A/B testing is a method of comparing two versions of a product or service to determine which one is more effective. In A/B testing, a control group is given the original version of the product or service, while a second group is given the modified version. The two groups are then observed to see which version performs better. In this article, we’ll provide a comprehensive introduction to A/B testing your LinkedIn content using Seenly, and help you to optimize your content style and strategy.
Introduction to A/B Testing
A/B testing is commonly used in the fields of marketing and user experience design. For example, a marketing team may want to test two different versions of an advertisement to see which one is more effective at generating clicks. In this case, the control group would be shown the original advertisement, while the second group would be shown the modified advertisement. The team would then track the number of clicks generated by each advertisement and use this data to determine which version is more effective.
In user experience design, A/B testing is often used to compare the effectiveness of different design elements, such as the placement of a button or the use of a specific color. For example, a design team may want to test two different versions of a website to see which one is more user-friendly. In this case, the control group would be given the original version of the website, while the second group would be given the modified version. The team would then observe the behavior of the two groups and use this data to determine which version is more effective.
So far we’ve talked about A/B testing in general, and admittedly, because of it’s difficulty its more commonly used by larger corporations with in-house data science and analytics teams. But is it possible to do A/B testing for your personal or small business LinkedIn page?
Boosted posts vs Regular Content
There are two main types of content which you might like to test and the approaches for A/B testing differ for each.
The first type which you can test is Boosted content.
LinkedIn Boosted Content is a feature that allows users to promote their posts and increase their visibility on the platform. When a user boosts a post, it appears in more users’ feeds and is more likely to be seen by their connections and followers. Boosted content can also appear in targeted search results and on the LinkedIn homepage.
Boosting a post on LinkedIn typically involves selecting the post that you want to promote and choosing a target audience, budget, and duration for the boost. LinkedIn will then use its algorithms to determine the best times to display the boosted post to maximize its reach and engagement.
The key with boosted content is that you post the two similar but slightly different posts at exactly the same time and test their performance simultaneously. Imagine that you want to test 2 hooks and you are not sure which one is better. Well, you can create two exact copies of the post, each with a different hook and then boost them with the same budget.
This upside here is that since you are posting them at the exact same time, everything is experimentally controlled. Basically whichever post has the best conversion rate wins.
Note on Experimental Controls
For those that haven’t heard of the concept, experimental control is a key concept in scientific research and refers to the process of isolating and controlling variables in an experiment. In an experiment, a control group is used as a standard of comparison for the results of the experimental group. The control group should be identical to the experimental group in every way except for the one variable being tested.
By using a control group, researchers can isolate the effects of the variable being tested and eliminate the influence of other variables. This helps to ensure that the results of the experiment are accurate and can be reliably replicated.
Experimental control is important because it allows researchers to draw conclusions about cause and effect in their experiments. Without control, it would be difficult to determine whether a particular outcome was caused by the variable being tested, or by some other factor. By using a control group, researchers can confidently attribute any observed effects to the variable being tested. Logical right?
How to account for variability (randomness)
Lets say you’ve boosted your two posts and for practical reasons you could afford to boost one of the posts more than the other. In the end of the A/B test, the results were:
Post A; 100 impressions and 10 clicks.
Post B; 10 impressions and 2 clicks.
Which of the two posts actually performed better? 2/10 is better than 10/100 right, so intuition tells us to go with Post B. But you have to be careful here, clicks come with a lot of randomness and the second post only had a small sample size.
To do this properly, you have to be thorough and do a statistical test. In fact, believe it or not there is a ~24% chance that A is actually better than B. So you probably don’t want to be betting too much of your hard earned money on either without a bit more testing.
We are currently building this framework into Seenly (coming soon 2023) so you don’t need to worry about the advanced statistics. Stay tuned.
Believe it or not, regular content is even harder to test. We’ve seen so many content creators drawing false conclusions from their experiments because they can’t properly control all the variables.
Consider for a second that you test two posts, one after the other. The first was posted on Tuesday and the next on a Thursday. As the posts were published on different days, you don’t know whether the performance was due to it’s quality or just the day of the week.
Then there are all the other factors! Maybe the performance is due to the season, maybe its your post frequency, maybe you’ve just grown your following since you last published content! There are so many factors at play influencing the amount of engagement you receive.
Therefore, without experimental control, we need to use to a technique called Regression Analysis.
Regression analysis is a statistical technique used to model and analyze the relationship between two or more variables. In regression analysis, a statistical model is created that describes the relationship between the dependent variable (the variable being predicted) and one or more independent variables (the variables being used to make predictions).
Regression analysis is commonly used in many fields, including economics, finance, and social sciences, to predict future outcomes or trends based on historical data. For example, a company might use regression analysis to predict future sales based on factors such as past sales, marketing spend, and economic conditions.
There are many different types of regression analysis, including linear regression, logistic regression, and nonlinear regression. Each type of regression analysis uses a different statistical model to describe the relationship between the dependent and independent variables.
Again, we are currently building this framework into Seenly (coming soon 2023). All you’ll need to do is mark your posts for experimentation and let us handle the details. In the meantime, please reach out to our support line for any inquiries, we are always happy to support.
In summary, A/B testing is a useful method for comparing the effectiveness of different versions of your content, post style or service. By carefully observing your performance and analyzing the data, you can make informed decisions about how to improve your engagement.
In this article, we’ve provided a basic introduction into the different techniques at play. If you would like to find out more information on this topic, please contact us here.