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Best Route to Beat Competition is Testing!

A/B testing on Facebook is used to test different advertising components to see what performs best.

Unfortunately, in reality, there is no golden shortcut to determining the best-performing ad creative, offer, or target demographic. And here is when A/B testing Facebook advertising comes into play.

At PlanB Marketing, we love Facebook ad experiments and try new ones frequently.

What Exactly Is A/B Testing?

A/B testing, often known as split testing, is a strategy for determining whether ad headlines, body content, pictures, call-to-actions, or a mix of the above perform best for your target demographic. Furthermore, you may test different Facebook groups and ad placements to see who your ideal target is and which placements they could be targeted with.

Typically, A/B tests are publicised for a couple of weeks while marketers wait for new findings. Following the completion of the trial, a decision will be taken as to whether one alternative outperforms the other (s). Using a statistical significance calculator, you can guarantee that your results are accurate.

Unless you’ve previously developed a large number of Facebook ad campaigns for your product, predicting what type of ad design will perform best for you or which demographic audience would be more likely to buy your goods will be difficult. This is where A/B testing kicks in: you can swiftly test different ad versions and target audiences to see which ones are most efficient.

A/B Testing Facebook Ads: 5 Ground Rules that we follow

There are a few principles we should follow that makes our Facebook A/B test findings to be statistically meaningful and applicable across numerous ads. This prevents our team to become overly preoccupied with testing concepts and neglect professional campaign setup and assessment methods.

Rule #1: Only test one variable at a time.

While many are tempted to begin testing EVERYTHING at once; we understand that the fewer ad variables give the faster meaningful test results.

Since we are evaluating only one ad variable each trial makes it easy to track and assess the results.

Rule #2: Make Use of the Appropriate Facebook Campaign Structure

When A/B testing different Facebook ad designs or other in-ad features, we have two options for campaign structure:

 

 

When one combines all of the evaluated Facebook ad variations into a single ad set, Facebook auto-optimizes the advertisements, and then you will no longer receive relevant testing data.

That is why we use the second campaign structure, which separates all of our ad variants into distinct ad sets.

Rule #3: Ensure the Validity of Your A/B Test Results

How do we determine when it’s best to examine the split test data and call the experiment a day?

To ensure the validity of our A/B testing, we have a significant number of results to draw conclusions from.

To make our Facebook tests to provide useful insights, we run them through an A/B significance test to see if the findings are genuine.

Rule #4: Establish an Appropriate A/B Testing Budget

The more ad variants one tests, the more ad impressions and conversions are needed to get statistically meaningful results.

So, how much will a successful Facebook ad test cost?

We’ll need at least 100 conversions per ad variant to achieve acceptable A/B test results. If our cost-per-conversion is $2.50 and there are four different ad versions to be tested, then the testing budget should be $2.5 x four x one hundred = $1,000.

Rule #5: Sort The Facebook Ad Tests by Priority.

When looking for Facebook A/B testing options, we consider which ad aspect will have the most impact on click-through and conversion rates. After all, both time and resources will restrict our testing capability. We even create a priority matrix to choose which ad components to test first.

What Should You A/B Test on Facebook Ads?

Plan B Marketing has analysed data for our clients in Facebook ad experiments and identified the campaign aspects with the greatest split testing ROI:

Nevertheless, the best A/B testing solutions are highly dependent on your brand objectives and we give importance to it.