What is a Good (or Bad) YouTube Retention Percentage?

YouTube Retention

One of the keys to enable your new YouTube video to go viral is Average View Percentage, basically how much of your video that viewers are watching. Put simply, YouTube Retention.

There are many ways to increase YouTube retention and we go through a few in our latest masterclass, and we’ll be doing more posts going forward on different tips and tricks (hint, newsletter, hint).

However one of the most frequent questions out there is, what is a good YouTube view percentage. There’s been numbers thrown around by even MrBeast himself, saying 70% while others have said anything over 50%. It turns out, it’s not that simple.

YouTube themselves shared the data as you can see above on what a good retention rate is by the length of the video. This will be directly what the Algorithm itself would see as averages and so on. As such this is the key to go by.

Basically, the key to the retention level is this, the algorithm looks at the percentage based on the length of the video.

The YouTube algorithm is a complex system that determines which videos are recommended and surfaced to users on the YouTube platform. Developed by YouTube’s engineering team, the algorithm employs machine learning techniques to analyze user behavior, video metadata, and other factors to personalize and optimize the content users see on their homepages, search results, and suggested videos.

At its core, the YouTube algorithm aims to maximize user engagement and satisfaction by delivering relevant and engaging content. It accomplishes this by considering various factors when ranking and recommending videos. Some of these factors include:

  1. Watch time: The amount of time users spend watching a video is a crucial metric. Videos with higher watch times are more likely to be recommended to others, as it indicates that they are engaging and valuable.
  2. Relevance: The algorithm evaluates the content of a video, including its title, description, and tags, to determine its relevance to user queries. It aims to match users with videos that closely align with their interests and preferences.
  3. Engagement metrics: User interactions such as likes, comments, and shares provide valuable signals about the quality and appeal of a video. Higher levels of engagement indicate that a video is resonating with viewers and may influence its visibility.
  4. User history and preferences: The algorithm takes into account a user’s viewing history, subscriptions, and other interactions on the platform to personalize recommendations. It seeks to understand individual preferences and tailor the content accordingly.
  5. Freshness: YouTube also prioritizes recently uploaded videos to ensure that users are exposed to the latest and most up-to-date content.

It is important to note that the YouTube algorithm is constantly evolving and adapting based on user feedback and data. YouTube regularly introduces updates and changes to improve the accuracy and relevance of its recommendations.

The algorithm’s ultimate goal is to create a personalized and engaging experience for each user, helping them discover new content they may enjoy while keeping them connected to their favorite creators. However, it has also sparked discussions around issues like filter bubbles and echo chambers, as it can inadvertently reinforce certain biases and limit exposure to diverse perspectives.