending on the needs of your campaign, Facebook suggests a minimum of seven days and a maximum of 30 days for any experiment like this.

Whatever duration you choose, remember to root it in a realistic hypothesis, in historical data, and on specific variables that really matter to your digital marketing needs.

5. Forgetting to set a budget.

Another rookie mistake you  don’t want to make when running comparisons on your Facebook ad campaigns is forgetting to set a budget based on your research needs. 

The “based on your research needs” part is especially critical in this common digital marketing mistake. If you’re looking to improve your A/B testing on Facebook, then you should ensure that your budget is high enough to confidently produce results, while being low enough to fit within your resource limits 

If you forget to set your budget based on your research needs, then you’ll end up either overspending or underspending on your overall process. Remember to set that all-important budget to return relevant results from your research correctly.

6. A/B testing on Facebook Ads too late.

You may have noticed that this digital marketing process takes a lot of time, effort, and money to execute correctly. Yet many digital marketing experts still make the mistake of conducting these experiments too late in the ad campaign game.

Let’s establish this right now: you need to plan your process in advance if you want to ensure increased returns on your Facebook Ads. By strategizing and executing your split testing early, you gain a better chance at returning results that will help you optimize your campaign again and again.

Again and again? Yes, you read that right. This’ll lead you to the last common mistake that digital marketers make when it comes to A/B testing on Facebook.

7. Only doing one split test.

If you think you’re just conducting one split test for your campaign, then think again. This final mistake that digital marketers make is often the most overlooked in the optimization process: doing things over and over again until you get results that really matter.

Your overall process isn’t just a one-and-done deal. With every A/B test, you gain more insights that will help you improve your Facebook ad campaign over time. To truly optimize your campaign, you need to undergo several split tests and revisions to get the best results from your Facebook ad.

This is why you need realistic hypotheses, data-driven research, specific variables, ample time, and lots of resources in this process. These will help you avoid rookie moves that will result in disastrous A/B testing mistakes. 

Key takeaways

A/B testing is one of the most common practices in digital marketing today. However, this practice often intimidates maketers because it’s easy to make costly mistakes. Now that you’re aware of these errors above, do well to avoid them, and keep these in mind as well when conducting your ad comparisons:

• Give yourself time to test. If you actually want results from your research process, give yourself time to conduct these experiments again and again to improve your campaign throughout this process. 
• Again and again? Yes, you read that right. Because you’re only split testing two specific variables every time, you’ll need to conduct several of them within one campaign in order to fully optimize your Facebook ad.
• Root your tests in the right data. Expert digital marketers out there will tell you to start your process with the right data. By using data relevant to your objectives, you’ll gain results that really matter to your entire optimization journey.

Not sure where to start on your split testing journey? Message the experts at Propelrr on Facebook, Twitter or LinkedIn for expert digital marketing advice right now.