- Hey, insiders, Rachel here.
And this is the series where I'm talking about
how our recommendation system works.
In this video, we're going to cover what factors influence
how many impressions a video receives,
or how many people are shown
and offered up the video at the end of the day.
So let's get to it.
What factors influence your impressions?
I'm going to touch on three of them today.
The first is personalization,
for how YouTube personalizes recommendations,
and make sure that recommendations are relevant
for a given viewer.
So I'm only gonna touch on performance,
which I think lots of creators are already familiar with.
So this is how a video performs
in the context of shown in discovery.
So things like click-through rate, average view duration,
average percent viewed, likes and dislikes,
all of those signals.
Then there are factors outside of our recommendation system
which I'll also touch on, things like how many viewers
are interested in a given topic,
how a video is performing relevant to its competition,
or all other videos that could be offered a viewer.
And then seasonal trends
like how many viewers are on YouTube in a given timeframe.
All of these factors combined
influence how many impressions your videos get,
and how many people see your videos.
So let's go through them.
Okay, let's start off with personalization.
So as I said, if we only use performance
like click-through rate and average view duration
to inform recommendations,
everybody would see very similar videos on YouTube,
and that's not what the site is designed to do.
We wanna make sure that recommendations are personalized
and relevant to each viewer.
Now, the way we do that are mainly
by looking at three types of signals.
The first is a user's watch history.
So what videos have they enjoyed in the past?
Understanding that will help us offer more videos
we're likely to enjoy.
But we don't wanna just offer stuff
that they've already watched,
we also want viewers to discover
and explore new topics and interests.
And the way that we do that is understanding
what videos are often watched together?
So imagine that two users have video A
in their watch history, but one user has video B,
that the other user hasn't seen yet.
Video B might be a really good candidate
to recommend to the viewer who hasn't yet seen it.
So, using what videos are often watched together
helps us identify videos that a user
might not have already watched or been exposed to yet,
but might be interested in.
The last one is understanding how much of a channel
or topic a user watches,
and how their interests might be changing over time.
That helps our systems identify
what to recommend them more or less of.
Every user has favorite channels that they watch.
The ranking of what they watch most will change over time,
depending on how their interests shift.
This is a really important point.
I brought in a gaming example
because this is the industry I came from,
but it is important to note
that viewers interests will change over time.
If you go back five years before today,
just think about what people were watching on TV.
We have different favorite TV shows.
We wear different clothes.
We have different favorite celebrities.
We play different video games.
We eat different foods.
People's interests change,
and it's important to evolve your content over time
along with people's interests.
And so in this example that I'm showing, for many years,
Minecraft was the game that everybody played
and it still is.
My cousins still play it all the time.
But the video game landscape changed
where you have a new game in the mix,
Fortnite, in and around 2018.
And as people's interests change,
as the games that they play shift,
so do the videos they watch on YouTube.
And Google trends is the most powerful tool
to see how viewers interests
in different topics are changing over time.
And no matter what category you're in,
I think that it's extremely valuable
to look at Google trends and understand
what's happening overall in the world,
and how people's interests change
because as people's interests change,
so do the videos they watch, which is why
it is absolutely essential to always keep experimenting.
If you make the same content, at some point,
people's interests will start to shift,
and will start watching more of something else.
And so, constant experimentation is important
to really maintain your scene and your audience
to evolve along with their interests.
If you think about celebrities
that have maintained their fame
throughout the course of their lives,
they had to reinvent themselves many times.
So the example that I sometimes give is David Bowie.
How many personalities did he have?
So many.
So my advice for creators is,
always keep experimenting to find a new thing.
And if you experience a long drop in impression,
so over the course of multiple months,
three plus months, and you're releasing the same content,
that's usually a sign that it's time to try something new
because if you're dropping,
there's only one of two things happening,
your viewers are on YouTube
but they're watching more of other stuff,
or they're spending less time on YouTube.
And so, most of the time it's the former,
it's that their interests have shifted.
So identifying when that's happening on your channel
is really important
because that's usually time to pivot,
experiment with something new.
So that's personalization.
Now let's touch on performance.
This is how does our system learn
about how a video is performing when it is recommended
to a given viewer?
So our recommendation system learns from signals like,
what viewers do when they're shown
or offered a video, that appeal.
So we look at when a video is offered to a viewer
and recommendations on home are suggested,
do they click to watch?
Do they ignore the video and choose to watch something else?
Do they click not interested?
This can tell us a lot
about the initial appeal of the video to the viewer.
Then once the viewer is in the video,
we look at how engaging it is.
So, did they watch it through?
That's average percent viewed.
How much of the video did they watch?
Or how much time did they spend watching?
The average view duration.
We use a combination of relative and absolute retention
to determine if a video is a good recommendation.
And then lastly, satisfaction.
So once they've watched the video,
how did they feel about it?
There we use the combination
of signals that I touched on in episode one,
which are user surveys that we often serve saying,
what did you think of this video?
And users can read it, and then signals like sound effects.
Lastly, I'm gonna call these external factors.
They're not objectives of our recommendation system,
but they also influence how many people see your videos.
So I think it's really important to touch on them.
The first one is topic interest.
So, it was important for creators to know
if different topics have different potential audience sizes.
And if you think about, if you're starting a business,
some businesses have different potential market sizes
than others that are larger and smaller.
So some are more niche interest.
A video I was recommended once
'cause it was relevant for me,
but probably it would be a niche interest topic
is a lecture about Honduran emerald hummingbirds.
So some topics are just gonna have smaller
potential audiences than others.
They're not necessarily relevant to recommend
to all viewers on YouTube.
And so when you're looking at your own content,
think about which videos might be more broad
or niche appeal than others?
Different topics are gonna have different
potential audience sizes,
and that will influence the impression.
Then competition, competition is interesting
because I don't think creators think about it very much
because when you are comparing your performance,
you're always comparing against yourself
because that's the data that you have access to.
But you're looking at how did my video perform
relative to other videos on my channel?
But what's important to know is that
our recommendation system
is not ranking you against yourself,
but it's ranking your video relative
to all other videos that a viewer might watch.
And so, when we're determining what are the best videos
to show on somebody's homepage, for example,
we're narrowing that down from the potential
two billion plus videos that are on YouTube
that could be shown again in viewer,
and what are the best recommendations for them?
And so, competition is important to understand,
because if your video is sometimes
not receiving as many views as impression,
but your click-through rate
and average view duration look high,
a lot of the time when that's happening,
it's because your video is not performing as well
relative to other videos on YouTube, outside your channel.
So, competition is something that impacts
how many people see your videos
and how much time a viewer spends
with content outside your channel.
If they're watching more of other channels,
that can mean they watch less of yours.
Or conversely, if they spend less time
watching other channels and more time watching yours,
your videos might rank higher for that individual viewer.
So, how content is performing outside your channel
also impacts how many impressions your videos receive,
or how many people are showing your videos.
Lastly, we have seasonality.
There's two flavors of seasonality.
You have the first one, which is how many people
are on YouTube during different times of year.
And that can change.
So, major fluctuations we see during holidays.
So, a period where a lot of people
might not be viewing YouTube,
and they're spending time with their families,
could be like New Year's or the end of Ramadan,
sometimes we see drops in and around eating
back to school in mid August.
A lot of viewership on channel starts to drop
and then spikes on Saturdays and Sundays,
'cause that's when viewers are then watching YouTube,
their schedules and behavior has changed.
So, how many people are actually
on YouTube to recommend videos to,
that shifts during different times of year.
That will impact how many people see your videos as well.
The second type of seasonality is topic related seasonality.
So, that is how interest changes in given topics
during different times of year.
So if you upload a camping video in October,
there's not as many people watching that kind of content
as there would be in August.
So if you uploaded it in August,
your video would probably do better.
I did a whole video on seasonality,
which I can link to in the description,
but just know, from this section,
that recommendations is not just based on performance
or your click-through rate and average iteration.
The other things that influence your impressions
are how broadly appealing the topic is,
or how big the potential audience size is for a given video.
How videos outside your channel are performing.
Because again, your video is being ranked
relative to all other on YouTube,
and seasonal fluctuations,
like how many people are on YouTube
to recommend the video to and how big the interest is
in that topic during that time of year.
That's it for part two.
In part three, we're gonna cover
10 frequently asked questions
about the recommendation system.
I hope that was helpful.
As always, keep it real.