Team Locowise posted on 3 November 2023
Welcome to the intricate world of social media A/B testing, where every like, share, and comment is a nugget of data ready to catapult your marketing campaigns to new heights.
Navigating the labyrinth of content creation, audience targeting, and result analysis might seem daunting, but A/B testing can be as simple or as complex as you make it. Either way, you’ll be getting data-driven insights into what makes your audiences tick.
Social media A/B testing is a type of A/B testing that is specifically used to test different versions of social media content to see which one performs better. It can be used to test anything from the headline of a post to the image used in an ad.
Social media A/B testing is a valuable tool for businesses of all sizes. It can help you to:
To run a social media A/B test, you first need to define your hypothesis. What do you want to test? What metric are you trying to improve? Once you have your hypothesis, you need to create two different versions of your social media content. These versions should be as similar as possible, with the only difference being the variable you are testing.
Next, you need to randomly assign your followers to one of the two versions. This can be done using a variety of methods, but most A/B testing tools will automate this for you. Once your followers have been assigned to a version, you need to collect data on how they interact with the content. This data can include things like likes, shares, comments, and website traffic.
Finally, you need to analyze the data to determine which version performed better. If the difference in performance is statistically significant, then you can conclude that the change you made had a positive impact on your social media engagement.
We’ll get into the details, but here are some quick examples of social media A/B tests:
Social media A/B testing is a simple and effective way to improve your social media marketing results. By testing different versions of your content and comparing the results, you can learn what works best for your audience and make informed decisions about how to improve your social media performance.
If you’re reading up on A/B testing for social media, it may be helpful to get familiar with some common A/B testing terms and phrases:
A/B testing and multivariate testing are two different types of testing that can be used to improve social media performance.
A/B testing is a type of testing where you compare two different versions of a social media post or ad to see which one performs better. For example, you could test two different headlines for a social media post or two different images for a social media ad.
Multivariate testing is a type of testing where you compare multiple versions of a social media post or ad to see which combination of variables performs best. For example, you could test different headlines, images, and call to actions for a social media ad.
|Feature||A/B Testing||Multivariate Testing|
|Number of variables tested||2||3 or more|
|Time required||Less time||More time|
|Sample size required||Smaller sample size||Larger sample size|
|Uses||Testing individual elements of social media content||Testing combinations of elements of social media content|
If you are new to social media testing, I recommend starting with A/B testing. A/B testing is a simpler and faster way to test individual elements of your social media content.
Once you have a better understanding of how A/B testing works, you can start to consider using multivariate testing. Multivariate testing can be a more powerful tool for testing combinations of elements of your social media content. However, it is also more complex and time-consuming.
Before you launch A/B tests, it’s important to do some research first. Otherwise, your approach can be scattered and produce test results that are not particularly valuable.
So, make sure you look at:
Start by analyzing your social media to see how your content is performing. This will give you a baseline to measure your A/B test results against.
Here are some specific metrics to look at:
It’s also a good idea to check out your competitors’ performance to see what they are doing well and what you can learn from them.
Here are some specific metrics to look at:
Finally, you need more insight into the overall social landscape in your niche. This includes factors such as:
By understanding your existing social media performance, your competitor performance, and the overall social landscape in your niche, you can develop more effective A/B test hypotheses and get more accurate results.
Here are some specific questions that you can ask yourself to get started:
Once you have a good understanding of these factors, you can start to develop A/B test hypotheses. For example, you might hypothesize that using a different type of image in your social media posts will increase engagement. Or, you might hypothesize that posting at a different time of day will increase reach.
By running A/B tests, you can test your hypotheses and learn what works best for your audience. This information can then be used to improve your overall social media marketing strategy.
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Before you start deploying variations of your ads or content, it’s essential to put a plan in place:
Above all, be systematic in how you document your tests. Even through a simple spreadsheet, make sure you keep track of what you’re testing and the results of each test.
Depending on how far into the minutiae you get, the possibilities for A/B testing are almost endless. But here are some common elements to test across both paid and organic social to get you started:
First things first, you need to run the different variants of your post or ad. The most straightforward way to do this is with an A/B testing tool. For example, tools like Tiger Pistol let you load up your variations and target audiences and do all the hard work of segmenting and displaying results for you.
You can operate far more manually using spreadsheets and posting content through your scheduler or running ads directly in your various social platforms. But a tool designed to run A/B tests will take a lot of the labor-intensive side of things off your hands.
Once your tests are running, collect data on how your metrics are performing for each variation. Once you have enough data, you can analyze the results to determine which variation performed better.
The longer your test is running, the more accurate the results will be. Audience size can also play a huge role. You need a large enough sample size to be 100% certain of your test results.
Let’s say you were testing different colors in a social media post graphic. The A/B test results show that Variant A is the clear winner, earning more impressions and engagement.
Now, you can switch up your A/B testing. Run the posts with the winning graphic, but now try two different versions of the post copy to see which is more effective. Or, try publishing the exact same posts at different times or on different days.
As you go on, A/B testing will help you continually optimize your content and ads for optimal results.
Finally having a data-driven approach to optimizing your social media can be exciting, but it’s important not to get carried away. Keep these best practices in mind:
The research and planning needed for effective A/B testing takes time. With Locowise’s competitor reports, hashtag research tool, and predictive analytics, you’ll be streets ahead. Test the stuff that really matters—start your Locowise journey for free!