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 a surprising number of marketers aren’t split-testing their content. In fact, 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 the top strategies for A/B testing on Facebook. Keep in mind that trends and best practices are constantly changing in response to audience preferences as well as Facebook’s tools and algorithms. It’s important 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 when used to measure results on the factors that have the greatest impact on 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 obviously depend on your unique audience.
In addition to these considerations, images are another great place to start when you’re setting up an A/B test. Visual content may be the most important overall element in a Facebook ad, so optimizing your images could lead to significantly better performance.
Limit Tests to One Element
With so many possible criteria for an A/B test, some marketers configure tests that compare performance with respect to a number of changes. This sounds good in theory, but it actually makes it much more difficult 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. Testing three versions of three different variables, for example, would require 27 ads, and it’s unlikely that a single version 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 on its own, but you also need to determine which of your results offer valuable information. This can be a confusing process 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 a period of 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 a tedious process, but it’s a great investment if you’re serious about optimizing your approach to Facebook ads. These tips will help you develop better split-tests and identify your most effective ads.