Spotify

Four reflections on SoundCloud’s fan-powered royalties & the flaws of subscription models

Posted on March 9, 2021 | by Bas Grasmayer

SoundCloud is adopting the user-centric payment model, branding it ‘fan-powered royalties‘. I’m a proponent of the system and was even product director at a music streaming service that employed a user-centric model (IDAGIO’s ‘Fair Artist Payout Model’). Yet, recently I wrote a piece in which I worried about fan-powered royalties being a distraction for SoundCloud, so here are four reflections on the latest announcement.

[Out of the loop? Read my primer on user-centric payment models.]

#1 Recalculations of revenue distribution don’t tell the full story

I’ve sat in on more than a few discussions about user-centric payment models. These discussions often cite research papers that compare how the status quo of music revenues would differ in a user-centric model versus pro rata model. While it’s important to use the data at hand, this also causes a bias.

The streaming landscape, including the vast majority of all digital strategy employed by labels & artists, are based on a pro rata model. What types of services would succeed in a user-centric landscape? What type of strategies would emerge?

#2 SoundCloud should consider fan-powered royalties a platform pivot

The way music streaming services function is strongly influenced by their economics (funding models, user payments, rightsholder compensation models).

Music streaming services sell the catalogue to get new subscribers, who are treated as listeners first, fans second. “Music for every moment”: radio stations, curated playlists, autoplay – all ways to stretch people’s listening sessions to get more value out of the service & either subscribe or stay subscribed (or at the very least trigger more ads).

In a user-centric model, encouraging high listening diversity runs counter to artists’ interests. If I start a rap career (under an alter ego, to avoid another trademark claim) and I bring a fan to a platform because it’s user-centric, I do not expect that platform to then do everything in its power to make sure that fan listens to many other artists, thus diluting the value I get per fan. Rather, I’d expect the relationship to look a bit more like fan clubs (e.g. some crossover between the Patreon / OnlyFans walled garden model and streaming’s attention diffusion model).

In other words: SoundCloud should follow these fan-powered royalties with feature sets that make the platform more fan-centric.

#3 The flaw is in the subscription model, more so than the remuneration model

60,000 songs are added to Spotify daily. The democratization of music has been great if you think it’s important that more people than ever participate in the creation of recorded music. It’s also creating an increasingly competitive landscape.

What happens when the entire potentially addressable market has been sold streaming subscriptions? The pie stops growing. Long before that, the growth of that pie will slow down much faster than the number of new artists adding their music to services’ catalogues.

Streaming subscriptions are a dead end road. A user-centric model only rewards those artists whose fans don’t listen to a lot of music or are extremely loyal, thus maintaining a high “average listening-share per user“. User-centric models don’t generate more revenue.

One of the most insightful people about this problem, who regularly writes & speaks about it, is Mark Mulligan.

#4 Music streaming services need to become ‘music services’ with revenue models that scale vertically

I’m going to skip over some important nuance:

Streaming is not a feature. Music access wasn’t a problem. Piracy solved that. Legal music access was a problem. Rightsholder remuneration was a problem.

For nuance, read my 2016 piece “Streaming is not the future of the music economy“.

When you look back at the history of streaming services like SoundCloud and Spotify, you find an era with APIs that allowed external developers to tack on all kinds of additional experiences. Much of that has been shuttered (in part due to licensing agreements) and an assumption has emerged that music streaming subscriptions paired with the familiar UX conventions are the definitive model. They’re not.

Music streaming was only supposed to be the base layer. It’s a layer on which we need to build alternative revenue streams that scale vertically. I may be butchering economic terms here, so what I mean with that is this:

Music streaming has done a tremendous job at scaling horizontally: getting millions of people around the world to pay a flat monthly fee of $10, or the local equivalent. It has done a horrible job at scaling vertically: fans with more to spend basically go unmonetized.

Meanwhile, the model for artists still looks the same:

Make great music.
Grow your fan base.
Monetize your most limited resource.

That most limited resource is time. A live show is a limited event. A virtual meet & greet is limited. A livestream is limited. An autographed shirt or record is limited. An NFT is limited.

Streaming services, besides integrating merch, have done very little to create new revenue opportunities for artists. This is a failure of the landscape, rather than specific services. It’s hard to run a music streaming service: the economics are brutal. You have a high burn rate with upfront ‘minimum guarantees’ paid to rightsholders, you need to justify that burn rate to investors with fast growth, so streaming services tend to get locked into a single model.

The first link in this article, about SoundCloud’s fan-powered royalties, goes out to Fred Wilson‘s blog – an investor in SoundCloud. Hopefully it’s a signal of an understanding between SoundCloud’s leadership & investors that the company has to pivot from being a music streaming service, to being a music service that supports fan-centric business models.

That’s essential, because what happens now is that an expectation has been created with artists. They expect fan-powered royalties to work out better for them, but what’s the strategy to grow the pie?

Smarter Playlists: automate your music discovery, playlist strategy, and library organisation

Posted on February 2, 2021 | by Bas Grasmayer

Smarter Playlists is still the best way to ‘automagically’ create and update playlists on Spotify. The tool, made by Paul Lamere of music data firm The Echo Nest (now Spotify), provides you with countless ways to source music, combine it, filter it, sort it and turn it into playlists.

I hinted at the value of Smarter Playlists / Playlist Machinery when I wrote about playlist strategy in a previous post titled If you want to start a music brand, don’t wait until the pandemic is over. Here’s how to use it.

Music discovery

Not everyone needs a playlist strategy, but everyone reading this is crazy about music and always curious to explore more. Here are some examples of recipes that surface gems.

New Music Friday… but high-energy from around the world

Fridays are when new music is released and Spotify helps surface that new music in numerous ways. It has its the algorithmic Release Radar which lets you listen to tracks from artists you personally follow. It also has New Music Friday playlists for specific territories that are editorial and mostly pop-focused.

I love seeing how trends emerge and are adopted around the world and have a soft spot for high-energy music, so I created a weekly tool to scout new tunes.

A lot happening in this screenshot, so let’s break it down by steps.

Firstly, all of the data streams in from the left and streams out (to a Spotify playlist) on the right. In between, there are various steps which either combine data (e.g. tracks from different playlists), filter, or sort it.

First I added a number of Sources. The Sources are Spotify’s New Music Friday (NMF) playlists from various regions. You copy the playlist URI and add it to the box. I’ve changed the box names to the region it’s sourced from.
Since the international NMFs also tend to feature the world’s biggest pop stars, who I’m already familiar with, I took the global New Music Friday playlist (which has over 3M followers) and connected it to the mixer with a red line. This ‘bans’ all the tracks on the global NMF playlist and essentially filters out the global hits from progressing down the workflow, in case they’re present on any regional playlist.
Since I’m working with 7 input sources, I set the mixer’s max tracks to a few thousand. Otherwise it clips to a low number by default.
It’s Friday – I want energy (tbh, I always want energy). So I took the energy filter and set it to ‘most energy‘. This filters out all tracks that are not energetic.
Next, I’ve sorted the stream by artist popularity and picked ‘reverse’, so that the most popular artist shows up on top of the list. This is counter-intuitive, but it makes sense if you dive into how they rank artist popularity numerically. I do this, because if people visit the playlist and play track 1, it makes it more likely it fits current trends and expectations and people are less likely to move on to another playlist.
But life shouldn’t be too predictable. So I’ve used ‘weighted shuffle‘, which lets you set the degree to which you want the list to be randomized. If you want things to be roughly in order of popularity, you set it to 0.1.
In the above recipe or formula, I save the output to an existing Spotify playlist in my collection. I’ve chosen to overwrite, but you can also select to append. Additionally, you can choose to create a new playlist altogether.
Hit the play button to run your workflow, check if the output makes sense in the Tracks tab and also check your Spotify library for the playlist.

👉 Playlist | Program

I’ve used the scheduler to update it weekly, because I was happy with the result and I imagine I can build a following with the playlist. You can find the scheduler by going to the Program section after saving your playlist recipe.

Scout labels’ playlists for unknown talent

Labels usually have regularly updated playlists which showcase their new releases. If you’re curious about musicians that are less well-known, you can set a filter that removes all tracks by artists that are too ‘popular’ (according to Spotify) for your taste.

The above example features 3 prominent drum & bass labels and is set to append less well-known artists’ tracks to a playlist on a weekly basis. (for the connoisseurs: some of the artists in the playlist are indeed quite legendary, but somehow don’t index high on Spotify’s popularity scoring)

👉 Playlist | Program

Playlist strategy

This toolset is also excellent for simplifying the work that goes into maintaining playlists one might use to build their following. Here are two examples.

Sourcing scene playlists for fresh music

Let’s say you read my recent post and are now building a new music brand. You already have a feeling of what it should sound like and are familiar with popular & less popular playlists in your scene. Your flow might look something like this:

I’ve added red dots to the playlist boxes to make it clearer which is which. In the big group, I have 8 different playlists (Wixapolo, Hardtekk, Lobsta B, Clubland, Pumping, Makina, Hard Dance Interpretations and an old playlist I no longer update) that get randomized and duplicate tracks removed before the mixer picks 50 tracks from them.

I’ve split 3 playlists from that path. For Lento Violento, I want to limit the amount of tracks that may show up, so the mixer on the left is set to a very low number, so only a couple of tracks enter the pool. For the Hyperpop playlist, I only care about the high BPM tracks that may be in there. Lastly, there’s trash rave, which is a big pool of music I add music to regularly. I want this playlist to dominate the flavour of the final playlist, so I’ve seperated it, so I can make sure the mix from the 10 playlists on the left have about a 50/50 ratio to the trash rave playlist.

Artist separation makes sure the same artist doesn’t appear multiple times in a row.

Enjoy some of the goofier bpms of dance music.

👉 Playlist | Program

Turn one big playlist into daily instalments

Let’s say you’ve been collecting loads of music into one big playlist, but you want to turn that into a highly engaging format that people come back to daily. This one is really simple.

For years, I’ve been compiling various types of Club Music into one big playlist – from Jersey Club to Juke to Bmore, you name it. Let’s turn it into a brief playlist that people can come back to daily.

Shuffle the input, so you don’t end up with only the top tracks (update: in this case, the ‘sample’ selector does the same as ‘shuffle → mixer’). Remove duplicate artists, since it updates daily, so keep it varied. That’s it. Don’t forget to set it to update every day via the clock icon in the Programs tab.

If you’re ready to move, give the resulting playlist a listen.

👉 Playlist | Program

Library organisation

Not everyone’s building playlist brands, but you may have a library that could use some organisation.

‘Focus music’ playlist based on what you know

This one was shared by Antoine Marguerie, a designer at Base Secrete.

I’ve rebuilt it and it takes familiar music (less distracting), filters the stream to only include low BPM tracks, removes some duplication, removes any lyric-heavy tracks, and takes a 100 tracks to add to the focused work playlist. A good way to reconnect with music you’ve already discovered.

For me, the result still requires some fine-tuning, because sometimes Spotify gets the BPM wrong and thinks a 160 bpm track is 80 bpm. This may not be an issue for most people, but my music taste makes those false positives quite likely to appear in my library. You could address that with energy and danceability filters.

👉 Program

Cleaning up a playlist with lots of albums

One of Spotify’s strengths is the convenience with which you can build playlists. Just drag and drop albums into a playlist and you’re done. The result is a playlist with albums all grouped together. In case you don’t want that, here’s something you can do.

This takes an unorganized source playlist, puts the most popular tracks towards the top and then shuffles things around with ‘weight’ (meaning you can set how random you want things — less random preserves the rough order of the list). In this formula I sent it to a new playlist, because I wanted to hold on to the source playlist.

👉 Playlist | Program

Your turn

The Smarter Playlists has FAQs and many additional examples. Start playing around and think of how you may put this to use. By automating, you’re programming, since this tool is a lot like a visual programming language. You can drop your programs in the comment section below, or drop them in this Twitter thread. Don’t forget to make backups in case you’re overwriting playlists.

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What if iTunes didn’t happen the way it did?

Posted on January 13, 2020 | by Bas Grasmayer

We all love to think “what if…”

What if Napster had managed to get its legal issues resolved? Would there be a Spotify now? What ecosystem would have emerged?

Last week I listened to a podcast interview between Tim Ferriss and Tony Fadell (“the father of the iPod”). They went into a piece of music tech history I wasn’t familiar with. Turns out iTunes launched as a somewhat re-engineered version of a startup’s software Apple acquired. This startup was called SoundJam and they had made some music software that would run on Macs, and could sync libraries with Rio music players. There’s a screenshot of it below and it kind of reminds me of WinAmp which I avidly used until Spotify came around. Note the chrome UI element which was characteristic for iTunes for a long time.

But there was another company Spotify was looking into acquiring. They were called Panic and developed a player named Audion. Also similar to WinAmp, it was more feature-rich than SoundJam and counted skins and visualizations among its features.

Audion didn’t end up getting acquired by Apple, because they never ended up meeting. The Audion team was already in talks with AOL and wanted to bring them together with Apple for a meeting. That meeting got canceled when AOL couldn’t make it, and that was the end of that.

The team behind SoundJam became the first developers to work on iTunes and after being lead developer for iTunes, one of SoundJam’s creators is now Apple’s VP of consumer applications.

Every product has a philosophy behind it and sometimes this philosophy can change the interfaces of a whole space. Look at how Tinder changed dating with its left-right swipe interface: not only a newcomer like Bumble decided to go for that, but so did the incumbent OkCupid. Or take Snapchat and the way its format influenced Instagram Stories and TikTok. This happens in music too, where some of the biggest influences can be traced back to IRC and Napster.

I think iTunes’ legacy is playlists. It really put the playlist front and center, which later on was also at the base of early Spotify. Spotify initially had no way to save artists or albums: you could star tracks and drag stuff into playlists. That was it.

It makes me so curious: if Apple had acquired Audion instead of SoundJam, would iTunes have been playlist-centric? Would the unbundling of the album have come about in the same way? Would we have the same type of ‘playlist economy’ as we see now?

If you’re curious to see what iTunes looked like upon launch, here’s a video of Steve Jobs demoing it (from 4:32 – excuse the pixels, we’re digging deep into YouTube’s archives):

Another obscure bit of Apple / iTunes history: watch Steve Jobs present the Motorola iTunes phone.

How the rise of Authorless Music will bring Authorful Music

Posted on December 9, 2019 | by Bas Grasmayer

Forty thousand. That’s the number of songs being added to Spotify every day. Per year, that’s nearly 15 million. With AI, we are approaching a world where we could easily create 15 million songs per day. Per hour even. What might that look like?

*Can music experiences performed by robots be Authorful? (photo: **Compressorhead**)*

The music trend we can most linearly extrapolate into the AI age is that of utilitarian music: instead of putting on an album, we put on workout music playlists, jazz for cooking, coffee time Sunday, music for long drives.

Artists have become good at creating music specifically for contexts like this. It often forms a big consideration in marketing music, but for also the creation process itself. But an artist can’t be everywhere at once. AI can and will be. Meaning that for utilitarian music, artificial intelligence will have an unfair advantage: it can work directly with the listener to shape much more gratifying, functional music experiences.

This will lead to the rise of Authorless Music. Music without a specific author, besides perhaps a company or algorithm name. It may be trained by the music of thousands of artists, but for the listener it will be hard to pinpoint the origins back to all or any of those artists.

Do we want Authorless Music? Well, not necessarily. However if you track music consumption, it becomes obvious that the author of music is not important at all for certain types of music listening. Yet we crave humanity, personality, stories, context.

Those familiar with trend watching and analysis, know to keep their eyes open for counter trends. When more of our time started being spent on social platforms and music became more anonymous due to its abundance, what happened? We started going to festivals in numbers never seen before. So what counters Authorless Music?

The counter trend to Authorless Music is Authorful Music. Although there will be a middle space, for the sake of brevity I’ll contrast the two.

	Authorless Music	Authorful Music
Origin	AI-created or obscure	Human-created (ish)
Focus	Specialised in function	Specialised in meaning
Relation	Little emotional involvement	Strong emotional involvement
Trait	Personalized	Socialized

Authorless Music: primarily driven by AI or the listener is unable to tell whether the listed artist is a real person or an algorithm. The music is specifically targeted towards augmenting certain activities, moods, and environments. Due to its obscure origin, the listener has little emotional involvement with the creator (although I’m looking forward to the days where we can see AI-algorithms fan bases argue with each other about who’s the real King / Queen of AI pop). In many cases it will be personalised to the listener’s music taste, environment, weather, mood, etc.

Authorful Music: primarily created and / or performed by tangible people or personalities. It will be focused in shaping meaning, as it is driven by human intent which embeds meaning by default. This type of music will maintain a strong emotional link between artists and their fans, as well as among fans themselves. This music exists in a social way – even music without lyrics, such as rave music, exists in a social context and can communicate that meaning, context, and intention.

With the increasing abundance of music (15 million tracks per year!), the gateway to Authorless Music has been opened. What about Authorful? What experiences will we craft in a mature streaming landscape?

Two important directions to pay attention to:

Socialising music experiences

It’s so easy to make and manipulate music on our smartphones now. Whether it’s music as a standalone or accompanying something on Instagram or TikTok. One reason for this massive amount of music being added to streaming services is because it’s easier than ever to make music. With apps that make it easy for people to jam around with each other, we’ll see a space emerge which produces fun tools and basically treats music as communication. This happens on smartphones but is strongly complemented by the virtual reality and gaming space.

See: JAM, Jambl, Endlesss, Figure, Smule, Pacemaker.

Contextualising music experiences

There is a lot of information around music. What experiences can be created by exposing it? What happens when the listeners start to enter the space between creator and listener and find their own creative place in the music through interaction? (I previously explored this in a piece called The future of music, inspired by a cheap Vietnamese restaurant in Berlin)

Examples of this trend: lyrics annotation community Genius, classical music streaming service IDAGIO, and projects like Song Sommelier.

Special thanks to Data Natives, The Venue Berlin, and Rory Kenny of JAM for an inspiring discussion on AI music recently. You’ve helped inspire some of these thoughts.

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The Chris Brown problem on Spotify

Posted on November 5, 2017 | by Bas Grasmayer

How do we deal with bad players in music when every listen translates to payment?

For a few weeks in a row now, Chris Brown has appeared in my Spotify Release Radar. I’m not sure why, because I don’t follow him, nor do I really listen to similar music, but that’s a different topic.

The issue I have is: I do not want my streams to put money into the pockets of abusers (Chris Brown has a history of violence towards women, and victim-blaming). So that means I can’t really listen to my Release Radar in the background, or most curated playlists for that matter, because I want to make sure Spotify never plays those tracks to me.

I’m singling out Spotify here, because I’m an avid user: basically all streaming services have this problem. I’ve made the case for a global ban button for particular artists before, when I wrote about the Moby problem on Spotify. Basically, in curated environments, it would be nice to give some control back to the user and let them blacklist certain artists they’re not comfortable with.

Not only would this give listeners a more manageable overall experience, but it would also allow people to immediately make sure their money doesn’t go to abusers (and in the aftermath of the Weinstein fallout, surely Hollywood’s revelations will start spreading to the music business too).

But there’s another issue: in the streaming era, how do we listen to controversial artists without sending money their way?

For example, some brutal details around rapper XXXTentacion came out a while ago. He comes across as an abusive monster, and regularly gets into fights with fans. Yet, he’s still very popular. I’m curious why – is the music that good? I opened Spotify to check him out, but stopped myself from hitting the playback button, being aware that listening means money will go towards him (or his label, and seriously, they should do a Netflix and drop this dude + donate profits to causes that help victims / survivors of abuse).

But the ‘Chris Brown problem’ is that dudes like this keep being put into popular playlists, keep appearing in users’ personal playlists through algorithm recommendations. As listeners, we need a way to shield ourselves, and prevent our money from going into the pockets of these people.

If Spotify and other services are serious about their passive ‘lean back’ experience: give us a blacklist button. Let us ban Chris Brown.

Meanwhile one Reddit user has a suggestion for when artists you like collaborate with such people (which I’m sure a lot of readers won’t like):

Comment from discussion Snoop Dogg's new album cover features him standing over Trump's dead body.

What the End of the App Era Means for the Music Business

Posted on October 30, 2017 | by Bas Grasmayer

The average smartphone user downloads less than 1 app per month, according to comScore. The era of apps is ending, and we’re moving in an era of artificial intelligence interacting with us through messaging apps, chatbots, voice-controlled interfaces, and smart devices.

What happens to music in this context? How do you make sure your music stands out? How do you communicate your brand when the interface goes from visual to conversational? And what strategic opportunities and challenges does the conversational interface present to streaming services?