How to Properly A/B Test on Facebook
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A/B testing is one of the most effective ways to increase your marketing ROI, but many marketers aren’t split-testing their content. Facebook is arguably the best platform for running informative split tests that give you the data you need to optimize future campaigns.
This article will cover a few of Facebook’s top strategies for A/B testing. Remember that trends and best practices constantly change in response to audience preferences and Facebook’s tools and algorithms. It’s essential to consistently reevaluate your current approach, carefully consider your key metrics, and look for ways to improve.
Test the Most Important Elements
Split-testing is almost always a good idea, but it’s typically a better tool to measure results on the factors that impact your success. While criteria like age, gender, and location tend to be closely correlated with marketing results, the best metrics for your next A/B test depend on your unique audience.
In addition to these considerations, images are another great place to start when setting up an A/B test. Visual content may be the most crucial overall element in a Facebook ad, so optimizing your images could lead to significantly better performance.
Limit Tests to One Element
With many possible criteria for an A/B test, some marketers configure tests that compare performance concerning several changes. This sounds good in theory, but it is much more challenging to take away meaningful information from each test.
The problem with testing for more than one variable is that you’ll put much more resources into each test than they end up returning. For example, testing three versions of three different variables would require 27 ads, and it’s unlikely that a single performance will emerge as the best option.
Rather than including these in a single test, separate each element when you start A/B testing. In that same situation, you could run a test for each of those variables by creating just nine total ads. From there, it’s as simple as combining the most successful version of each element into a single optimized ad.
Use an A/B Significance Test
Setting up an informative split test is complicated enough, but you must also determine which of your results offer valuable information. This can be unclear if you don’t have previous experience with statistics.
In general, you should try to collect at least several hundred conversions on each variation over several days before examining your results. You can also enter your results into a significance test tool to find the most actionable data.
A/B testing can be tedious, but it’s a significant investment if you optimize your approach to Facebook ads. These tips help you develop better-split tests and identify your most effective ads.