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Do you want more views on YouTube? Do you want to grow your subscriber base and drive more people to your business? If so, you need to understand how the YouTube algorithm works. 81% of YouTube users say they watch videos suggested by the YouTube search algorithm. The recommendation algorithm drives 70% of all views on the platform. It’s a fundamental component of YouTube, and as a creator, you can’t ignore it.
Like most search engines, YouTube isn’t entirely transparent
about their algorithm. They don’t want creators to know exactly how it works,
otherwise those creators would create content that serves the algorithm, not
their viewers. Nevertheless, through careful study and experimentation, and
some comments by the team at YouTube, we know a bit about the algorithm’s
process.
In this article, we’ll shed some light on how YouTube
algorithm works and how you can use it to your advantage when starting your
YouTube channel.
What is the YouTube Algorithm?
The YouTube algorithm is a set of computer processes used by YouTube to determine which videos to display and
recommend to users. This algorithm aims to keep users engaged on the platform by offering them content tailored to their preferences and viewing history.
The specifics of the algorithm are proprietary to YouTube
and are not disclosed fully to the public. However, through observations and
statements from YouTube, we know it involves factors like watch time, user
feedback, video freshness, channel authority, and more.
The YouTube algorithm is continuously evolving. The platform
makes regular changes to how content is recommended and ranked based on a
multitude of factors. This means strategies that work at one time might not be
as effective in the future.
As a content creator, it’s important to understand the
algorithm so you can produce high quality, engaging content that resonates with
your audience and makes it into their feed.
Brief History of the YouTube Algorithm
Before we dive into how the YouTube algorithm Works today,
let’s take a short walk through history.
2005 to 2011
In the beginning, YouTube didn’t have an algorithm.
Recommendations were based on views. During this period, YouTube’s algorithm
prioritizes videos to get the most clicks. This is the beginning of the
clickbait era. Some creators would use misleading or deceptive titles and
thumbnails to convince you to click on their videos, thereby bringing their
content to the top.
2012 to 2014
YouTube reengineers the algorithm to focus on watch time and
percent-of-video completion. Some creators focused on long-form content to
secure more watch time. other creators focused on short-term content to achieve
better percent-of-video completion. Unfortunately, it did not make the overall
quality any better.
2015
2015 was a big year. YouTube invested deeply into data
science to invent a new way of suggesting content to users. Instead of using
overall popularity and other broad metrics, YouTube would now recommend content
based on user preferences and behavior. For the first time, how you interact
with YouTube determines the content you see.
2016
2016 was the beginning of content moderation. This is when
YouTube began to take deliberate steps to remove harmful content and
misinformation. creators are now subject to community guidelines or risk having
their content demonetized or removed.
How Does the YouTube Algorithm Work in 2023?
YouTube pushes videos to users based on user feedback. User
feedback can be explicit, such as clicks, likes, or subscriptions, or implicit,
such as watch time and shares.
Interestingly, YouTube doesn’t evaluate the content at all,
just how people interact with it. “Our algorithm doesn’t pay attention to
videos; it pays attention to viewers. So, rather than trying to make videos
that’ll make an algorithm happy, focus on making videos that make your viewers
happy,” says YouTube.
Over time, YouTube builds a profile on each user. Artificial
intelligence models and machine learning use that profile to sort content and
show you options that you’re likely to engage with. Here’s the two-step process
that explains how it works.
Step 1: Candidate Generation: The algorithm picks out a few
hundred videos from their entire library of billions of videos. The candidates
reflect the user’s demographic data, as well as explicit and implicit feedback.
Step 2: Ranking Video Candidates: Now that YouTube has a bank of videos you’d like, it ranks them by engagement. Videos with more views, likes, shares, and comments get a higher score. Then the platform offers videos to you based on that score. (Some randomness is built-in so you don’t get bored.)
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