Social Media A/B Testing: Steps, Best Practices & Tips

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.

What is A/B testing on social media?
A/B Testing Lingo
Before You Get Started With Social Media A/B Testing…
How to Plan Your A/B Testing for Social Media
How to Implement and Analyze Your Social Media A/B Tests

What is A/B testing on social media?

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:

Improve your social media engagement
Increase your website traffic
Boost your sales
Learn more about your target audience

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:

Testing two different headlines for a social media post
Testing two different images for a social media ad
Testing two different times of day to post on social media
Testing two different hashtags to use in social media posts
Testing two different call to actions in social media posts

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.

A/B Testing Lingo

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:

Control: The original version of the page or app that is being tested against the variation.
Variation: The new or modified version of the page or app that is being tested against the control.
Metric: The specific measure of success that is being used to evaluate the two versions. Common metrics include click-through rate (CTR), conversion rate, and engagement time.
Sample size: The number of users that are included in the test. A larger sample size will provide more accurate results.
Statistical significance: A measure of whether the difference in performance between the two versions is likely to be due to chance or to the change that was made.
P-value: A statistical test that is used to determine statistical significance.
Significance level: The threshold that is used to determine whether the p-value is statistically significant.
Lift: The percentage difference in performance between the two versions.
Landing page: A web page that is designed to convert visitors into leads or customers.
Conversion: An action that a user takes on a website or app, such as signing up for a newsletter, making a purchase, or filling out a form.
Conversion rate: The percentage of visitors to a page or app that take a desired action.
Engagement: The amount of time that users spend on a page or app and how actively they interact with it.
Multivariate testing: A type of A/B testing that allows you to test multiple variables at the same time.
Dark launch: A type of A/B testing where the variation is released to a small percentage of users before being rolled out to everyone.

A/B Testing Versus Multivariate Testing

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
Complexity	Simpler	More complex
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 Get Started With Social Media A/B Testing…

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:

Your Existing Social Media Performance

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:

Engagement: This includes metrics such as likes, shares, comments, and saves.
Reach: This is the number of people who saw your content.
Click-through rate (CTR): This is the percentage of people who clicked on your link.
Conversion rate: This is the percentage of people who took a desired action, such as signing up for your newsletter or making a purchase.

Your Competitor Performance

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:

What type of content are they posting?
What hashtags are they using?
What times of day are they posting?
What is their engagement rate?
What is their reach?

The Overall Social Landscape in Your Niche

Finally, you need more insight into the overall social landscape in your niche. This includes factors such as:

What are the most popular social media platforms in your niche?
What are the most popular hashtags in your niche?
What are the most popular types of content in your niche?
What are the trends in your niche?

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:

What types of content are performing the best for me on social media?
What types of content are my competitors performing the best with?
What hashtags are trending in my niche?
What times of day are people most active on social media in my niche?
What types of call to actions are resonating with my audience?

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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How to Plan Your A/B Testing for Social Media

Before you start deploying variations of your ads or content, it’s essential to put a plan in place:

Define your goals: What do you want to achieve with your A/B tests? Do you want to increase engagement, reach, click-through rate, or conversion rate? Once you know your goals, you can choose the right metrics to track.
Identify your variables: What are the different elements of your social media content that you want to test? For example, you could test different headlines, images, call to actions, or posting times.
Develop your hypotheses: For each variable, develop a hypothesis about how changing it will affect your metrics. For example, you might hypothesize that using a more eye-catching image will increase engagement.
Design your tests: Decide how you will implement your A/B tests. For example, you could use a social media scheduling tool to create two different versions of a post and then schedule them to post at different times.
Choose your sample size: The size of your sample will depend on your goals and the specific metrics you are tracking. However, it is important to have a large enough sample size to get statistically significant results.

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.

What to Test in Your Social Media Strategy

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:

Content type: Test different types of content, such as text posts, images, videos, and live streams, to see what resonates best with your audience.
Headline: Test different headlines to see which ones are most effective at grabbing attention and driving clicks.
Image: Test different images to see which ones are most visually appealing and effective at driving engagement.
Call to action (CTA): Test different CTAs to see which ones are most effective at getting people to take the desired action, such as clicking on a link or signing up for your newsletter.
Posting time: Test different posting times to see when your audience is most active and engaged.
Hashtags: Test different hashtags to see which ones help you reach a wider audience and get your content seen by more people.
Targeting: Test different targeting options, such as demographics, interests, and behaviors, to reach the right people with your content.
Ad placement: Test different ad placements to see where your ads are most effective.

How to Implement and Analyze Your Social Media A/B Tests

1. Implement Your Variations

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.

2. Run your tests and analyze the results

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.

3. Implement Your Learnings

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.

4. Keep Social Media A/B Testing Best Practices in Mind

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:

Only test one variable at a time. This will help you to isolate the effect of that variable on your metrics.
Make sure that your variations are as similar as possible, with the only difference being the variable you are testing. This will help to ensure that the results of your test are accurate.
Run your tests for a long enough period of time to get statistically significant results.
Track your results carefully and make sure to analyze them properly.

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