Teach Algorithms To Give You MILLIONS OF STREAMS While You Sleep
Summary
TLDRThis video script offers musicians insight into how algorithms on platforms like Instagram and Spotify work, explaining user modeling and music genome analysis. It advises artists to understand these algorithms to grow their audience, suggesting strategies like engaging with similar artists, using effective hashtags, and collaborating to build algorithmic connections. The script also touches on the importance of recency in connections and the power of features and collaborations for algorithmic growth.
Takeaways
- 🎶 Algorithms on platforms like Instagram and Spotify recommend artists based on user modeling and music analysis.
- 🔍 User modeling involves creating profiles of users and their interests to suggest similar artists.
- 🎵 Music genomes analyze songs for sound and rate them on various markers to match user moods and preferences.
- 🚫 'Algorithmic jail' refers to being stuck without algorithmic recommendations due to lack of user engagement or connections.
- 🔗 Building connections with other artists through collaborations, features, and remixes can boost algorithmic visibility.
- 📈 Regularly engaging with similar artists and using relevant hashtags can signal to algorithms that you belong to a particular community.
- 📊 Recency of connections matters; consistently making new connections helps maintain algorithmic relevance.
- 📈 Clout bombing, where multiple artists are featured together, can create a surge in algorithmic connections and fan engagement.
- 🎧 Platforms analyze user behavior to recommend artists, so engaging with content from similar artists can influence recommendations.
- 📝 Understanding your micro-genre and niche is crucial for effectively targeting and connecting with the right audience and artists.
Q & A
What is 'algorithmic jail' mentioned in the script?
-Algorithmic jail refers to the situation where an artist's music is not being recommended or discovered by algorithms on platforms like Instagram, TikTok, and Spotify, despite the artist creating good music and engaging content.
How do algorithms recommend artists to users?
-Algorithms recommend artists through two main methods: user modeling, where they create profiles of users and their interests to suggest similar artists, and music genomes, which analyze songs for sound and rate them on various markers to match with users' moods and preferences.
Why is it important for musicians to understand how algorithms work?
-Understanding algorithms is important for musicians because it helps them strategize how to be discovered by more users. It allows them to create content that aligns with what algorithms look for in terms of user engagement and music characteristics.
What is the role of hashtags in helping an artist's music get discovered?
-Hashtags play a crucial role in helping an artist's music get discovered as they provide context to algorithms about the content and its potential audience. Using relevant and popular hashtags can increase the visibility of an artist's content within the algorithm.
Why should musicians collaborate with other artists?
-Collaborating with other artists helps musicians create stronger algorithmic connections. When artists collaborate, they appear on each other's pages, which can lead to mutual fans and increased recommendations by the algorithm.
How can musicians build connections with other artists on platforms?
-Musicians can build connections by engaging with similar artists on social media, using relevant hashtags, collaborating on projects, and participating in community activities. This engagement signals to algorithms that the artists are part of the same community.
What is the significance of 'clout bombing' in the context of algorithmic growth?
-Clout bombing is when multiple artists or influencers come together in a single post or event, which can lead to increased sharing and tagging, thus deepening algorithmic connections and potentially ramping up new followers for all involved.
How can musicians ensure their music is recommended in algorithmic playlists?
-To be recommended in algorithmic playlists, musicians should focus on creating music that fits well within specific moods or themes, collaborate with popular artists, and consistently engage with their community to build strong algorithmic ties.
What is the impact of recency on algorithmic recommendations?
-Recency matters because algorithms prioritize recent connections and interactions. Artists need to continually create new content and engage with their community to maintain and grow their presence in algorithmic recommendations.
How can musicians leverage their presence on short-form platforms like TikTok?
-Musicians can leverage their presence on short-form platforms by creating engaging content that includes their music, using trending hashtags, and interacting with other artists and users. This helps in building a strong community and getting recommended by algorithms.
Outlines
🎶 Understanding Algorithmic Recommendations for Musicians
The paragraph discusses the frustration musicians face when their growth seems slower compared to others, despite creating quality music. It introduces the concept of 'algorithmic jail,' where artists are not recommended by algorithms due to a lack of understanding of how they work. The speaker promises to explain the basics of algorithms, focusing on user modeling and music genomes. User modeling involves creating user profiles based on their interests, while music genomes analyze song characteristics to match them with listener preferences. The paragraph emphasizes the importance of understanding these mechanisms to gain algorithmic traction.
🔗 Building Algorithmic Connections through Clout and Collaborations
This paragraph delves into strategies for building stronger algorithmic connections. It discusses 'clout bombing,' where artists increase their visibility by appearing together, thus deepening algorithmic ties and attracting new followers. The importance of recency in connections is highlighted, emphasizing the need for continuous engagement with other artists. The paragraph also underscores the value of features, collaborations, remixes, and split releases in growing an artist's reach algorithmically. These activities allow artists to live on each other's pages, converting fans and building long-term connections that can lead to algorithmic recommendations on various platforms.
🚀 Leveraging Algorithmic Growth through Consistent Promotion and Community Engagement
The final paragraph focuses on the compounding effect of good algorithmic connections, such as those gained through tours, collaborations, and consistent mutual promotion. It explains how new fans, who already have established connections with other artists, can boost an artist's recommendations. The paragraph also touches on the importance of engaging with similar-sized artists, using hashtags, and creating content that teaches the algorithm about the artist's community. It concludes with advice on finding a micro-genre and niche, engaging with similar artists, and the necessity of understanding how to grow on YouTube for further expansion.
Mindmap
Keywords
💡Algorithmic jail
💡User modeling
💡Music genomes
💡Algorithmic connections
💡Clout bombing
💡Recency
💡Collaborations
💡Micro genre and niche
💡Engagement
💡Compounding effect
Highlights
Feeling terrible about slower growth despite making great music could be due to not understanding algorithmic rules.
Algorithms can be surprisingly easy to understand, contrary to popular belief.
User modeling is a fundamental way algorithms recommend content based on user profiles and interests.
Music genomes analyze songs to rate them on various markers for mood and recommendation matching.
Being in 'algorithmic jail' often means the algorithm lacks hints about who to serve your content to.
Using ineffective hashtags or following irrelevant accounts can hinder algorithmic growth.
Building connections with smaller, growing artists can increase algorithmic recommendations.
Engaging with hashtags and artists similar to your niche can improve algorithmic visibility.
Clout bombing, or grouping with other artists, can deepen algorithmic connections and grow followers.
Recency of connections matters; consistently build new connections to stay relevant in algorithmic recommendations.
Collaborations and features create strong bonds that grow algorithmically as the connected artists grow.
Genomes help streaming platforms understand why users skip songs, improving mood-based playlist recommendations.
AI detections in videos can serve users similar content, creating a feedback loop for algorithmic learning.
Compounding algorithmic connections through consistent promotion and collaboration can lead to rapid growth.
Finding and engaging with artists of a similar size is crucial for building a strong algorithmic presence.
Understanding your micro genre and niche is essential for effective algorithmic growth and community building.
Consistent engagement with your community and similar artists is key to teaching the algorithm about your content.
Transcripts
I bet you've looked at another musician or like I make better music than them. And yet they're
growing way faster than you. And it probably makes you feel terrible. But in my experience,
there's so many of you who are making great music. We're also
good at making Tik Toks or reels. And yet you're still in algorithmic jail.
This is probably because you're playing a game that you don't understand since
how are you supposed to win a game? You don't get the rules up. And those
rules are how algorithms work. But you don't need to be some Neo like Matrix
seeing nerd to get that. It is actually shockingly easy to see how it all works.
Hell, even a musician who makes country rap could understand it. So in this video,
I'm going to show you how to make connections to other RSO algorithms,
start to recommend you. Instagram, and all the other platforms. So you can figure out
what you're messing up and let the algorithm grow your music. So let's start off here.
Algorithms are actually shockingly easy to understand. There's two ways they figure out how
to recommend you. The first is user modeling. This is basically. Really simple. What these algorithms
do is they make profiles of users of the platform and their interests. So let's say you follow 10
artists. They find people who also follow those 10 artists, and then they suggest the other artists.
Those people follow to you and vice versa. It's a hair more complicated and smart than that, but
I'll get into all that a little later. The second is music genomes. This is where the algorithm
analyzes your song, um, For sound and then rates it on a variety of different markers. This is
helpful since it can match it to people's moods and it helps to discriminate recommendations.
Since think of it this way, let's say you're having a dance party. You wouldn't want to
be listening to say LCD sound systems, upbeat, Bops. And then I have that song,
someone great come on, you know, where he's talking about deaths and funerals and all
these things. And all of a sudden the party is fighting to get in the bathroom to end it all
from the miserable depression from putting on one of the most depressing songs of all time.
Or if you're listening to some classic outcasts. to set the mood. And you're like, Hey, yada,
your date. And then all of a sudden Andre 3000's new ambient flute album comes on and suddenly
your date is asleep. Like me. When I listen to that record, music genomes basically allow the
algo to be a bit more smart and not bring down the mood solely based on artists connections.
Since after all artists have multitudes of moods,
but what so many of you don't get is oftentimes when you're in algorithmic jail, it's because
the algorithm has absolutely no clue who to serve you to, because you haven't given it any
hints. And yet you're blaming the algorithm. Everything about pointing that finger inwards.
I mean, really, you're not even, like, giving it any good hashtags. You only
follow your cousin Pauly, who posts about his pickleball league all day and not music,
and your friend Norman, who just creeps on barely of age girls all day and uses
the word princess way more than any man who isn't playing Legend of Zelda all day should.
Or just as bad, you're using ridiculous hashtags like best new music and hashtag music video that
tell the algorithm nothing about the people who are most likely to be your fans. So let's
talk about making connections so you can win this game. So because these companies mostly
model behavior off of other users, a lot of what they do is keep a running score.
Of how many connections that user has to that artist. So if you listen to say, Playboy Cardi,
you probably already listened to these five other artists. Everyone else like
you does too. But if you are not listening to one of those five artists, that artist
is going to get recommended to you, since it seems like you will be likely to like them.
To measure this, the platforms are always keeping a score of your connections to
other artists. And you know, When people engage with the content of those other
artists as a marker to know who they should be recommending. This is why I constantly tell you,
you need to find smaller artists in your community so you can build connections with them.
Since your score is never going to be able to get high enough to be connected to Playboi Carti,
unless you really pop off. But a small artist that's building up and growing. You can get
connected to. So let's say you're tagging other growing artists who are around your
size on Tik TOK, you found one to three hashtags on Tik TOK, where if you do a search, you see
artists who have fans who would love you of a similar size and monthly listeners on Spotify.
And now you are interacting in that hashtag following the artists
and creators who post in it regularly. You post comments and even video replies. Well,
as long as your videos aren't trash, you should start making your way into
this algorithm. And if you're making videos with your songs earworm, you should then be
getting into the feet of people who like your music and getting connected to those artists.
And then the listeners will head to Spotify and listen to you and connect you to the best
possible music fans, the ones who are Listing a Spotify who already jumped over from TikTok, which
is going to really give you a lot of algorithmic connections. And now you have connections on two
platforms with the people who in this genre enough are scouring TikTok and jumping to Spotify who
really are the most passionate music fans, which is really going to get you off on a good foot.
But you know that concept clout bombing, you know, when you see a bunch of cool famous people or even
just A bunch of musicians all taking a picture together. This is so effective because people,
when they see a bunch of their faves in one pic, it often inspires them to share it,
make fun of it, make some snide remarks about it, but they tag all those people and starts
to deepen the algorithmic connection with all of them and kind of create a critical mass.
And this often ramps up a bunch of new followers for everyone involved in the
clout bomb. As people want to understand their favorite artists community. I've
typed this on the morning after the Grammys, you know, that cursed award
show where I am faced with a doom scroll of enough clout bombs to supply a whole war.
If I'm clout was a weapon. Wow. That joke, um, but really as the platforms
analyze when they see a spike of activity of new connections. They will often suggest
users to follow whoever is spiking in those connections to the followers of
the other people connected there. But it's also important to understand that recency matters.
Oftentimes this is about who you're connecting with in the present day and on regular basis.
Since a lot of the biggest artists have a lot of connections since they've been
making them over the years and they've been building up their algorithms. So
you have to continually be doing content that gets you connected to these artists.
Since over time new artists will come around and build connections to these artists and you won't
get a recommended unless you're continuing to make new connections. And continuing to grow.
But one of the reasons this is so effective is that when the artists you're connected to grow,
well, if you're connected to them, you get recommended continually while they
grow and vice versa, which is why you hear so many artists with millions of streams say
so much of it is algorithmic playlists, like discover weekly and release radar,
as well as those interest based algorithmically programmed ones.
But I also should say, whether you get on user playlists that fans make
or Or editorial playlists. One of the main benefits you get from these is that listeners
are listening to you alongside of a bunch of other artists and building algorithmic ties
to you. Which is often what does the best building for all the algorithmic playlists.
So everything here in the ecosystem matters. Since we have ties to these artists,
their growth is often your growth. We should probably talk about the
strongest bonds you can build. So it's obvious to see why one of the biggest music marketing
opportunities today are doing features, collaborations, remixes, and split releases.
Since when you do these, you live on the artist page and you go into the algorithm
with them and endlessly convert fans over as you're getting their
fans to listen to you and vice versa. As you spread around the internet and
your song goes and gets new listeners throughout the lifespan of this artist.
But also when fans share that artist, you get tagged with that artist continually racking
up even more connections. And when people are loving that song, they can have an easy hint
to go deeper on you and you're already part of a song they like and in the community with an
artist they enjoy. And if you missed my full video on that, I highly suggest you watch it
as it's in the description below, but truly the ties you get to the artists you collaborate with
help you grow algorithmically, whether it's on the YouTube browse page, Spotify radio,
discover weekly recommendations on Instagram and Tik TOK for years to come.
Which is why they are so huge and why everyone is investing in them with budgets. But let's
go over to those genomes we talked about before and talk about how they
figure in. Most of the platforms don't use genomes, but the ones who deal with sound
and recommendations often do. And since this is how some of the streaming audio sites get
more sophisticated in their recommendations, see against the algorithmically programmed
playlists like Furry Love or Skiing Orgy or whatever obscure thing Spotify makes you.
Well, those are a huge part of what you see in big artist streams these days. These genomes
need to know why people skip some songs from artists that they usually like the song of. And
this is where the genome comes in. If you go to musicstacks. com, that's a website that aggregates
Spotify's data. You can see some of Spotify's data on your song or any song for that matter.
And what we'll often see is Spotify is playing songs with similar scores and dance ability or
say instrumental ness. Since that'll be really high in an algorithmic playlist
like ambient music for dirty dorm rooms. You know, you rock that one on the reg, but these
algorithmic playlists are often playing similar things for people and keeping a similar mood.
Basically genomes are a check on that the artist isn't working in a different mood to make sure
that the algorithm itself doesn't mess up the vibe. But there's some small complications to
all of this. As the algorithms pick up so much today with AI detections,
the songs of the backgrounds of your video are like hashtags and they regularly serve
people use the same song to make videos to users who watch the video with that song.
The same goes for the words in your video. That are in the title or even what you put in captions
is they'll often try out similar subjects to people who've engaged with the subject
a lot. But let's get into the compounding effect of all this. When you set up all
these good algorithmic connections, let's say you're doing a tour and have a collab
with another artist or you two just constantly talk about each other and go live together.
If fans start to tag you two together, the algorithm on
each platform starts to recommend you more and more. And as you get new fans,
those fans have pre established connections to listening to and discovering other artists,
which you then start to get recommended with. And when each of these artists gets bigger,
if you're still connected to them, you will get recommended of their success and continue to grow.
This is why the artists who do what I recommend and continue to stress. Consistent sustained
promotion as well as collaborating really are the ones who grow fast This also brings
you to listeners attention on short form since if you're regularly getting tagged
with them The chances you come into a potentials fan algorithm grows more and
more and gets you connected to other artists and the algorithm Learns what to do with you,
but the way this compounds is when people are tweeting Instagramming And doing at
tags about your collaboration or the shared show you have or whatever you
did together that spreads from platform to platform because think about it this way.
If the fan of you on Instagram who likes other artists just like you
then jumps to Spotify to check you out and starts rinsing you. Well,
you get connections to all their other artists, and then they're going to follow those other
artists on Instagram and vice versa. And the circle of algorithms keeps recommending you.
But one of the things to keep in mind and why I love this so much is these are the most adamant
music fans who are often the ones who listen to the most music. And that's why they help you grow
so much more than buying ads. So to do this work effectively, you need to continually be finding
artists that are of a similar size or just a little bit bigger and connecting yourself to them.
If you do your community research and people enjoy what you do and are constantly
working to tie yourself to your community and actually pay attention to doing this at scale,
meaning you don't try to do this with artists with hundreds of thousands of
monthly listeners when you only have hundreds.
Well, this is going to give you a great algorithm that will hopefully love you. But you want to know
more specific on what you do each day to get this to happen. So let's get this out of the way. The
first thing you need to do is figure out your micro genre and niche. And for Christ's sake,
for those of you who make fun of how I say that word, both pronunciations are correct.
So turn in your grammar police badge to the local nerd station and take notes instead of coming for
me in the comments, you dorks. Okay, but first you need to know that micro genre and what you sound
like And if you don't know that, I have a video on how to figure that out in the description.
After that, you need to watch my video on how to find community and grab my free spreadsheet
to collect some information, which is also in the description, let's assume you've done that.
So now you have a list of tons of artists who are similar to you in sound and your niche
micro genre. And let's also remember. It's not just about sound. Sometimes you can even
add niche identities. This could be queer, or if you're Asian, or if you sing songs about hockey,
or other artists who don't necessarily sound like you, but you have something in common.
All of this can get put into an algorithm and get recommended upon in this day and age. So
let's say you got a hundred of those on a list now, because you really believe in your music,
and are hardworking, and really want to make your dreams come true. Now we want
to follow all those artists on Tik TOK, Instagram, and YouTube, as well as any
of the short form tech sites like threads, blue sky, or whatever we're calling Twitter.
Right now, you want to start engaging with these profiles and commenting and doing
video replies to teach the algorithm that you are in this community. Now watch the hashtags
these artists use and click on them. And search for them. When you see ones where
artists are similar to you, that's the ones that you should be using in your own videos,
but also regularly look at this search and comment in video reply to videos in this
and interact with the artists who are part of this community and really teach the algorithm.
These are the people you should be being shown to the fans of, but most of all,
Reach out to the artists you find, collaborate, do shows together, do things online together,
like going live and chatting. I don't know, get creative, but continue to get fans to have
conversations about you and with you and the other artists your fans like, who are on the way up.
And most likely you'll all build together and all will work out and you'll get out of
algorithmic jail and everyone will hold hands and everything will be great. And won't that
be wonderful. Okay. So here's the thing. While you just learned all about how algorithms work,
if you really want to grow your fan base, you need to understand how to blow up on YouTube,
which is on the video that's linked in the screen right now.
So make sure you watch that next. If you really want to level up. Thanks for watching.
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