Teach Algorithms To Give You MILLIONS OF STREAMS While You Sleep

Summary

This 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

00:00

🎶 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.

05:01

🔗 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.

10:05

🚀 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.

Keywords

💡Algorithmic jail

The term 'algorithmic jail' refers to a situation where an artist's content is not being recommended or surfaced by platform algorithms, leading to limited visibility and growth. In the video, the speaker discusses how understanding the workings of algorithms can help artists escape this 'jail' by making strategic connections and optimizing their content for algorithmic recommendation.

💡User modeling

User modeling is a technique used by algorithms to create profiles of users based on their interests and behaviors. The video explains that algorithms use this data to suggest content, such as music, to users by finding similarities with other users who follow the same artists. This concept is central to the video's theme of how algorithms can be leveraged for growth.

💡Music genomes

Music genomes involve the analysis of a song's sound and rating it based on various markers. This helps algorithms match songs to users' moods and preferences. The video uses the example of a dance party to illustrate how music genomes can prevent mismatches in song recommendations, ensuring that the listening experience remains enjoyable.

💡Algorithmic connections

Algorithmic connections refer to the relationships that algorithms establish between users and content or between different pieces of content. The video emphasizes the importance of building these connections to increase the likelihood of an artist's music being recommended by algorithms. This is achieved through engagement with similar artists and content.

💡Clout bombing

Clout bombing is a marketing strategy where multiple artists or influencers come together in a single event or post, which can lead to increased visibility and algorithmic recommendations. The video describes how this strategy can create a 'critical mass' of connections, resulting in new followers and algorithmic growth for all involved.

💡Recency

In the context of the video, 'recency' refers to the importance of maintaining current and regular connections with other artists and content to stay relevant in algorithmic recommendations. The speaker suggests that consistent engagement with the community is crucial for ongoing visibility and growth.

💡Collaborations

Collaborations, such as features, remixes, and split releases, are highlighted in the video as a powerful way to build algorithmic connections. When artists collaborate, their fan bases can cross-pollinate, leading to mutual growth as the algorithms recognize and recommend the connected artists to each other's audiences.

💡Micro genre and niche

The video encourages artists to identify their 'micro genre and niche' to better understand their target audience and the community they should be connecting with. This involves recognizing the specific sub-genre or style of music they create and the unique aspects that define their artistry.

💡Engagement

Engagement, as discussed in the video, is a key factor in algorithmic recommendations. It includes actions like commenting, video replies, and hashtag usage that signal to algorithms the relevance of an artist's content to a particular community. The video suggests that active engagement can help artists 'teach' the algorithm about their community and improve their chances of being recommended.

💡Compounding effect

The compounding effect in the video refers to the cumulative growth that can occur when an artist consistently builds algorithmic connections. As these connections multiply and reinforce each other across platforms, the artist's visibility and reach can grow exponentially, leading to a snowball effect of increased recommendations and fan base expansion.

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.