With over a billion users and billions of hours of video, the fact that YouTube’s algorithm manages to deliver what you want to watch when you visit the site is a testament to software engineering. So, how does it work?
The short answer: Nobody knows the details—not even YouTube, to an extent. YouTube’s algorithm uses machine learning to suggest videos, which means there are no set rules we can tell you. Besides, Google wouldn’t tell us anyway, as that would lead to people exploiting them.
What We Do Know
When you train a machine learning model, you give it a bunch of input and then rank its suggested outputs on how right they are.
Here’s a greatly oversimplified example. Say you wanted to train an AI to tell the difference between pictures of cats and dogs. Essentially, you’d give an AI a bunch of pictures of cats and dogs, have it start choosing, and then score it right if it answered correctly. The more it gets correct, the better it gets at choosing. The result is a machine that can identify cats and dogs. This training uses a metric by which results are judged; in our case, the cat-o-meter, or what percent of the image is indeed cat.
The metric YouTube uses is watch time—how long users stay on the video. This makes sense because YouTube doesn’t want people skipping around looking for videos to watch, as that’s more work on their end, and less time spent watching.
It’s much more nuanced than just “how long you watched a video,” though. The algorithm takes into account many different factors and ranks them accordingly: viewer retention, impressions to clicks, viewer engagement, and some other behind the scenes factors that we never see. YouTube then tailors these factors to your profile so that it can suggest videos you’re more likely to click.
What to Take Away From This
If you’re an aspiring YouTuber, the two main things to work on are maximizing your average view duration, and maximizing your click-through rate. Take the following upside-down pyramid.
YouTube suggests your video to a bunch of people, on the home screen and in the suggested tab. On my account, I have almost 750 thousand impressions. That seems pretty good, but only a fraction of those people click your video. This fraction is called your click-through rate, and it’s measured as a percent (you can see in my example that I have a 4.0% click-through rate). The Views figure shows the actual number of people that clicked through.
After someone does click the video, YouTube then measures the amount of time those people spent watching the videos.
You can see why so many YouTube creators use clickbait titles and thumbnails (to get those click-throughs) and long, drawn out videos (to up retention time). These are two very annoying traits of many YouTube creators, but hey, blame the algorithm.
A Case Study
Let’s take a look at two big channels that take different approaches to tackle the algorithm. The first is Primitive Technology, a channel run by a guy who goes into the wilderness and builds things with no tools. All of his videos are very long but keep up a good level of engagement throughout that length—quite an accomplishment as there is no narration. This fact means that he probably has a very high average view duration, which is good in the algorithm’s eyes.
Because he only makes one video a month, it’s surprising that he has over 8 million subscribers. This is probably because the long time between videos creates a feeling of something new when the next one drops. His videos are iconic, and whenever they show up in my feed, I almost always click them. I’m guessing others feel the same way, so he probably also has a high click-through rate as well.
The second channel takes a slightly scummier approach. BCC Trolling, a Fortnite “Funny Moments” channel, takes clips from popular streamers and edits them into daily videos. In the last year they’ve mastered the algorithm and shot up to 7.3 million subscribers. To maximize watch time, they put the title clip of the video somewhere in the middle of the video, forcing people to watch it for a while before coming to the clip they clicked on, essentially getting them “hooked” on the video. Because of this, their watch time is higher.
They’re also excellent at clickbait thumbnails and titles, putting *NEW* in all caps on many videos, and always with colorful thumbnails that are usually custom-made, and often very misleading. But, they’re not obvious clickbait; the videos do deliver on the title, but it’s just clickbait enough to get people to click.
This is the main thing to take away from BCC: if you’re going to clickbait your thumbnails, do it subtly. Putting outright lies in the title will often make people angry and may have the opposite effect you intend.
Either way, you should find what works for you, and use that to your advantage. Keep watch time and click-through rates in mind going forward, but stick to your format, and don’t let the algorithm dictate your content.