A User Guide to Split Testing on Social Media

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<a>A User Guide to Split Testing on Social Media</a>

Split testing, also known as A/B testing, is a universal process refinement system that is used to test and then compare results from two similar processes implemented in similar scenarios, with similar goals and under similar circumstances.

Originally developed and used for scientific research, split testing is now an inseparable part of business product and marketing campaign optimization. In this guide, the focus will be on how the A/B testing system can be implemented to refine actions on social media for maximum benefit.

Opportunity Identification

Technically, any and all steps on social media can be A/B tested, but doing so would be a waste of time, money, and effort. Therefore, it is necessary to identify and prioritize the opportunities for split testing with the highest optimization potentials. You should split test social media actions that are likely to have the most impact first and ensure that no resources are wasted on A/B testing steps with little to no scope for optimization.

Test Rules

The primary rules that guide all split tests universally are as follows:

  1. The test must not involve more than two variants (A/B).
  2. Ideally, each split test should be designed to test only one aspect of the action.

In the next step, we will learn how these rules are implemented to prepare a split test for social media.

Preparing a Split Test for Social Media

There are several mediums through which marketing actions are carried out on social media platforms, which further differ, depending on the concerned social media site. For example, ads, personalized messages via Messenger, and Timeline posts are the three main marketing mediums on Facebook. However, posts hold the highest potential for customer engagement and interactions across all social media platforms. Therefore, we will be focusing on split testing social media posts.

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Most social media posts can be divided into the following components for split testing:

  • Text: Heading, subject, and other written content in the post
  • Media: Image/audio/video included in the post
  • Tags: Hashtags, mentions, etc.
  • Timing: Posting time and date
  • Link Integration: Anchor text, anchor size, anchor type, etc.
  • Order: The order in which the different components are arranged

Following the primary rules of A/B testing, each of your split tests must be designed to test only one of the above components. For example, if you were to test the efficacy of posting this guide on Facebook based on its Heading, it be easily divided into:

  • Split Test A Headline: A User Guide to Split Testing on Social Media
  • Split Test B Headline: A Step-by-Step Guide to Split Testing on Social Media

Ideally, none of the other components should be changed to preserve the accuracy of this test. To understand why this is so important, let’s expand on the same example:

  • Split Test A: A User Guide to Split Testing on Social Media — posted at 8 AM
  • Split Test B: A Step-by-Step Guide to Split Testing on Social Media — posted at 8 PM

As we can see, the above compares two components (Headline and Timing) of the same post simultaneously. While it’s not an uncommon practice, it blurs the test results’ clarity. For example,

  • If Test A proved to be more successful than Test B, how will you determine the responsible component?
  • Was it the Headline or the Timing that made Split Test A more successful than Split Test B?
  • How will you compare the two metrices in terms of their impact on the same post?
  • How much of an impact did the Headline make in comparison to the Timing in Split Test A and B?
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By now, it should be clear why testing multiple components in a split test can be detrimental to the process’s effectiveness and compromise the results.

What is a Multivariable Split Test?

A multivariable split test is where split testing and A/B testing can no longer be considered synonymous. Instead of testing just two variants of the same social media post, at least three or more variants will be tested simultaneously to collect data and compare results. For example, a multivariable split test designed to test the headline for engagement can be divided into:

  • : A User Guide to Split Testing on Social Media
  • : A Step-by-Step Guide to Split Testing on Social Media
  • : A Beginner’s Guide to Split Testing on Social Media

Note that the single component rule for split testing is still valid here, but multivariable split tests will often include one or additional alterations between the variants. Whether that muddies the data accuracy or not is up for debate, but if you are looking for the most accurate data possible, test only one component at a time.

Nevertheless, multivariable tests with more than one variable will save time and effort, thus making them more suitable when you need to test several batches simultaneously. It’s good practice to include just one unique variant to improve the accuracy rate of multivariable tests that test multiple components. The following example tests both the heading and the order simultaneously:

Split Test A

  1. Heading: A User Guide to Split Testing on Social Media
  2. Media
  3. Tags
  4. Link

Split Test B

  1. Media
  2. Heading: A Step-by-Step Guide to Split Testing on Social Media
  3. Tags
  4. Link
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Split Test C

  1. Heading: A Beginner’s Guide to Split Testing on Social Media
  2. Media
  3. Tags
  4. Link

Other than the change in heading, we see that Split Test B is a unique variant of the social media post because it’s the only version that tests a different order, as compared to Split Test A and C.

Data analytics and insights from social media platforms are relevant beyond just the individual platforms themselves. The data they provide helps businesses identify, quantify, and qualify their target audiences with a considerably higher rate of accuracy.

Split testing results further refine the data which, in turn, helps in developing marketing strategies with significantly higher chances of success. A/B testing also boosts company/project resource optimization by determining the best way to allocate them on both micro and macro levels.