A/B testing is one of the most effective tools that digital marketing professionals use to optimize their content.
This strategy takes advantage of the defining feature of digital marketing: its measurability. With, for example, a magazine ad, you never know how many people look at it or how many conversions it drives. With a digital marketing asset, on the other hand, you always know how many people look at your content and how they interact with it.
What Is A/B Testing?
A/B testing, also called split testing, consists in creating two versions of the same piece of content (versions “A” and “B”) that differ only by a single element in order to measure the impact of that element on the audience.
For example, if you are A/B testing a landing page, you may create two versions of the same page, one where a “Buy Now” button is red, and another one where the same button is blue.
If the page with the red button generates more conversions, then you’ll know that using a red button makes a substantial difference, and therefore it’s the best alternative.
A/B testing can be applied to most marketing assets, including, but not limited to:
- Email campaigns
- Images and video
- Landing pages
- Calls to action
- Product descriptions
- Social media copy
A/B Testing Aspects to Keep in Mind
As mentioned earlier, basically any piece of content can be optimized using A/B testing. However, the procedure may vary depending on the specific type of asset you want to test.
For example, A/B testing an email campaign can be very easy. Since you have a list of contacts, all you have to do is send “A” and “B” versions of your content to separate groups and then measure the results.
While things may be a little more complicated with other types of content, these are some of the aspects you should always keep in mind:
- Test one variable at a time. Otherwise, you’ll never be sure of what element is enhancing (or hindering) performance.
- Create a “control” (also called “champion”) version of your content, and a “challenger” version.
- Run the test long enough to accumulate substantial data.
- Test both versions at the same time. Testing one version during summer and another one during winter, or running one during the morning and the other one in the afternoon leads to unreliable results.
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