The YouTube algorithm is safeguarded by the platform itself, but many experts have done incredible work solving the data chains that make up the algorithm. Understanding how this algorithm can work in your favour is critical for many brands and content creators looking to increase their views, subscribers and overall ad revenue opportunities on the YouTube platform. This process is sometimes called YouTube search engine optimization, or YouTube SEO.
In 2016, Google engineers Jay Adams, Paul Covington and Emre Sargin wrote an in-depth white paper on the YouTube algorithm and what they identify as important signals of influence within the platform. These signals are responsible for ranking YouTube videos, thereby increasing the likelihood that they will be seen by users not subscribed to a particular channel. These seven data signals include:
- The click-through rate or the number of clicks on your video
- Time spent watching – this is the combined amount of time that viewers spend watching your videos
- How many times a user has watched other videos on your channel
- How many times a user has watched similar videos
- A user’s search history
- The user’s total analysis of previously watched videos
- The user’s location and other important demographic information
It is important to understand the signals that you can influence and the signals that are determined beyond your channel. Click through rate and time spent watching are critical objectives that users can manipulate to increase their channel’s videos and overall visibility on the platform.
The most important equation in the YouTube algorithm is the delicate balance of click-through rate and time spent watching. This balance is a critical component of YouTube’s supposed “war on clickbait.”
Clickbait videos try to mislead the algorithm by increasing their click-through rate by promoting exaggerated, often false video titles and incorrect image thumbnails. Users who take advantage of clickbait content try to cheat the system and increase their visibility when users search YouTube.
To combat this, YouTube has embedded the time spent watching feature, calculating the time viewers spend watching a video. In the data of the algorithm, the primary indicator of a quality video is the amount of time viewers spend watching it, not simply if the video is clicked on and then quickly exited. If a video has a high click-through rate and low watch time, it is flagged as clickbait.
By contrast, if you prioritize watch time data, your channel would consist of incredibly long videos that are less likely to have a strong click-through rate. This important balance of click-through rate and time spent watching helps YouTube optimize quality videos for the platform.