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Looking to increase your YouTube video views? Step one: find out what’s new with the YouTube algorithm.

More than 70 percent of time spent on YouTube is spent watching what the algorithm recommends, according to the company’s CPO, Neal Mohan. And the algorithm is very effective at knowing what people want: Mohan also says the average mobile viewing session lasts 60 minutes.

It turns out that just as the YouTube algorithm guides viewers’ behavior, it also has a big effect on the people making those videos. What your video is about, how long it is, when you post, what keywords you put into your metadata, and what action your call-to-action calls for can affect not just your video, but your whole YouTube marketing strategy’s success.

Here, we’ve compiled the latest information on what’s going on behind the curtain at YouTube, so that your video can claim its rightful place among the 400 hours of video uploaded every minute.

Bonus: Download a free guide that reveals the exact steps one entrepreneur took to gain more than 23,000,000 views on YouTube with no budget and no expensive gear.

A brief history of the YouTube algorithm Before 2012: view count

Up until 2012 (back when users were only watching 4 billion hours of YouTube per month, instead of 1 billion per day) YouTube ranked videos based on one metric: view count.

While this tactic was supposed to reward great videos, and place the most popular ones in front of audience eyeballs, instead it created a clickbait problem. If a video’s title is misleading, people might click play, but they’ll also stop watching pretty fast. This strategy was bad for quality, which was bad for advertisers, which was bad for the platform.

2012-2016: view duration and session time

YouTube re-jigged the algorithm to favor view duration (a.k.a. watch time), and time spent on the platform overall (a.k.a. session time). This caused a new wave of annoying tactics: like taking an unnecessary amount of time to deliver on a video’s promise. (Although, to be fair, YouTube has always told people that snakey optimization practices don’t guarantee anything, and to just focus on making good videos.)

Simultaneously, rewarding videos that retained viewers for longer amounts of time (some content creators interpret this as “longer videos,” though that’s not necessarily the case) meant that creators had to reduce the time they spent making each video. They couldn’t afford to make frequent, high-quality, labour-intensive videos that were also long. (This is why it’s no surprise that five of YouTube’s top ten stars in 2018 made their name by recording themselves playing video games.)

2016: machine learning

Then, in 2016, YouTube released a whitepaper that described the role of deep neural networks and machine learning in its recommendation system.

And all was made clear:

Source: Deep Neural Networks for YouTube Recommendations, 2016

Just kidding. While we learned a lot, the algorithm is still very, very secret.

And it’s not perfect. YouTube has suffered some criticism over the past two

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