Understanding YouTube: How Video Ratings Work
All of our videos are ranked based on a number of important metrics. Let's look at what and how affects video ratings:
Watch Time: This is the amount of time users watch your video. The bigger, the better.
Average Watch Duration: How much time users spend on average watching a video in one session. This is an important factor in understanding interest in content.
Maintaining audience attention: Percentage of video views compared to its total duration. The closer to 100%, the better.
Click-through rate (CTR): This metric measures how much your video attracts clicks. High CTR means more views.
Shares: The number of times your video was shared on other platforms. This is a way to distribute your content.
Likes and Dislikes: The number of likes and dislikes given to your video. This setting now has less impact following changes to dislike counting.
There are also other metrics that influence video rankings based on the personalization prediction model. These include location, user history, viewing history, as well as the frequency of clicking on “uninterested” on a video and other factors.
Knowing these metrics will help you better understand how YouTube video ratings work.
Watch Time: This is the amount of time users watch your video. The bigger, the better.
Average Watch Duration: How much time users spend on average watching a video in one session. This is an important factor in understanding interest in content.
Maintaining audience attention: Percentage of video views compared to its total duration. The closer to 100%, the better.
Click-through rate (CTR): This metric measures how much your video attracts clicks. High CTR means more views.
Shares: The number of times your video was shared on other platforms. This is a way to distribute your content.
Likes and Dislikes: The number of likes and dislikes given to your video. This setting now has less impact following changes to dislike counting.
There are also other metrics that influence video rankings based on the personalization prediction model. These include location, user history, viewing history, as well as the frequency of clicking on “uninterested” on a video and other factors.
Knowing these metrics will help you better understand how YouTube video ratings work.
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