A/B Testing Mistakes to Avoid for Your Ads on Facebook
PropelrrMay 12, 2023
A/B testing pay-per-click (PPC) ads on Facebook is one of the most fundamental practices for social media marketers – which is why making simple A/B testing mistakes can be so disheartening, especially for experienced digital marketing experts.
Committing common errors in this basic practice can be easily avoided when you know what to bypass for your brand’s benefit. With a comprehensive list of mistakes to evade, you can enjoy a smooth experimentation process for your business and its social media marketing this year.
Looking for a list of errors to avoid when comparing versions of your Facebook ads? You’ve come to the right place. Check out this guide to the most common split experimentation mistakes you can mitigate, as prepared by the experts at Propelrr today.
What’s A/B testing and why should you do it for Facebook ads?
Before you can start navigating around common experimentation errors to avoid, you need to know what A/B testing is first, and why this practice is so important for your Facebook ads.
A/B testing, also known as split testing, is a process that allows you to compare and refine two versions of an ad, so that you understand what ad components are most effective in bringing in customers and conversions. Facilitated with A/B testing tools on Facebook’s own platform, this form of experimentation can help you gather data on your ad campaign, creatives, and audience, all at the same time.
Since this process can be facilitated by this social media platform’s own tools, newbie marketers may think that there’s no risk to running these experiments without prior experience. But if you don’t know the most common mistakes to avoid, you can still run into major experimentation errors – even with the most foolproof of conversion optimization tools today.
Navigating common mistakes for an A/B testing guide
Navigating around errors is a valuable practice in marketing experimentation. By knowing what to avoid ahead of time, you can run more effective comparisons that provide helpful insights for more data-driven decisions and improvements.
The process of A/B testing in marketing campaigns is also an iterative process – that is, you need to test again and again to continually improve and adapt your ads for long-term success. This raises the risk for making mistakes that can cost your brand real money, time, and resources too.
This risk makes prior knowledge and navigation all the more crucial for your brand’s success on Facebook. So now that you understand why this list matters for your expertise, you should read on to discover the most common examples of A/B testing mistakes to avoid for your paid ads’ success right now.
10 examples of A/B testing mistakes on Facebook ads
Having the best A/B testing tools won’t benefit your efforts if your method isn’t sound and you’re making avoidable mistakes. Discover the expert rationale behind avoiding these common pitfalls by reading this comprehensive guide right now:
- Having an unrealistic hypothesis.
- Running too many comparisons at once.
- Experimenting with the wrong audiences.
- Running it too short or too long.
- Forgetting to set a budget.
- Testing too early.
- Testing too late.
- Only doing one split test.
- Changing variables in the middle of a run.
- Giving up.
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 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 experiment is what you call a multivariate test. A/B testing vs. multivariate testing is a pretty long discussion, so we won’t get into those details for the time being.
For now, you must remember to define a specific control variable and challenger variable for your 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 running comparisons for today.
Moreover, this specificity will provide you with more insights on how to improve your Facebook Ads and its succeeding iterations.
3. Experimenting with the wrong audiences.
Here’s something you need to remember about A/B testing Facebook ads: you need to conduct your process on the right audiences to get the most relevant results.
This can go in either two ways:
- 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;
- You can also reach audiences that are similar to your existing customers through Lookalike 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 tests on social media. If you’re running split tests for two separate campaigns on this social media platform, 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 14 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 spending 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 ads by running experiments on this platform, 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. Testing too early.
There is a right time to experiment with your brand. Don’t just run resource-heavy tests for the sake of running them – do so with ample data, well-formulated hypotheses, and sound design principles in mind so that you can set yourself up for success.
Let’s say you want to run a paid ad campaign on Facebook, and you want to see which version of the ad’s creative will garner the most clicks. If you run your test right away without setting it up properly, you’ll definitely garner erroneous results and waste resources in the long run.
Take time to prepare your experiment’s design so that you have a sound hypothesis and data-driven parameters. Don’t rush into it – or else you’ll be wasting your precious time and resources on inconsistent and useless results.
7. Testing too late.
While you’ll want to avoid running tests too early, you may have noticed that this digital marketing process takes a lot of time, effort, and money to execute correctly. 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 experiments early enough, you gain a better chance at returning results that will help you optimize your campaign again and again.
8. Only doing one split test.
If you think you’re just conducting one split test for your campaign, think again. This 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 experimentation mistakes.
9. Changing variables in the middle of a run.
Changing variables in the middle of an experiment is a surefire way to ruin any test run. If you change or interrupt any parameters during your experiment, you compromise the integrity of your collected data, thus rendering it useless for your brand’s needs.
Even if you realize you made a mistake during the setup of your experiment, you need to see it all the way through to the end. This way, you can at least account for the mistakes you made, once you reach the data analysis process. Conduct your A/B testing efficiently by avoiding this error, and then just run another experiment to gather more data for your ad.
10. Giving up.
Though it has been mentioned multiple times throughout this guide, it still bears repeating: you need to run tests again and again in order to gain helpful findings for your Facebook ad campaign. If you give up on this long and iterative process, then you won’t collect the relevant data you’ll need to actually improve your ads today.
If you don’t feel good about your first few experiments, you can always revise your design and improve your next few runs. You can decide to stop iterating your ads when you have reached your set goals, or when you want to pivot to a different campaign. Just don’t make the mistake of giving up when you have so much more to give for your brand’s success.
Given all these mistakes to avoid when improving your ads on this social media platform, you should now have a good foundation on which to build your upcoming experiments. But if you have other problems or questions to clarify, check out this next section to troubleshoot issues on Facebook ads.
Why your A/B testing isn’t working: Troubleshooting issues
Facebook’s Help Center has a list of tips to improve some of the more prevalent problems that marketers face while running A/B tests. Here are just three of their many tips:
- Avoid the under-delivery of tests by making sure your audiences are big enough. Since Facebook divides your total audience population for their split tests, your ad sets may be more vulnerable to under-delivery due to these small audiences. Consider broadening your audience sizes more than usual to avoid the risk of under-delivery.
- Increase your budget to collect more relevant data and hit your goals. Under-delivery can also happen if your budget for experimentation is too low. If you run into this problem, consider increasing your budget to reach more people and gain more relevant results.
- Ensure your ad sets are different enough. If your ad sets or test setups are too similar, Facebook won’t be able to confidently declare a winning version. If, for example, your audience groups’ ages are too similar to garner conclusive results, try customizing your audiences based on interest targeting instead.
If you find yourself unable to garner results that meet your KPIs, don’t be afraid to take a step back and analyze your testing process. Garnering “negative” or “failed” results shouldn’t stop you – take it as an opportunity to dig a little deeper and gather insights that’ll help you hit your goals today.
A/B experimentation is one of the most common practices in digital marketing – but it often intimidates marketers because you can easily trip up and make costly mistakes. Now that you have this guide prepared by the experts at Propelrr, you can avoid common errors and keep these final tips in mind as you conduct your future ad comparisons:
- Give yourself time to test. If you actually want results from your research process, give yourself ample time to conduct these experiments again and again to improve your campaign throughout this process.
- Experiment repeatedly. Because you’re only comparing 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.
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