How the YouTube Algorithm Works in 2023: The Complete Guide

How the YouTube Algorithm Works from 2005- 2023: The Complete Guide

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Hey what’s up family? It’s your favorite blogger/Youtuber and entrepreneur Mellow here again and I’m back with another blog post in my YouTube and AI Series where I investigate the many new innovative ways that you can use to leverage AI tools to automate the YouTube creation process and master the algorithm.

If you believe in free will, we have terrible news — well, at least when it comes to YouTube. Because YouTube’s algorithm for recommendations drives 70% of what people watch on the platform.
Seventy percent! That is some seriously staggering influence. YouTube’s 2.5 billion users watch 694,000 minutes of video each minute. And the platform’s recommendation system is responsible for the majority of those views.
So, it’s no surprise that marketers, influencers, and creators are obsessed with unlocking the secret of the YouTube algorithm. Getting recommended to the right viewers at the right time is the ticket to YouTube stardom, but how does it work? What makes it tick? And, most importantly, how can we take advantage of this mysterious formula?
Well, ponder no more, my ‘Tube curious friends because in this blog post, we’ll cover everything about the YouTube algorithm that you’ve been dying to know.
What the algorithm is (and isn’t)
The most recent changes to the YouTube algorithm
Pro tips for getting YouTube’s search and discovery systems to work for you
Bonus: Download the free 30-day plan to grow your YouTube following fast, a daily workbook of challenges that will help you kickstart your YouTube channel growth and track your success. Get real results after one month.
A brief history of the YouTube algorithm
What is the YouTube algorithm? To answer that question, let’s do a quick overview of how YouTube’s Algorithm has changed over the years. And how it works today.
2005 – 2011: Optimizing for clicks & views
According to founder Jawed Karim (a.k.a. the star of Me at the Zoo), YouTube was created in 2005 in order to crowdsource video of Janet Jackson and Justin Timberlake’s notorious Superbowl performance. So, it makes sense that YouTube’s algorithm started off by recommending videos that attracted the most views or clicks.
Alas, this led to an increase in misleading titles and thumbnails (a.k.a. clickbait). User experience plummeted as videos left people feeling tricked, unsatisfied, or plain old annoyed.
2012: Optimizing for watch time
In 2012, YouTube adjusted its recommendation system to support time spent watching each video. It also included time spent on the platform overall. When people find videos valuable and interesting, they watch them for longer. Or so the theory goes.
This shift to reward watch time was a game changer. According to Mark Bergan, author of Like, Comment, subscribe: Inside YouTube’s Chaotic Rise to World Domination, “[Watch time] had an immediate impact. Early YouTubers were basically making TikTok videos…but watch time created gaming, beauty vlogging, alt-right podcasts… all these verticals we now associate with YouTube.”
Accounts that were big performers previously (like videos from eHow, or Mystery Guitar Man) dropped off almost immediately.
YouTube’s algorithm change led some creators to try to make their videos shorter in order to make it more likely viewers would watch to completion. Others made their videos longer in order to increase watch time overall. YouTube didn’t comment on either of these tactics and maintained the party line: make videos your audience wants to watch, and the algorithm will reward you.
That said, as anyone who has ever spent any time on the internet knows, time spent is not necessarily equivalent to quality time spent. But then in 2015 YouTube changed tack again.
2015-2016: Optimizing for satisfaction
In 2015, YouTube began measuring viewer satisfaction directly with user surveys. It also prioritized direct response metrics like Shares, Likes, and Dislikes (and, of course, the especially brutal “not interested” button).
In 2016, YouTube released a whitepaper describing some of the inner workings of its AI: Deep Neural Networks for YouTube Recommendations.
In short, the algorithm had gotten way more personal. The goal was to find the video each particular viewer wants to watch.

This is where we stand for now regarding The YouTube Algorithm and to be honest it’s probably another update coming soon, we’ll just have to wait and see, I hope this blog post was helpful and I have helped you understand what the algorithm wants from you so you can be more effective and produce content that gets views and subscribers.

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