- Hey insiders, it's Rachel here
and I'm a product manager on YouTube recommendation system.
I've spent the last few years working on YouTube analytics
and my job was really to help creators understand
their video performance.
Today, I wanted to share a presentation
that I normally give at conferences
that helps explain how the recommendation system works
and what factors influence
how many impressions your videos receive.
I hope it's helpful.
Let's get into it.
Okay, so in this section, we're gonna cover in general
how the recommendation system works
and what it's designed to do.
If you learn anything from this presentation,
I hope it's that you do not need to be an expert
in algorithms or analytics to be successful on YouTube.
You just need to understand your audience.
So to start, let's talk about the goals
of the recommendation system,
those have always remained the same.
The algorithm is designed to do two things,
match viewers with videos
they're most likely to watch and enjoy
and then hopefully, recommend videos that make them happy.
So we really maximize long-term satisfaction,
so our viewers keep coming back to YouTube
because they know that they'll find videos
that they'll like there.
Now, although our goals have always been the same
what we optimize for has changed over time.
If we go back to 2011,
we were optimizing for clicks and views,
and this was not unique to YouTube.
I think many platforms started recommending videos
that people were just likely to click into.
But that's not that great of a metric
because it may indirectly incentivize click baity
or sensational titles, or thumbnails
that get people into watch a video
but it doesn't make them very satisfied or happy.
And we received a lot of your feedback
in and around the 2011 time period
that a lot of the viewers home pages or recommendations were
these sensational off-putting videos.
And so to help correct for that,
we moved towards watch time.
And how much time somebody spends watching a video
or a channel is much more indicative
of the quality of the content
because if you spend more time watching something
it is more likely that you're gonna be interested in it,
but watch time is not a perfect metric either.
So, I'm sure you've all seen time
maybe binging through a series that you watched hours of
but you still didn't feel that good afterwards.
And so what we wanted to identify is
how do we define quality or valued watch time?
And so, what we wanted to optimize
more towards is satisfaction.
And the way we do that is directly asking viewers about
what videos they like and enjoy through surveys,
and then actually optimizing for what they like.
And not only is satisfaction important but we've also added
more and more responsibility objectives
because especially in spaces
like news or medical information, it's really important
that we raise authoritative voices and content
and reduce the spread of any borderline violative content
that could harm viewers or contribute
to the spread of misinformation.
So if you fast forward to today,
what we actually optimize for in discovery
is much more satisfaction related goals.
Like what videos do viewers regularly watch
but make them happy as well as making sure
that we're doing it responsibly.
Okay, so if I just spoke all the time about satisfaction
and you're like, "Well, how do you
"actually measure satisfaction?"
We serve out millions of surveys per month to viewers.
Now, they're not served on every single video.
The viewers don't fill this out for every video they watch,
we don't wanna overwhelm viewers with surveys.
But we use this data to train prediction models
to identify which videos are highly satisfying.
So an individual viewer may only receive one
or two of these surveys per month,
but the main point you need to understand is
that we no longer consider all watch time
on YouTube to be equal.
We really focus on satisfaction.
Surveys are one of the most important pieces of data
that we use to train this.
We don't yet share this data with creators today
because we often don't have
enough significant data on an individual video
for you to really make significant conclusions around it.
So sometimes an individual video
might only receive five responses.
Now that information is valuable to us
because we can still train our prediction models
and be like, "Okay, identify stuff
"that's satisfying in general."
But for an individual creator sometimes
that's not enough data to act on.
We are looking at adding more satisfaction data
and externalizing it to creators.
So it is something we're working on.
But surveys aren't the only piece of data
that we look at for satisfaction.
So in addition to that, we also use signals from viewers
like not interested feedback.
So when videos are recommended to viewers on the homepage
or suggested do they click not interested?
We also use signals like likes and dislikes and shares
to determine whether a video is satisfying.
Throughout this presentation
I'm gonna use the word recommended or recommendations a lot
and I wanted to clarify what I mean when I say that.
So recommendations when I say that,
I'm referring to the home page and suggested
or the videos that are algorithmically offered up to viewers
that they may want to watch on the page
when they open youtube.com or the mobile app which is home
or the recommendations that are shown alongside the video
the viewer is currently watching.
So that's what suggested is,
so suggested on desktop is like the panel on the side
or the videos beneath the, when you're watching on mobile.
Now, creators often refer to the algorithm.
We actually have different systems depending
on where users are watching videos on YouTube.
Like the homepage offers up a broad array of videos
when you visit youtube.com
and it uses similar signals as suggested,
but they are designed to do slightly different things.
So, the algorithm is a bit of a misnomer
'cause we actually have multiple systems
that are designed to do different things
depending on if the viewer is on home, suggested or search.
But when I say recommendations,
I'm talking about homepage suggested.
This is how the majority of viewers watch
and discover videos on YouTube.
So it's likely, but these are the top traffic sources
for your channel, but that's of course not always the case.
A lot of creators ask, how do I optimize my content
so I get more views from homer suggested.
And the difficult answer is you can't,
you can't optimize for a traffic source.
You can only optimize for people or viewers.
But I will describe how they work
and some general things you can do
to optimize to build an audience.
So let's start with the homepage.
Home offers up a broad array of videos
that viewers are most likely to watch
when they visit YouTube.
And at that stage, we don't have a lot of information
about the intent the viewer might have
when they're coming to YouTube,
maybe they're coming here to learn something
or maybe they're coming here to be entertained.
So home offers a pretty diverse array of recommendations.
And the way it ranks videos
is primarily based on two main categories.
The first one is performance.
So that means the signal
that you're probably already familiar with,
like click through rate, average view duration
and if the viewers are happy.
So performance is really looking at how a video performs
when it is offered to viewers on home.
So in the context it's shown.
So the homepage is really learning
from when videos are recommended to viewers on home,
what do they do with it?
Do they click to watch?
Do they click not interested?
Do they ignore it and click something else?
In addition to that, we have personalization.
So if we only use performance like click through rate
and average view duration to inform
which recommendations were good for viewers,
YouTube would just be like a giant explorer
or like trending tab, which is not the case.
It's all personalized for the viewers own interest.
And so we use a combination of performance
and personalization and personalized factors
or like how much that individual viewer watches a channel
or topic over time, or their recent watch history.
That also helps inform,
is this a good relevant recommendation for that viewer?
Now again, there's nothing you can specifically do
to get more views from home but you can look at your content
and think what would a new viewer
who has never been exposed
or watched any of the content from my channel,
what would they do if they saw this recommendation?
Like really look at your title and thumbnail
from the eyes of a new viewer.
This is where a lot of content is discovered
for the first time was on the homepage.
And so be sure to look at your content
and think what a new viewer click to watch
if they had otherwise never been exposed to this content.
So maybe you have a video that's like
it's my mom's birthday, if you're a vlogging channel,
that might be relevant to your core audience
but if we recommend that to a new viewer on home
they might be likely to ignore that recommendation
and click something else
because it doesn't mean something for them.
Okay, moving on to suggested.
Now, suggested is designed
to offer the best videos that if you were as most likely
to watch next after the one they're currently watching.
So suggested to rank videos using a lot of signals
such as videos that are often watched together
this helps us identify videos that you might not have seen
that are relevant to your interests,
topically related videos but also they're not just videos
that are relevant to the one you're currently watching,
they're also based on your past watch history.
They're both personalized and a little bit more related.
And suggested, the recommendations are usually
a little bit narrowed down compared to the homepage
because we already have an idea of what kind of session
the viewer is having at that point.
Again, can't optimize for suggested
but you can optimize to keep people watching your channel.
The most effective thing that I've seen creators do
is of course develop series or topically related videos.
Those videos are more likely to be recommended together
because people are more likely
to watch them in the same session.
So you can imagine if I'm watching a video
about antique music boxes, I'm more likely
to get more music box videos
in the panel on the side or in suggested.
So, if you develop a series that is really consistent
or has commonalities between the content,
it is more likely to be recommended together,
viewers are more likely to watch them one after another.
And so series is one effective way to keep people watching,
having a consistent title and thumbnail style is another.
So you can imagine when a viewer is looking
at everything that they could choose to watch next,
there are a lot of options there.
And if you have really strong identifiable branding
that's consistent, it's really easy to pick out
which videos are from your channel
and it just makes such decision all the quicker for viewers.
And of course, there's other stuff you can do,
you can do call to actions to watch more.
If you liked this video, I think you'd be interested
in another one on my channel.
And we have playlist and end screens and those work too.
Okay, another question I often get from creators
is should I worry or be concerned about
if a video has more or fewer views
from a specific traffic source?
And I wanted to reassure creators
that this is completely normal.
Viewers discover videos in different ways
and that's expected.
So for example, learning or how to videos.
They often get more views from search
because that's how viewers try and find learning videos.
Like we don't know to recommend a video
to them when they visit YouTube
because we don't have any signals
at that point that they are there on YouTube
to learn how to build a work bench.
So, how to content often gets more views from search.
Music on the other hand often gets more views from playlist
or mixes because that's how people listen
to music on YouTube is often through autoplay or playlist.
News content often gets more views from the homepage
because we have a breaking news shelf there
and that's how a lot of viewers
who are interested in news discover content.
And so if you see different traffic source mixes,
video to video, don't worry about it.
It's completely normal.
It depends on the type of content
and how the viewer really is engaging
with this type of content on YouTube,
but just know that viewers discover videos in different ways
and that's completely normal.
So if you learned anything from this section,
I hope it's that you understand
that YouTube's recommendation system is designed
to find videos for viewers and not viewers for videos.
And what I mean by that is,
sometimes creators have a perception
that the recommendation system pushes out
or promotes videos to viewers.
And in reality, the system is designed
to work the opposite way where a viewer visits youtube.com
and then our recommendation system pulls in
and then ranks the best candidate for that viewer
depending on the page that they're on.
And so, it is a viewer focused system.
So that's all for this first video.
In the next one, we're gonna cover what factors influences
how many impressions your videos receive
and how many people see your video at the end of the day.
As always, keep it real.