Instead of counting the number of clicks or views a video gets, YouTube’s algorithms focus on ensuring viewers are happy with what they watch.
This article examines how YouTube’s algorithms work to help users find videos they like and keep them watching for longer.
We’ll explain how YouTube selects videos for different parts of its site, such as the home page and the “up next” suggestions.
We’ll also discuss what makes some videos appear more than others and how YouTube matches videos to each person’s interests.
By breaking this down, we hope to help marketers and YouTubers understand how to work better with YouTube’s system.
A summary of all facts is listed at the end.
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Prioritizing Viewer Satisfaction
Early on, YouTube ranked videos based on watch time data, assuming longer view durations correlated with audience satisfaction.
However, they realized that total watch time alone was an incomplete measure, as viewers could still be left unsatisfied.
So, beginning in the early 2010s, YouTube prioritized viewer satisfaction metrics for ranking content across the site.
The algorithms consider signals like:
- Survey responses directly asking viewers about their satisfaction with recommended videos.
- Clicks on the “like,” “dislike,” or “not interested” buttons which indicate satisfaction.
- Overall audience retention metrics like the percentage of videos viewed.
- User behavior metrics, including what users have watched before (watch history) and what they watch after a video (watch next).
The recommendation algorithms continuously learn from user behavior patterns and explicit satisfaction inputs to identify the best videos to recommend.
How Videos Rank On The Homepage
The YouTube homepage curates and ranks a selection of videos a viewer will most likely watch.
The ranking factors include:
Performance Data
This covers metrics like click-through rates from impressions and average view duration. When shown on its homepages, YouTube uses these traditional viewer behavioral signals to gauge how compelling a video is for other viewers.
Personalized Relevance
Besides performance data, YouTube relies heavily on personalized relevance to customize the homepage feed for each viewer’s unique interests. This personalization is based on insights from their viewing history, subscriptions, and engagement patterns with specific topics or creators.
How YouTube Ranks Suggested Video Recommendations
The suggested videos column is designed to keep viewers engaged by identifying other videos relevant to what they’re currently watching and aligned with their interests.
The ranking factors include:
Video Co-Viewing
YouTube analyzes viewing patterns to understand which videos are frequently watched together or sequentially by the same audience segments. This allows them to recommend related content the viewer will likely watch next.
Topic/Category Matching
The algorithm looks for videos covering topics or categories similar to the video being watched currently to provide tightly relevant suggestions.
Personal Watch History
A viewer’s viewing patterns and history are a strong signal for suggesting videos they’ll likely want to watch again.
Channel Subscriptions
Videos from channels that viewers frequently watch and engage with are prioritized as suggestions to keep them connected to favored creators.
External Ranking Variables
YouTube has acknowledged the following external variables can impact video performance:
