How does the YouTube algorithm work? What should marketers know?

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In this article, we'll show you how to get the most out of YouTube, understand the platform's algorithm, and make your best videos visible.
YouTube is the most popular social media platform focused on videos. Since its launch in 2005, the platform has been responsible for keeping viewers engaged. It's clear that YouTube is doing the right thing with its algorithm, but what makes it so successful? Learn how the algorithm works and what you can do to get the most out of YouTube and get your best content seen.

How does the YouTube platform algorithm work?
The YouTube algorithm is an artificial intelligence - based recommendation system designed to serve YouTube users the best and most interesting content. The algorithm is constantly evolving to understand each user's preferences. Preferences and engagement inform the algorithm about what the user wants to see more or less of.

Understanding the YouTube algorithm
Of course, the social media algorithm is designed to provide users with the most relevant and interesting content possible. But will it always work in favor of the company's principles, values, and mission?

YouTube tells us that their approach is based on four points, which aim to:

remove content that violates policies,
reduce the number of fake news,
increase authoritative sources of news and information,
Reward trusted creators.
This tells us that YouTube's algorithm tries to prioritize macedonia mobile database content that follows policies, is factually correct, and comes from authoritative channels and trusted creators.

How will the YouTube algorithm work in 2023?
Like many other algorithms, YouTube uses machine learning to understand user behavior. The algorithm evolves to prioritize videos that users are most likely to watch and interact with.

YouTube will recommend different content to each viewer, namely the content that, according to the available data about them, will be most interesting to them.

This algorithm takes several factors into account when recommending content. These factors can be divided into two categories:

Viewer personalization.
Video performance.
Viewer personalization includes signals such as which videos users watch, which ones they ignore, etc. Video performance is then based on viewer satisfaction.