A Beginners Guide to A/B Testing Your Articles
Tired of writing articles that don’t convert? I’m going to show you how to A/B test your article so you can write better content and get more conversions.
An increasing number of marketers are using A/B testing to establish which content works best for their target group. This article will explain what A/B testing is and how you can use it in your blog posts.
Background information
A/B Testing, also known as split testing, is the method used to compare two marketing strategy variables to identify which performs best. You can apply it to the content of a blog production, for instance. Imagine that you are deciding over which type of visual resource to insert in your blog posts.
A/B testing allows you to compare these two options and see if one works better or has a higher conversion rate than the other.
To be able to apply A/B testing, you need two versions of the same product. You can compare them on various channels or on one channel for different target groups.
Testing multiple versions on each channel is also possible but more expensive since you need additional marketing expenses per tested version. There are four primary phases in an A/B test:
a) A control is determined
b) The experiment is executed
c) Data analysis takes place
d) Results are presented
The initial version of your marketing content is being compared with another version later on. There are different options for what you can use as a control
- A current version of the content that you had created before the experiment took place. This is advisable if you want to run A/B tests on multiple monitored channels.
- A template for your future experiments. In this way, you can adapt earlier work and choose between different versions during further publishing activities.
- A version that already has a high conversion rate. If none of the available versions are satisfactory, you can also choose a version from another blog production with similar content and an optimal conversion rate.
In this case, you have to consider if your target group might be different from the tested version. If they are different, the best version may work for another target group that doesn’t work best for your current situation. Step b) is advantageous for this case because you can compare different versions of the same content to find out which one works best in your situation.
Once you have determined a control, it’s time to execute phase b). You should inform multiple channels about your experiment so that you can measure the performance of your tested content.
You should also inform people about A/B testing in general by placing links with explanations on each monitored page. This will allow them to participate in the following phase, which is data analysis (step c). To be able to define the best-performing version of your content, you can use various performance indicators.
These might include the average visit duration on your blog production, for instance.
Data analysis gives you an impression of which version your target group prefers and what they like about it. This helps you to choose between alternative versions in future blog productions. Results are presented in step d).
It’s advisable to analyze the results of A/B testing with statistical methods. The more people participate, the better you can predict future performance.
The following table explains what content elements might work best in your situation based on previous experiences.
What works well already? Use it as a template for further experiments
What has no effect? This isn’t a bad thing necessarily!
The goal of A/B testing your blog content is to determine which elements attract your target group and make it easier for them to reach their goals.
When you have determined the best-performing version, you can include it in future blog productions. There’s no reason to waste resources on unsuccessful content elements, but you can use them in future experiments.
You should also plan a date for the end of your experiment and do some final analysis. This allows you to draw conclusions about which content element is preferred if any time-related effects might have influenced user behavior, or what other factors were at play. You can then adapt your marketing strategies.
Here I have covered the basics of A/B testing on your blog posts. It’s possible to apply this method to other marketing activities, though. If you want to learn more about user behavior in different situations, here are a few links to some articles that may help you understand the behavior and physiology used in marketing.
I hope these articles are helpful for you and thank you for reading.
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