How to Run an A/B Test in YouTube Analytics

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The competition for producing great quality and engaging content is as steep as ever with many creators turning to videos to give their brands an edge at driving traffic and viewer engagement. Nowadays, everyone is uploading videos in YouTube by the thousands and making the most of its free online sharing platform. A bevy of videos make it to the trending lists daily while racking millions of viewers and subscribers. But for anyone eager to get to the top and be part of the select few who have made it to the trending lists, finding new strategies to market their videos effectively is crucial.

Many brands have opted to bring on the big guns by using site analytics and other 3rd party tools to better understand user engagement and drive more traffic to their channels. The competition is high and many creators have started talking about the need to use A/B Testing on their videos to measure the effectiveness of their uploads and make the right timely adjustments without having to wait for negative feedback when the video goes live.

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A/B Testing is the process of testing two variants of the same thing. In video marketing, this involves comparing two different versions of a particular video to gauge which one does a better job at attracting more YouTube views or engaging an audience. Video makers need to understand that nowadays, it is no longer just about putting out great content but being able to market it effectively enough to continue organic growth over time.

On top of having great quality content, video thumbnails, tags and descriptions contribute to driving traffic as well. Oftentimes, having great content with poor video descriptions or thumbnails can spell the difference between getting more YouTube views or not, and that’s not the only factor. Having the ability to do an A/B test on videos prior to publishing them live to check which version and setup works better for their audience is essential to success.

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A/B Testing can be in the form of the following:

  • Video thumbnail A VS thumbnail B. Since thumbnails are the first thing people see when seeing videos for the first time, choosing the right thumbnail for your video is an important factor to consider. Creators need to make sure thumbnails are interesting enough to get people to actually click on the play button. With A/B Testing, creators can easily make two variants of a particular video that uses different thumbnails to check which one does better at driving views and playtime.
  • Video Titles, Tags and Descriptions. A/B Testing can help creators test which title, tags and descriptions work best for the video at giving viewers what they need to get more traffic. As simple as having the wrong description can turn people away without watching the content entirely. Having the ability to test this with two video variants ensures the video has all the right qualities to perform better.
  • A & B Video Uploads. Through A/B Testing, video creators can upload two different video variants and test which one may be more engaging to viewers before making it the final version. Rather than wait for feedback when videos become live, A/B testing allows them to test which works better so they can make the necessary adjustment to their editing and message delivery for their product or brand.

Sadly, YouTube does not offer a feature for A/B Testing as of yet for its videos. Brands and companies who are serious about getting their videos to outperform and outlast their competition need to make the most of 3rd party tools that offer this feature. TubeBuddy, a YouTube analytics company that offers tools to measure key metrics, site analytics, keyword optimization and competitor tracking has made waves for creating an A/B testing tool that allows creators to check what type of images and keywords are able to drive more traffic to their videos.

The A/B testing tool is free and a convenient and helpful tool which can be used by creators to come up with their own experiments around the uploading of test videos to check on performance or engagement factors. Creators can now easily come up with two videos to check which title, description, thumbnails and tags will work best for SEO purposes in their final video release. These are then tested by TubeBuddy once a day for a particular time period. The results are then tracked and provided to video creators so they can check how these variants measure up against their original video. When all testing is done, a report is made which offers important data on key performance indicators such as views, likes, dislikes, shares and subscribers. The A/B test runs for a minimum of 2 weeks with 7 days for each video variant. This gives each video enough time to get around and for TubeBuddy to obtain enough data to give video creators the means to decide what variant they’ll stick with for the final release.

In essence, A/B testing gives creators actual insight based on collected data to make better decisions on which thumbnails, tags and descriptions work best at driving traffic to their content. This goes a long way at ensuring only the best videos make it live which helps them save on costs and time as well.

Here’s an example of how A/B testing YouTube titles can improve CTR:

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With the emergence of video as a new powerhouse in online marketing and the rising cost of video creation and production, banking on your video’s potential to generate user engagement and traffic is easier when you have Analytics and A/B testing tools to ensure you are in the right track to success. New and changing trends in the market which include the surge of different SEO strategies, means that getting your videos to the top has evolved and now goes beyond just producing great content. Video creators need to learn to think of the bigger picture and find the means to understand what their audience is looking for in terms of engagement and getting more traffic.

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Unfortunately, no. YouTube doesn’t have it’s own A/B testing tools, but you can use third party tools to A/B test some things, such as titles, thumbnails, and metadata.

While A/B testing is a great way to improve YouTube videos, there are situations where it doesn’t make much sense.
For example, you should not use A/B testing if

  1. The video is not getting enough views.
  2. The improvement is obvious (in this case, just go for the improved version right away)
  3. You can spend time on more impactful things

Of course, it depends. But one example of shows improving it as much as 32%