Don’t get lost in translation: How to interpret artists’ engagement rates

A guide by Marie Cebrian & Julie Knibbe

Whenever you share content on a platform, whether it’s music on a streaming service or a video in an Instagram story, your success will depend on how engaging your content is. The way people react towards what you present impacts your content strategy. So-called “Engagement metrics” are instrumental in measuring how well you are doing. In the music business, gathering an engaged community is critical, as content consumption directly impacts revenues on a national and global scale. 

It’s important to define what engagement metrics are about – it’s easy to get confused on the way! An engagement metric will always be defined as a percentage. Contrary to absolute follower numbers, they provide more insights regarding how your audience behaves. For example, among all listeners of a radio you get played on, how many of them shazamed your song? Was it 0.1%, 1%, 10%? The ratio tells you how much people want to learn more about your song.

For most digital marketing activities (social media, SEO, advertising, etc.), engagement means that users will go where you want them to go: listen to your new release, read an article, watch a live stream, buy a concert ticket, etc. While looking at your engagement metrics, how can you know if the number/percentage you see is good, “can-be-better”, or just bad?

Here are a few examples of engagement metrics you must be familiar with:

  • radio impressions / shazam
  • notable followers / total followers
  • likes and comments / post
  • listeners / followers 
  • playlist additions / new listeners

Building and growing an audience is a continuous life cycle. We represented it as a snail – as an artist, you start with a very engaged small community and develop your project step by step by reaching out to new outlets.  

But, no matter how many playlists or radio spins you get, you should look at the ratio between your exposure and the number of actual new streams or fans you get. If you don’t, you will likely spend time and advertising efforts on operations that don’t pay off as much as they should.
Every step of the way, you must care that your audience is engaged and that your engagement metrics are good. From the beginning, your very first followers/supporters should be reliable and support your project whatever it takes, and you should have a good understanding as to what your brand is to them. These people will make your project real and help you grow from a healthy standpoint.

Streams, Followers and Listeners

Let’s talk about the relationship between your number of streams, your total number of followers, and your number of listeners. These metrics can be volatile and they are interdependent – one variation affects the two others.

To illustrate the specific relationship between these three, we will take the example of the Spotify Fan Retention ratio:

Spotify Fan Retention = Spotify Followers / Spotify Monthly Listeners

In plain English, from all the people who listen to your music on Spotify, how many follow you?

We took Spotify as an example because you can easily find any artist’s Monthly Listeners in an open/non-private way. You can calculate your own by finding these two metrics in the section ‘About’ of your artist profile. 

This ratio is one of the most relevant metrics in terms of ROI. Did your digital promotion and editorial playlists work to get more fans and followers? From something ephemeral like getting a placement on a big editorial playlist, did you manage to convert listeners into followers who are interested in following your career?

The ratio can be under 100% or over 100%. There is no bad or good ratio; it depends on where your career currently stands. 

Case studies

Case 1 – the ratio is under 100% 

→ The number of listeners is over the number of followers. 

  • Established artist – Jorja Smith (19,53%)

Jorja Smith has about 2M followers and 10M listeners, which makes her current fan retention ratio around 20%. Her songs are featured in many large playlists, whose cumulative followers reach 50M.  Given her current activity, several releases and campaigns over the past few months, we can consider that 20% is a good and healthy ratio.

  • Emerging artist – Jaycee (63,45%)

Jaycee is a very prolific DIY Australian musician. He recently finished his world record-breaking project #OneSongOneDream where from July 1st 2019 to January 1st 2020 he released a new song and music video every week for six months straight. Such commitment and growth got him under the TuneCore radar, which now actively supports his career. Looking at the above graph, it’s interesting to observe whether increased exposure (more Spotify monthly listeners) translated into follower base growth, which is the case here. Over the past year, his ratio fluctuated between 25% during peak exposure times in April 2020 (which tends to make the ratio lower) and 100%. Compared to Jorja’s, it all makes sense.

Case 2 – the ratio is over 100%

→ The number of followers is over the number of listeners.

  • Superstar artist – Ed Sheeran (131,56%)

That is a particular case. Ed Sheeran broke many records and grew a very large community across all platforms. His follower base (76M followers on Spotify, 22% of the 345M total Spotify Monthly Active Users in Q4 2020) is now such that even with a lot of exposure, his followers would usually outnumber the number of monthly listeners. His ratio is likely to remain very high and frequently above 100%.

  • Emerging artist – Anissa Altmayer (150,47%)

At the opposite end of the spectrum, Anissa Altmayer is starting her career. She doesn’t have a lot of songs yet in her catalog (2 EPs over the past 3 years), which doesn’t help the monthly listeners KPI, but her fans are actively supporting her no matter how many songs she posts. In her case, followers outnumber her monthly listeners, which should be interpreted as a good engagement sign from her community. When she’ll grow and get more active and more exposure, her ratio will decrease and reflect that new reality. 

These four examples illustrate that an artist’s audience size is not necessarily correlated to the final ratio – it can be over or under 100%. 

In summary, here are a few factors to take into consideration when analyzing your engagement ratio:

  • your career stage: undiscovered, emerging, or established 
  • your status: active (tour, release, marketing campaigns…) or inactive (no current project or activity)
  • your catalog size: large (several albums, more than 100 songs) or limited (one EP, a few singles)

As you can guess from the above examples, your fan retention ratio will usually be under 100%, and be considered healthy anywhere between 10% and more, depending on your current exposure. 

To justify this statement, we’ve built a data set composed of various French artists. Here are the panel characteristics: 120 artists analyzed, among which 70 emerging artists and 50 established artists. Career status has been determined with Billboard’s standard classification: 

  • Established artist: has at least one chart position in Top 200, Top 100, Viral 50
  • Emerging artist: no chart position ever 

Artists were selected from two major playlists:

  • Hits du Moment by Spotify France – 50 songs – 1,716,617 followers 
  • Découvertes by Gil from Deezer – 70 songs – 85,407 followers


  • 97% of the panel is under 100% (only three artists are above: Anissa Altmayer, David Guetta, Uele Lamore)
  • 81,8% of the panel is under 50% 
  • Emerging artists have a median ratio of 7,99% whereas established artists have a median ratio of 18,86% 


  • It is crucial to know how active the artist is and how much exposure they get (release, tour, playlist addition…) – the lower median ratio for emerging artists can be explained by the “status” factor. They usually are releasing songs/albums to launch their careers. It simply means that they are completing the first part of the Audience Snail represented above. More content, more campaigns, more exposure.
  • Fan retention tends to be higher when established. There is a direct correlation between a high ratio for established artists and the size of their catalog (the more the better). 
  • Compare artists from the same market (here, in France) and at the same career stage.

What does it mean when your ratio is very low?

Some artists may have a very low ratio, sometimes under 1%. It means that their songs are streamed much more than they are followed, suggesting a lack of artist brand identification. That happens when an artist is pushed at an early stage of their career into big editorial playlists with thousands of followers. Most times, the songs are being heavily streamed before the artist has time to grow his community and so get an engaged community. This is the syndrome “I know this song but I don’t know who sings it”. 

Some notable examples, we’ve spotted recently: Olivia Rodrigo, Masked Wolf, or Issam Alnajjar.

This phenomenon is even more relevant with the User Generated Content era and the renowned ‘Challenges’ on Tiktok (pick a 15’ audio and dance on it), or with artists featured on specific moods playlists.

Issam Alnajjar is a young Jordanian singer who’s getting millions of streams thanks to his unique song ‘Hadal Ahbek’ – used in more than 3.3M videos on Tiktok. 

His song was added to Spotify’s biggest playlists: Pop Rising (2M followers), Global Viral 50 (1.7M followers), Viral Hits (1.5M)… However, he only has 156k followers on his Instagram account. 
In that case, you need to work on building a relationship with your fans. In Amber Horsburgh’s words, from her guide about practical ways to build a band’s brand, “you’re building a thing that’s bigger than you and can be remembered beyond a Spotify playlist add”.

Engagement metrics are meant to be compared

You should explore the market in which your artist is developing. Thanks to music data analytics platforms, you can easily access analytics from other artists. In this piece, data is coming from Soundcharts and Chartmetric

Benchmarks can be also very useful to put the ratio you are studying into perspective. Several solutions were built to back your benchmark when comparing artist growth with similar audiences. Here are two relevant tools:

  • Next Big Sound Audience Engagement gauge

Next Big Sound is generating weekly analysis gathering the artist audience on Twitter and Pandora. The gauge is gathering comparable audiences and pinpoints the artist’s position depending on comparable artists’ activity.

  • Soundcharts Compare Artists tool

In a nutshell

Understanding your audience is critical when building a sustainable development strategy for your artist. Engagement metrics depend on many factors that can nuance your interpretation. It’s not all about streams or followers (at least, not only!). Career stage, status, the market, all these factors influence ratios that have to be interpreted in a specific context. 

However, a few things always remain the same: Healthy career development stems from working on your brand and building a strong core community. 

More reading around the topic:

How Jaycee’s #OneSongOneDream came true

Success story written by Julie Knibbe and Marie Cebrian

Jaycee is a very prolific DIY Australian musician. He recently finished his world record-breaking project #OneSongOneDream where from July 1st 2019 to January 1st 2020 he released a new song and music video every week for six months straight. Such commitment and growth got him under the TuneCore radar, which now actively supports his career. His work and authenticity paid off as he became a rising TikTok star (now close to 750K followers). He quickly reached more than 1M views on his latest hit “Who Are You“: “I need a beat where people can dance to it {…} And now I need your help to make it viral”

In this piece, we’ll dig more deeply into factors that contributed to his success story.


The below graph from Soundcharts shows consistent growth across networks over the past year, demonstrating how his brand is getting established as his reputation grows:

His content is authentic, Jaycee makes his story very accessible. You can check out his “Lie to You” storytelling video. His community is growing as fast as he delivers content. He released 26 tracks, along with a video clip for each one of them in less than a year. He makes a case for a “test and learn approach”: rather than spending months refining the same track, he just releases the state of his work for a given week and see what resonates best with his audience, increasing his odds of success.


Focusing on Spotify, his fan base is growing consistently, with a few bumps following his songs being featured in editorial playlists (Local Hype and New Music Friday AU & NZ).

On TikTok, he shares his daily inspirations, successes, failures, pranks, challenges with his community with a lot of humour, and he is now reaching 760K followers. He recently shared how he had the idea of his latest single “Dehydrated” in the bathroom, and now pranks his friends by throwing water at them, challenging his community to do the same using his challenge hashtag #dehydratedprank.

Influencer support

Check out Jaycee’ Tiktok charts position in Australia:

Noticing his work, just as perceived momentum has its own multiplier effects, TikTok showcased him as one of their Australian success story. He becampe part of a recent Tiktok campaign called “It starts on TikTok”.

His TikTok success got him noticed and he now has several songs plugged on Nova radio (Australia). 

Those 3 factors, consistency, authenticity and promotional support put Jaycee on the spotlight. This first wave of community and then platform support is sparking even more interest, creating a virtuous circle for him. No doubt he’ll manage to catch even more attention later on during his career.

3 Music Data Resolutions for the New Year

Happy new year everyone! Let’s start 2021 by sharing what we would like to see this year in the music & data world. Here are our goals:

  1. Develop data literacy
  2. Tell more data stories about emerging artists
  3. As an industry, be more mindful about how we use technology.

Wish #1 – Develop data literacy and critical thinking skills

In Cherie Hu’s words, “as much of the music industry remains online, data literacy has become more important than ever across all sectors”. Managers and marketers have plenty of information to analyze, and it’s sometimes difficult to extract the right insights from the abundance of available data. Now that I’m almost done writing our series about how music professionals use data – check it out on Cherie Hu’s Water & Music publication – I’d like to help professionals develop their critical thinking skills.

Here’s an example: Correlation doesn’t mean causality.

While the left example is fairly obvious: we all intuitively know that ice cream sales don’t increase murder rates (- hopefully). We wouldn’t jump to that conclusion even if someone was showing us data backing up that story. We would keep looking for another factor. Sometimes, it’s not that obvious: while Instagram growth is often paired with listeners growth on streaming platforms, it may not be the full story.

Wish #2 – Tell more data stories about emerging artists

There are a lot of success stories written in traditional media about artists hitting the billionaires club on Youtube or breaking charts records. These metrics are impressive and artists and their teams should indeed be praised for such achievements.

However, becoming a TikTok star or getting high volumes of views or streams matters, but it is not the only end-game. I discussed that topic in previous pieces focused on A&R and talent scouting, definitions of success vary widely from one case to another. Among the 25M music creators, about 43,000 artists account for the top 90% of streams in the world on Spotify (therefore belonging to the top-revenue generating artists on the platform). Not all of them are Youtube billionaires, and I’d like to see more coverage about non-billionaires and their strategies to achieve success. 

It’s not just the hits industry versus the long tail, there is a whole world in between.

Wish #3 – Be mindful about how we consume technology 

My last wish is for us to be more mindful about how we use technology, both as consumers and as music industry professionals.

Discussions about the environmental footprint of the music industry should focus as much on waste produced during music festivals and concerts, as on the digital carbon footprint. Technologies are great but they’re not neutral, even when they’re labelled renewable or green. 

On the consumer side, the dematerialization of music consumption has resulted in significantly higher carbon emissions than at any previous point in the history of music. Audio and video streaming are the biggest drivers of data consumption growth, and they are expected to account for more than 80% of Internet data traffic in 2021, according to Cisco. Streaming music videos from Youtube without watching them is an example of digital waste we could easily avoid by streaming audio only. 

On the business side, we can be mindful about when carbon-intensive technologies like HD-video streaming, VR or AI are relevant or not to solve our business problems. There are many discussions about whether tech efficiency gains can offset the carbon impact of online services’ rising demand. The answer is “it depends”, but it’s not very likely that efficiency gains will be enough to keep up with an exponentially rising demand. Here are the 2 best reads I could find about the topic: 

Data Cheat Sheet: A summary of data ownership in the live events industry

Guide by Julie Knibbe & Marie Cebrian

Photo by Amy Harris/Invision/AP

A study from PwC forecast that the live music industry would be worth $31 billion worldwide by 2022 if it weren’t for the pandemic. In the U.S., the live sector alone accounted for 48% of total music-industry revenues in 2019, outpacing the recorded-music sector. Long story short, the live sector is an industry within the music industry. 

I recently wrote a piece for Cherie Hu’s Water & Music publication, How the touring industry will use data in 2021 — even in a pandemic, providing guidance about how professionals use data in the live sector. During the research phase, I realized how data flows in this sector were even more complex than I thought. Ticketing platforms, promoters, venues, festivals, agents, bookers, and artist managers all collect and use data. On top of that, hybrid virtual events, live streaming and privacy regulations are now reshaping relationships between them: who owns what? Who needs access to what data? I even got lost at some point 🙂 That’s why I summed it all up in a Data Cheat Sheet to help you navigate this live universe within the music industry. 

Since a picture is worth a thousand words, Marie and I designed an illustrated version as well to describe data relationships between players of the live sector. As lines get blurrier between them and the recording industry, it’s a good time to review our basics to understand this ever-changing landscape.

How to read this chart

Ticket sales sit at the very center of this chart. It’s a goal and KPI shared by any player involved in the sector. All parties must define:

  • their own strategies to achieve ticket sales (outlined colored circles) 
  • data they own (colored circles)
  • external KPIs they need to get their job done effectively (outer circles)

Thanks to all the ground work done in the industry, data is now accessible and made actionable by several data tools that I will mention below. For more details regarding how to use these KPIs and tools, read the full guide on Water & Music.

Ticket sales

Incumbent ticketing platforms provide not only primary KPIs about sales, such as:

  • Tickets sold, 
  • Capacity percentage, 
  • Seating chart, 
  • Gross revenue,
  • Time of purchase,
  • Buyers personal information,
  • sometimes ticket transfers. 

but also secondary KPIs regarding the event marketing (see below) 

Tools: Eventbrite, Ticketmaster, Seated,…

Event marketing

Ticketing platforms like Eventbrite and Ticketmaster enable promoters to add pixels to track where sales come from, so that marketers can optimize their digital advertising investments according to which channel shows the best conversion rates. 

The COVID-19 pandemic reshaped the event promotion structure. It gave more power to artists and labels, who often act as the promoter as well, through live streaming events. Data collected from virtual events can provide significant leverage when physical shows will happen again (email, D2F stores, advertising…).

KPIs include: 

  • Pre-sale registrations,
  • Ad campaigns performance,
  • Sales channels, 
  • Sales channels conversion rates.

Tools: Audience Republic, Audiencetools, Arenametrix, ….

Touring history

Cherie Hu wrote about this in how booking agents use (and don’t use) data (highly recommended reading). In her words an artist’s touring history is “by far the most crucial kind of data that agents reference in their day-to-day decision-making. 

However, depending on the granularity you require, you may find out that complete touring history (ticket sales, time of sales, sales channels, attendance, fan contacts, artist contracts, fan behavior during the event, …)  is dispatched between at least three players.

For example, while managers and agents “know about tours months in advance of them ever going on sale …  promoters are often the last ones in that chain owing, in the most part, to the bidding processes, despite being the folks with the biggest job to do” said Sammy Andrews, Digital Marketer, in Music Week

Tools: Pollstar, Bandsintown, Songkick, …

Fan behavior during the event

Data collection during live events primarily serves several purposes:

  • Making sure that logistics and operations are running smoothly, and that the event is profitable,
  • Learning more about fan behavior during the show to optimize future shows and to develop targeted marketing campaigns after the show,
  • Complying with sanitary regulations with regards to the COVID-19 pandemic.

KPIs include: 

  • Attendance
  • Time of entry
  • With whom the fan attended
  • Contact tracing (COVID)
  • Seating plan
  • Purchases
  • Merch purchases
  • Time of purchases

Tools: TicketMaster SmartEvent, Aloompa, Appmiral, atVenu, …

Artist fandom and local footprint

Gauging and anticipating demand for shows remains a challenge. Understanding where fans are and what makes them willing to see a show can go a long way to anticipate and build demand.

KPIs include:

  • Facebook, Instagram fans per city
  • DSP Streams & listeners per city
  • Local airplay

but also secondary KPIs such as:

  • Bandsintown trackers
  • Local charts
  • Local playlists

A side note about live streaming: the meaning of ‘local’ marketing takes a different shape online: In Diana Gremore’s words, Business Intelligence Analyst at Paradigm Talent Agency, “instead of events being geographically local, they are local to online communities.”

Tools: Social networks analytics, Soundcharts, Chartmetric, Google Trends, ..

About data sharing and ownerships

In the chart, arrows represent data that is potentially shared. Data that is actually being shared depends on the promoter’s relationship and contract with the ticketing company, and on the artist management / agent relationship and contract with the promoter.

It is imperative that data collection be organized carefully, as exchanging data between third parties is under GDPR/CIAA regulations to protect privacy. Artists and their teams need to make sure that they have the right to use the data collected in the contracts they form with their partners, at least for their own marketing purposes. Partners also need to make sure they comply with data protection laws, so that the artist can be legally granted access that they need.

The Music Managers Forum (MMF) published a Fan Data Guide, which is a great support to understand where fan data flows from ticket sales to event attendance. They also built a data checklist to highlight items you should be aware of when dealing with promoters as an artist or a manager.

“A&R has always been about data”: a deep dive into the role of data in A&R with Chaz Jenkins, CCO at Chartmetric

Chaz Jenkins Chartmetric

I recently published a piece about how A&Rs use data to scout and evaluate artists on Cherie Hu’s Water & Music publication, and I wanted to share a bit of the background research I conducted to write this piece. Here’s an exclusive interview I did with Chaz Jenkins, Chief Commercial Officer at Chartmetric, where we discussed the role of data in A&R and how it evolved over the years with the advance of analytics tools. Before joining Chartmetric, Chaz had previously founded Grammy Award-winning labels, an artist management company and was VP International Marketing at Universal Music Group. 

Julie Knibbe: Given your experience at Chartmetric and previously as an A&R person, how is data transforming A&R departments?

Chaz Jenkins: First thing I’ll say is something really controversial. I’m actually allowed to do that because I used to be an A&R person. A&R has always been about data. Data has always been a really key component of any A&R person’s job, but we never really think of the data we used to use as “data”. It was just insights, information and acquired knowledge from the marketplace. A&R people have always been able to absorb a lot of information. The more information that they could absorb, the better decision making they could make. If you don’t understand the marketplace, then, yeah, you can still hear some music and think, wow, that’s amazing. But how can you make a good business decision about whether you should sign up artists, or whether you should put that artist together with that producer for example? You can’t do any of this unless you actually know a huge amount about the marketplace. And there’s always been data available. I mean, we think of tools like Chartmetric as revolutionary tools, but they’re just evolutionary because in the past there wasn’t much data. There was always market data. There were always the charts. There was always ticket sales. And our people knew tons of shit, basically, meaning they knew what was going on. We call it “gut instinct”, but the dire of the gut is data.

JK: Given these large amounts of data to process for A&Rs, how does Chartmetric help with the artist discovery process? And with the decision-making involved in evaluating whether or not an artist is a good fit? 

CJ: The challenge today is it’s just too much data. Twenty years ago, I, the average consumer, created two data points because I bought two products: a C.D. and a concert ticket. That was all the data which was generated. Today, the average consumer is creating 20,000 data points per year. To make it even more complex, there’s a lot more consumers than ever before because the music industry is monetizing far many more people than it ever did in the past; and it’s doing it on a global level. The music industry basically only operated in 40 countries in the world years ago. Today, it operates in 200, because we have consumers everywhere and they’re all interlinked. Today, A&R people just can’t absorb it all. They need additional tools to be able to make sense of all that sits in this information, so they can gain really simple insights. 

There’s a huge amount of artists out there to discover. On chartmetric, we track about 3M artists at the moment. Therefore, the majority of those artists have never been discovered by anybody. There’s not really a problem finding good artists to discover; the difficulty, if you’re an A&R person is finding artists who could fit. You can then invest if you have the skills to add to what the artist is doing in order to lift something which is small. Ever since the emergence of digital, it’s not entirely coincidental that there’s been a trend in the music industry to discover or sign artists later and later. Record labels have been progressively signing artists who are more and more successful. There’s been a focus on just signing tracks as well: not taking the risk of signing artists, just taking a track which is successful and trying to provide investment to make it even more successful. If you’re investing in something, you want to generate a return. Therefore, generally, you want to find something very small and make it absolutely colossal. That’s high risk but will generate a significant return. 

A lot of A&Rs over the past 20 years, have just become more and more conservative looking for things which were already successful and just tried to make them a little bit more successful. I think there’s been a gradual realization in the industry over the past five years that that’s not sustainable because there’s less and less of an incentive for an artist or a songwriter to actually sign once they’ve already become successful on their own. Why would you want to give away so much of your revenue? Is this organization genuinely going to provide additional support in order to make me even more successful? 

There is a need to really look early, but also learn what characteristics there are among emerging artists, emerging songwriters, which provide the seed for an artist to become successful in the long term. We’ve become, as an industry, too reliant on looking for things which are already successful or quite successful. Looking for things which are quite successful is not necessarily the best way to find things which are going to be very successful in the future. 

For the people who use Chartmetric, the key thing is not looking for artists who have achieved successes like getting a lot of streams or a lot of followers. Those things are too easy to fake and too easy to manipulate. A single outside influence like getting into a big playlist can have too profound an effect. Just because an artist gets into a playlist, does that mean they’re going to get into a big playlist in the future? Does that mean, if you sign the artist, that you’re going to be able to make them successful? Or it is because of chance that they achieve success this far? We’re advising to look for key triggers. That all predominantly comes down to evaluating the ability not only to acquire an audience but to retain an audience. Audience retention is the big challenge for the music industry going forward. The industry is great at audience acquisition, in the old days, it consisted of motivating fans to go to a record store, to hand over 15 $ and walk out with a CD. That was it, all you had to do, the job was done. Today, acquisition is just one part of a story, you need to retain the attention of the listener. Record labels can enable an artist to acquire more audiences, but they’re not so good at retaining them. Looking for artists who have that ability to retain the attention of audiences is a much more valuable use of A&R resources these days.

JK: Do you see that happening? Do you see in A&R departments focus shifting from raw follower numbers to audience retention ? 

CJ: Yeah, very much so, particularly over the past few years. I think there’s been a big shift in terms of not just looking at performance on DSPs, not just looking at a higher number of monthly listeners or number of followers, but looking at much more complex data, looking at multiple datasets to try to identify why. If an artist is having success, why? Where is that success emanating from? Another trend, of course, is globalization. We published a piece of research last year called Trigger Cities. 20/30 years ago when I grew up as a kid, emerging artists did not release music. They played gigs week after week, month after month, year after year, in the vain hope that one day an A&R person would come along to their gig, see them, and think they were amazing, listen to that demo and then give them a chance to go into a studio and record something properly. Even then, when they released that, they would be releasing it in one country. Then maybe if it was successful, they got a chance to record another single. If that was successful, maybe an album. Generally, artists didn’t have the chance to see their music being sold internationally until they’d had two successful albums. These days, kids in their bedroom, instead of recording a demo, record properly and release their music globally within 24 hours. We’ve gone from a very, very localized marketplace to a completely globalized marketplace. Your addressable audience is much bigger. It turns out that, unsurprisingly, the way people engage with artists, share their love for artists is very different around the world. The behavior in the West is very different from many other countries. 

JK: The music industry is structured by territory/countries. So how do managers and labels reconcile that global launch with their local investments? 

CJ: Today, it’s very simple to get global data. All the data in Chartmetric is borderless, because consumers are borderless. On the other end, the music industry is still structured along very localized lines. Companies are local companies. Even a major record company operates as separate entities in every single country. That makes anything international very slow, very painstaking, even quite political in many instances; but data is available everywhere. I think although certain parts of the industry are struggling to adapt to this borderless marketplace, in general there is a quite rapid transition to looking at the music industry as one big global marketplace. 

Ultimately, people find out about artists from friends, your biggest influences. I was influenced by my friends. My taste in music depended on my friends. But I only had four friends because I grew up a long time ago before the Internet, and my four friends lived on the same street. 

Kids today have friends all over the world, so they usually don’t struggle too much with discovery. 

JK: As you mentioned, A&Rs have tools plus their own network to help them with discovery, and that’s a lot of information to process. Some of them have a short list of 20/30 artists they’re going to look at during a day, to evaluate whether or not they could be a good fit. How do you think analytics tools are going to evolve moving forward? Because A&Rs are usually pretty overwhelmed and afraid to miss a gem.

CJ: It’s where data science really comes in, in order to evaluate metrics for huge numbers of artists across multiple different channels. Ultimately, there will always be a need for a human being involved in the process, because there are many critical metrics to A&R, which cannot convert into data. At the moment we are a long way from converging data. Computers are not good at listening to music nor hearing characteristics about music. In terms of really listening to music and discovering the emotional content of music, that’s actually still quite difficult. Computers can’t assess personal relationships very well. They can’t tell whether the manager is very good, whether the artist has an agent who genuinely believes in the artist, whether the artist is willing to do promo or wants to sort of like living a quiet life. These are difficult things to convert into algorithms at the moment. There is a critical need for human beings being involved in the process. There is so much data already available across streaming services. Comparison across individual artists, who will develop in unique and different ways, requires some real serious designs. That’s a huge part of what we do. Chartmetric is continuously analyzing trends across millions of artists in order to learn what happens and what has happened over the past five years. 

JK: Data science is usually good at solving large scale problems.. Do you think tools like Chartmetric can actually tackle the artist scouting problem at a large scale? To be more specific, each A&R person has his.her own set of criterias to make their decisions depending on their market. To what extent do A&Rs need personalization on these tools? 

CJ: When you are having to analyze data points across a dozen different services, building and reading the impact of radio, factoring in the impact of other media exposure, that then becomes very challenging for a human being to do when they’re looking at 30 artists a day. There is a need to use analytics to actually help the process in terms of personalization. Ultimately, we’ll head to the point where you can put every single channel and personalize it. Building tools now is about enabling every A&R to personalize their own search according to their own priorities. 

One artist can be a perfect fit for one company, but a terrible fit for other companies. Every company has a very unique skill set. Very often in the past, artists have signed to the wrong record company. That’s always happened. That’s a mistake for the artists and it’s a mistake for the record company. Nobody wants to sign the wrong artists; but one record company overlooking an artist doesn’t mean that another record company will not see that same artist fitful. 

JK: What’s your take on predictive tools? Is there such an algorithm that can predict success?

CJ: We’ve been building predictive tools for a couple of years now. You can always be predictive, but it depends on what people are looking for. Predictive tools can take a lot of the workload out and help identify new things which are not on your radar. But, if you’re a record company and you’re making a colossal investment in an artist, you’ve got to have a lot of trust in predictive tools. We want people to have trust in them, but you have to be able to provide a complete suite of supporting data as well for people who will make the decision. An A&R person won’t sign many artists in their career. 

To be more specific about predictive tools, you also have a matter of time frame because the shorter time you’re trying to anticipate, the easier it gets. But if you’re looking at longer term predictions, you have to factor in a lot of things that are not right now in the tools or in the data set. 

Every single person has their unique objective. It’s easy to develop a predictive predictive tool which will predict, but will it predict what people want to see? Everybody has slightly different objectives. Prediction is an obvious goal for all of this. What’s the next level of prediction? It’s a gradual, inevitable, but never ending process. 

JK: In data science, there is something called the “cold start” problem. If you know nothing about an artist, you can’t predict what’s going to happen. So A&Rs would have to, like you said, bet on something that’s already there. There has to be some success already happening to be able to predict more. 

CJ: Once you get your prediction to that level, that’s when the human element is threatened, but I think we are very far from that point. You have to factor in the team, the level of how are they willing to invest and expose themselves all the time? Do they want to tour? Do they want to lifestream? What is their take on their career? This information is not available in datasets.

JK: What about artists who fake their numbers?

CJ: There are always going to be numbers that are easy to fake, but it’s so easy to spot the numbers. A lot of A&R have several steps : discovery, initial analysis and testing – why do we see these numbers? Is there something on? Very often, you can see at an early stage that a number is not organic. Sometimes it can benign: something exploding on TikTok, getting into a big playlist, .. because we combine data, you can now see these things. If nothing explains, you can see that this person is buying followers. These aren’t new trends, in the old days people bought tickets for their own gigs. People sort of rented their crowds.

JK: How is Chartmetric trying to ease the A&R job? 

CJ: Essentially, we want people not to walk away from Chartmetric with a ton of data in their head because they won’t be able to hold that much data. We want to weigh with just a single insight which enables making a really smart decision. 

There’s one number in Chartmetric which people are increasingly measuring and that’s CPP, cross platform performance. We’re combining multiple metrics from multiple platforms because of course, when faced with a lot of data, people will often focus on the one number which suits their argument. With CPP, which is a cross platform index, bringing in data points from multiple different platforms eliminates that. You may still see a number which you really like, but then when you look at CPP, you say, hang on a minute, that number is not influential. 

JK: You introduced this new KPI, CPP (Cross Platform Performance), in the music industry. How was your experience introducing that KPI and driving its adoption? 

CJ: We haven’t really blasted it out there with a massive launch. We went for a gradual adoption of the metric. Eventually, we felt it was particularly used by marketing people as a way to gauge genuine performance across multiple platforms. We’re horribly siloed in our marketing approach in the music industry. We think success on one channel equates to success everywhere. And it doesn’t. Of course, you have to have success in multiple different places. 

The great advantage of using CPP is because it’s looking continuously at three million artists across multiple different platforms and providing this in a single index, which is adaptive over time. It enables far easier comparison. Then you can actually dive in and look in more depth to really understand why an artist is developing. 

JK: What advice would you give an artist to pass the “A&R” test? 

CJ: Perseverance. There’s no such thing as overnight success. It’s about hard work. It’s about not giving up. These days, in particular, we always talk about 365 marketing. Marketing campaigns in the old days in the music industry were very short term. The music industry, compared to most other business sector, releases a huge amount of new products. It has fallen into this age-old strategy of marketing things for two weeks and moving on to the next thing. In an age where audience acquisition and retention are the critical factors, you’ve got to retain an audience. You have to be working 365 days a year on audience retention. If the artist isn’t doing that, then nobody else can do that. 

Really successful artists have always worked 365 days a year. It’s critical for everybody involved in the process, not just the artist management or the label. It’s tougher if you approach things from an old mindset. If you approach things with a fresh mindset, if you look at the marketplace for how it exists today and don’t try and equate it to the old marketplace, then I think it’s a lot easier. 

Keeping attention is challenging but cheap. There’s almost a sense of freedom to the marketplace. You know, in the old days, it didn’t matter how hard you worked unless you were able to cover the final mile and get your product in a store in front of consumers. However, if you didn’t have that covered, then it didn’t matter how hard you worked. You had to get to the front and center in record stores to get attention. It didn’t matter what else you did unless you were there. Once you were there, it was very difficult to keep that success because of so many other priorities coming from record labels which were going to displace you. These days it’s completely different. When I started in the record industry, they always used to be this adage which, if you went to a sales conference at a major label, one of the label heads would back in their fist on the table and say, it’s all about making it big. Your new release had to be as high in the charts as possible. You had to have the highest charts placing, because if you didn’t do that, then it would be impossible to get exposure. That may sound daunting, but the marketplace today provides a lot more freedom, a lot more flexibility, a lot more creativity in terms of marketing. 

Understanding music discovery algorithms – How to amplify an artist’s visibility across streaming platforms

Recommendation on streaming platforms

This piece is based on the panel about Streaming & Algorithms I organized with France during the JIRAFE event put together by the Réseau MAP in Paris, where I interviewed Elisa Gilles, Data Scientist Manager at Deezer, and Milena Taieb, Global Head of Trade Marketing and Partnerships at Believe, about music discoverability on digital streaming platforms.

The idea
Understanding how music discovery algorithms work and including this knowledge in marketing plans can boost a song release campaign.
How it works
Algorithms can amplify momentum about a song or artist. To best leverage them, 1/ get metadata right when distributing songs to streaming platforms, so that classification is accurate; 2/ engage a community of early fans to help recommender systems understand for whom the song is the best fit.

Algorithms are at the heart of streaming services. Catalogs of modern streaming services now exceed 70M tracks, and recommendation algorithms have become essential tools that help users navigate this virtually unlimited pool of artists and songs. The most prominent examples can be found in systems powering personalized playlists like Spotify’s Discover Weekly and Release Radar, or Deezer Flow; but streaming personalization extends far beyond such discovery features. Home section layouts on most streaming platforms are personalized, and so are the search results. Algorithms are also used to pitch users similar content, determining which artists or songs are showcased next to the ones you are currently looking at. YouTube Chief Product Officer Neal Mohan shared at CES 2018 that recommendations are responsible for about 70 percent of the total time users spend on Youtube

Recommendation algorithms are now at the heart of digital music consumption, and so I could not stress this enough: to optimize artist visibility in the modern streaming landscape, it’s crucial to understand how these algorithms work.

From where do people stream?

As Milena Taieb, Global Head of Trade Marketing and Partnership at Believe, has pointed out during our interview: 68% of total streams are user-driven — streaming from their library, their own playlists or searching for their favorite albums or artists. 14% of streams are algorithmic driven, and 10% editorial driven. This is far from Youtube’s 70% algorithm-mediated consumption share, but that doesn’t make algorithms any less important. To get added to the user’s library or personal playlist, the artist needs to get discovered by said user first — and it’s editorial and algorithmic playlists that will often help get them there.

From where do people stream? Believe Digital data, 2020

The fact that most people stream from their libraries and personal playlists doesn’t mean that that is where you should concentrate all of your attention. Yes, the goal is to move the listener from “passive” streams (originating from algorithmic or editorial playlists) to “active”, user-driven streaming — but in most cases you can’t have the latter without the former. Put simply, to get user-driven streams, you need to build up algorithmic discovery first.

A side note on COVID-19: lockdown had little impact on those discovery patterns. Elisa Gilles, Data Scientist Manager at Deezer, told me that she noticed a peak of kids content and live radio consumption, while the usual peaks during commute horse evened out across the day. However, overall behavior regarding recommendations didn’t change much. Overall streaming was down to about 15 to 20% for the first few weeks, but soon returned to normal volumes.

So, what influences a song’s discoverability and its chances to be recommended?

First of all, let me explain how recommendation systems work. There are two main ways to build recommendations for a user:

  1. By content similarity — “I recommend that you listen to an emerging hip-hop artist because you listen to a lot of hip-hop”
  2. By behavioral similarity — “I recommend that you listen to Tones & I because most users who listen to the same artists you do also listen to Tones & I”

The latter is also known as “The Netflix” approach or collaborative filtering.

This graph above is an example from the music discovery team at Spotify, looking at which artists are most commonly added in playlists together, and then using these probabilities to drive recommendation.  

Most streaming platforms use a combination of both content and behavioral approaches to power their recommendation systems. However, the exact way they are able to describe music and how they analyze listening patterns, remains the “secret recipe” of each respective recommendation engine.

How to optimize for content similarity?

Content similarity is usually more important when it comes to freshly released songs that don’t have much in terms of streaming behaviour and playlist additions for the platform to analyze. This is known as the “cold start” problem — in order to overcome it, the artists are asked to fill in the initial information about their songs when they submit music to distributors (i.e. metadata): title, artist, label, main genre, secondary genre, etc. Filling these fields as accurately as possible is very important, as this data will be a basis for the initial song classification across streaming services.

Example of a single submission form on TuneCore 

That said, streaming services usually don’t rely only on the metadata alone. Broad genre tags like “Pop” or “Dance” may take on different meanings depending on the context — and so streaming platforms develop their own content analysis systems to expand on that basic data. Such tools allow them to analyze raw audio files coupled with provided metadata to assign more narrow content tags and power initial content similarity recommendation.

So, making sure the song is properly described, and that all possible data is provided — including lyrics and even label name, can come a long way when it comes to helping your music get discovered. Making sure metadata is right is Discoverability 101.

How to optimize for behavioral similarity?

As I’ve mentioned above, a behavioral similarity approach only works when there are some listens, searches, playlists additions, saves and other consumption patterns for the algorithm to analyze. But how can you leverage that to amplify the artist’s visibility across streaming platforms?

Well, the first step is to identify which artists and songs have affinity with your music. In which playlists does your song belong? Who are other artists featured in those playlists? The  chances are that users who like those artists and listen to those playlists will also dig your music. The more users who like your songs listen to other similar songs and artists, the more relevant patterns there are for the algorithm to analyze. The more patterns there are for an algorithm to analyze, the better it will get at matching your music with your potential audience.

That means, for instance, that there is next to no point in paying for random streams. They won’t help the algorithm to qualify your song and recommend it to the right users — on the contrary, they will establish fake consumption patterns that will only hurt your discoverability.

Instead, what works is:

  • getting played and added to playlists by fans who enjoy your music and your style: they will also listen to other artists similar to you, and help the algorithm understand where you belong;
  • getting on curated playlists that are focused on your style or genre.

As you can see, optimizing for editorial and algorithmic playlists works really well together. Beware, though — editorial playlists have to be focused on your genre, especially if you are an emerging artist who’s just starting to build your fan base. Getting featured in a huge editorial playlist — something like Spotify’s “New Music Friday”, for example — can be a double edge sword. Such discovery playlists blend many artists that may not have much in common, at least sonically, with your music. In a way, too much exposure that comes too soon — that is, before your music is properly qualified — can lead the algorithm to push it semi-randomly to unqualified users, which ought to get you bad skip rates and lower your song long-term potential.

Algorithms are becoming the primary source of music discovery. The latest research from MRC Data/Nielsen Music highlights that 62% of people surveyed said streaming services are among their top music discovery sources while “just” 54% named friends and family. These algorithms are not artificial though, they work by analyzing how fans listen to your music. Building an engaged and active community around your artists and their music is still the key to running a successful and sustainable music career. These fans, even if their number is small, are your biggest resource that will help you spread the word about your music and find new listeners. Beyond that, they are the ones who will help algorithms pick up on your momentum and amplify it through the recommender systems. 

Dig Deeper

If you’re curious to learn more about how you can find the right strategies (and right spaces) to promote your artists, check out the piece I wrote for Cherie Hu’s Water & Music on how to use data to market new releases, which includes a section on how to find relevant playlists to target in your pitching campaign. To dig even deeper into understanding how your music is classified, Bas Grasmayer and Carlo Kiksen put together a tutorial to learn how the Spotify AI categorises your music and check out your song audio analysis. 

Travis Scott’s literally Astronomical event on Fortnite: What music managers can learn from THE SCOTTS release

Travis Scott Fortnite

“Ooops, I did it again” 

A bit more than a year after Marshmello’s previous set in Fortnite, Travis Scott and Epic Games set a new record with the ‘Astronomical’ 3-day residency, with about 12 million players tuned into the experience the very first night, beating Marshmello’s 10.7 million attendance in early 2019. In total, 27.7 million players watched the event, and that doesn’t even account for Youtube or Twitch views later on. It’s important to note that it is also a record for Fortnite that peaks at 7.6 million players on a regular non-eventful day.

It’s not the first time the music and gaming industries fool around together. For instance, Solomun appeared as the primary DJ for the GTA Online Protagonist’s Nightclub and stayed resident from 24 July to 31 July 2018. The trend is picking up everywhere, even more so now that Covid-19 put half the world on a stay-home policy. Major festivals and concerts are struggling with cancellations and sanitary restrictions. Live streamed events are booming and the music industry is resilient enough to push innovation forward in these difficult times. A virtual music festival is happening inside Minecraft this month

Why Astronomical worked so well? 

Music & Virtual Reality had a complicated history so far. MelodyVR and other VR companies are bringing orchestras to the living room. However, no music & VR experience has reached any mainstream audience so far. Trying to replicate a concert experience in a living room is bound to be disappointing. Just because it’s trying too hard to replicate something that already exists. The social dimension of going to a show is very strong, people go to concerts to live the moment with the band and other fans. No headset can make you feel the heat of being surrounded by other human beings vibrating to the same beat alongside you. VR suffers from the comparison that users can hardly prevent themselves from doing.

What Fortnite, Marshmello and Travis Scott successfully did is to actually create a new experience that fans wouldn’t compare with anything else: leverage an existing virtual universe, use its users habits and codes, and leverage them to create a unique artist/fan experience.

On top of building an amazing user experience, the move is also smart because one doesn’t have to create a whole new virtual universe, it is already there in the game, as well as the audience. Fans don’t have to get new equipment to benefit from the show. It is original, unique and with a seamless experience for those already in the game. As a product designer, I can only applaud. What about those who don’t play?

A success beyond gaming platforms

Cherry on the cake, non-gamers were not left behind. The virtual event can be broadcast live on Youtube, Twitch and/or Instagram; which makes for a fully integrated experience across all networks. There is no FOMO for non-gamers since fans can see what’s happening. Youtube and Twitch in this case supplement the experience, enabling replays on other devices later on. Travis Scott’ team leveraged all platforms and tailored content for each accurately: Fortnite for the live immersive show, Youtube and Twitch for replays, and Instagram for the community.

The Astronomical event today has almost 30 million views on Youtube, surpassing attendance on Fortnite. Travis Scott’s Youtube channel gained 2 million subscribers, as well as his Instagram page. The only remaining question is whether the virtual, video experience also promoted the song well and if fans enjoyed the audio art as well. Spotify figures seem to point in that direction, as monthly listeners reached an all time high at 44 million (data courtesy of Soundcharts). 

Key takeaways

IRL, what can you do (without Travis Scott’s marketing budget)? Here are a few takeaways you can bring home when thinking about your next campaign:

  • Go where they go: leverage existing audiences and fit their needs and habits,
  • Tailor the experience for the platform you will work on: don’t duplicate content and think specifically about how to adapt for one given platform.
  • Think 360 across all networks: fans use several social networks and streaming platforms, think about their experience from start to finish. 

PS:  I won my first game on Fortnite last night. Couldn’t resist.

It’s Raining Men – Statistics about The Gender Gap in Music

On March 8th, also known as Women’s Day, I was invited as a music & data intelligence expert to speak on a panel about gender and music organized by Sofar Sounds and Despite my commitment to become a role model and stand for gender equality, I had not specifically dug into women in music numbers yet and I figured that was perfect timing to finally do it. 

Peggy Gou
Peggy gou

Jump right in:

1/ How many women among artists?

To be able to count female singers, musicians, producers or songwriters, it is required to have gender specific data describing them. However, here’s the first roadblock, there is little public gender-differentiated metadata describing artists. Most data providers don’t have this kind of information, and the most comprehensive dataset I found so far is the Musicbrainz database. 

Musicbrainz database gender statistics

Gender is only known for about half of individual artists of the Musicbrainz database. Among those with known gender, women represent only 11.6% of “non-group” artists (still that’s 141,318 people).

Among songwriters, PRS members in the UK are 16% women, SACEM members in France are 17% women (2018 figures). The gender divide across all regions is roughly 30% female to 70% male with an optimistic outlook.

2/ How many female artists make it in popular music?

The USC Annenberg Inclusion Initiative examined the prevalence of women among the top 800 popular songs from 2012 until 2019. 

Female artists across the top 800 songs (2012 – 2019), USC Annenberg Inclusion Initiative

Those results are consistent with other studies I could find, and with my own research from Soundcharts: female artists account for about 20% of the top charts, regardless of the platform you look at:

Gender Ratios on Top Charts, the Gender Play Gap

3/ Are streaming platforms male-dominated? The French Hip Hop case

Digger deeper for France (my beloved country) where urban music tops the charts, ratios tend to be worse. Early March this year, only 9% of the top streaming charts were female on Deezer, Spotify and Youtube. The first female I could find in the rankings was Tones & I at the 28th rank on Spotify. The week I looked was indeed a pretty bad week for female music, and hopefully it’s not necessarily the case all year round.

France Top 50 as of March 17th, 2020

In many countries, streaming top charts skew towards urban music because subscribers are typically younger and play music on repeat. French streaming charts are owned by French rappers in particular, whose audience usually skew 60-70% male. Let’s look at Ninho for instance:

Ninho’s Instagram followers are 64% male (Soundcharts, March 2020)

Does it mean that streaming charts are male streaming charts? Why would streaming charts skew towards “gender-imbalaced” artists?

There are many possible explanations there, and the truth probably lies in a combination of the following:

  • Subscribers of streaming services are more numerously male than female. Spotify has 43% female listeners for instance.
  • French rappers have their music played on repeat more than any other genre.
  • Streaming charts can be influenced by plays that are not properly qualified. (e.g. fake plays by bots, or social accounts connected that are male by default)

Interestingly enough, radio airplay doesn’t show as much imbalance. That same week, the Top 100 French Airplay Charts were featuring 35% female artists. In the US too, rap radio is more supportive of female rappers. Being 100% curated, traditional radio has an opportunity to be ahead of the curve and super-serve a female audience that streaming has not yet grasped.

4/ Do men and women consume music differently?

In a nutshell, yes, streaming statistics from Deezer and Spotify show that listening habits differ between males and females. More particularly, women tend to listen to more female-artists on average. On Spotify, female listeners stream 30.5% from female or mixed-gender artists, while male listeners stream 17.2% from female or mixed-gender artists.

Back to French hip hop as an example, Deezer published the gender balance for the 200 biggest hip hop artists of 2018.

Gender balance for the top 200 hip-hop artists of 2018, Women and hip-hop,Deezer

The further an artist is on the right, the more the gender balance is towards female, showing how women would favor more female artists.

To illustrate the impact of gender balance, Paul Lamere from The Echo Nest/Spotify looked at gender specific top artists (2014 numbers) :

“No matter what size chart we look at – whether it is the top 40, top 200 or the top 1000 artists – about 30% of artists on a gender-specific chart don’t appear on the corresponding chart for the opposite gender.”

Paul Lamere, Music Machinery
Gender Specific Listening, Music Machinery

5/ Are men more “passionate” about music?

Now let’s look at how much men and women listen to music. To avoid as much bias as possible, I first looked for Youtube numbers, since the platform has the biggest music audience in the world and allows active streams for free.

Youtube usage for music doesn’t show too much imbalance, as about 80% Youtube users, male or female, would use Youtube for music as well. 

Looking at music products overall, buying patterns hardly differ from men to women, age being a lot more discriminating than gender. 

Music Products purchased over the past 6 months, 2018

Men and women seem to be equally interested in music at first, but gender imbalance still appears on music specific apps or services : most music services have audiences that skew male. Paid streaming subscribers tend to be more men, and the same trend can be observed on TikTok as well.

Share of TikTok users by gender and age (2019)

The Australian Music Consumer Report could explain why. Obviously, music enthusiasts are driving online music consumption on Youtube and streaming services. The report highlights that male or female millenials are equally passionate about music between 16-24. However, they usually are not accounted in streaming subscriber statistics as they remain free users or their parents pay for their online subscriptions.

Age and gender breakdown of music passionates, Australian Music Consumer Report

Later in life, the gender gap starts to appear. From 25 years old, males would declare being more passionate than females about music. Another VEVO study about millenials shows that stereotypes die hard. Males would identify more as “Tastemakers” while females would identify more as “Front Row fans” (groupies). Sound diggers are usually pictured as masculine and that view is translating into consumer patterns.

6/ Why so few women pursuing music careers?

Stacy Smith, one of the USC Annenberg Inclusion Initiative leaders, hints at social conditioning:

“Women are shut out of two crucial creative roles in the music industry (…) What the experiences of women reveal is that the biggest barrier they face is the way the music industry thinks about women. The perception of women is highly stereotypical, sexualized, and without skill. Until those core beliefs are altered, women will continue to face a roadblock as they navigate their careers.”

Stacy Smith

During the Music & Gender panel, Claire Morel from France pointed out as well how women often have to fit stereotypes: the fragile woman singer, the charismatic rock star, the inspiring muse, the woman-child, the hypersexual rapper, and so on. In the 90s, each member of the Spice Girls would illustrate one of these feminine stereotypes. There is little space for a woman who is an artist to just be an artist. Younger female artists who don’t fit these stereotypes are more likely to give up on their music career because they feel less legitimate. 

“The male artist, in our image of him, does everything we are told not to do: He is violent and selfish. He neglects or betrays his friends and family. He smokes, drinks, scandalizes, indulges his lusts and in every way bites the hand that feeds him, all to be unmasked at the end as a peerless genius. Equally, he does the things we are least able or least willing to do: to work without expectation of a reward, to dispense with material comfort and to maintain an absolute indifference to what other people think of him. For he is the intimate associate of beauty and the world’s truth, dispenser of that rare substance — art — by which we are capable of feeling our lives to be elevated. Is there a female equivalent to this image?”

Rachel Cusk, Can a Woman Who Is an Artist Ever Just Be an Artist? 

Artistic talent, like any other, requires nurturing. Men tend to be more favored along the way, the music business being mostly a men’s network. Females are still seen and evaluated through their gaze most of the time. Women in Music, and other women initiatives aim at bridging this gap by building women networks, and by bringing these diversity issues to light. 

The road ahead

Gender gaps won’t disappear anytime soon. However, I’m optimistic about a trend towards more diversity in the music industry. First, female fans or artists now have plenty of role models they can identify with. Democratization of music production and distribution enabled millions of artists to reach new audiences and the offer is no longer limited to heavily stereotyped girls or boys bands. Among the happy few in popular music, Tones and I, Billie Eilish, Adele and many more are leading as examples of women artists.

Second, the music business is transitioning towards more data-driven decision making. Talent scouting is no longer driven by gut-feeling with all biases that we know. When music professionals evaluate artists to decide whether or not they are going to sign or program them, they listen to the music, and they also now look at KPIs like fan base engagement and retention. Although not perfect, these KPIs enable comparing artists on facts rather than feelings, which will hopefully bring more diversity in the mix.

Third, data also makes the music business accountable. Reports like the one from the USC Annenberg Inclusion Initiative measures year after year how female artists evolve in the charts. The Grammys this year proved that change is here. Billie Eilish became the first woman to win the “Big Four Grammys”

Can robots write musical masterpieces?

I wanted to comment on the overall assumption we commonly see in publications that AI will never write a “critically acclaimed hit” or out-Adele Adele. 

It is usually very politically correct (and less frightening) to suggest that AI can’t make art better than humans. It’s okay let them replace automated tasks but we like to think that more “right brain” activities are not that easily replicable. We hold on to the belief that only human creations can touch someone’s heart and mind. The way we humans create music requires getting in touch with one’s own feelings and find means of expression, on top of mastering playing one or more instruments. 

The truth is, AI can write songs as well as humans can, if not better. “Beauty is in the ear of the listener”, if I may 🙂  If you think about creativity as exploring unexplored territories, mixing or creating new sounds, trying new combinations, then AI has a lot more creative juice than any human brain. It can explore more than we can, with a lot less mental barriers about what should or shouldn’t be tried or experienced. 

“Of all forms of art, music is probably the most susceptible to Big Data analysis, because both inputs and outputs lend themselves to mathematical depiction”. 

Yoah Nuval Harrari

The real argument here is more about the very definition of an artist.

I just googled it to see what’s commonly used to describe an artist. Here’s Cambridge’s definition:

  • “someone who paints, draws, or makes sculptures.
  • someone who creates things with great skill and imagination.

This definition will evolve as musicians use AI to explore, and won’t have to produce so much entirely by themselves.

Most likely, in the future, being able to produce won’t matter as much as telling a story and having a personality that people will want to follow and hear more of. Hanging out at FastForward earlier this year, we were discussing about artist careers and about what makes people becoming fans of artists. 

Depending on musical genres and audiences, it is a mix of musical skills, personality, familiarity and storytelling that creates fandom. Song quality by itself is definitely part of these requirements, but it is usually not enough to create an audience. For now, we don’t have any AI mastermind replicating both personality and songwriting. So, artists are not directly replicable per say but both types of AI do exist already. 

In the near future, unless laws banning anthropomorphism pass throughout the world, we are even bound to see the likes of Lil Miquela, fictional artists, releasing singles on Spotify. Just like real artists, these fictional artists will have whole teams behind them to manage their careers.

Will they write better songs than Adele? 

There are some evidence that AI can write beautiful masterpieces already, that I’m sharing here. I found the following study while reading Homo Deus, by Yoah Nuval Harrari, an essay about what awaits humankind in the AI era:

“David Cope has written programs that compose concertos, chorales, symphonies and operas. His first creation was named EMI (Experiments in Musical Intelligence), which specialised in imitating the style of Johann Sebastian Bach. It took seven years to create the program, but once the work was done, EMI composed 5,000 chorales à la Bach in a single day.“

“Professor (…) Larson suggested that professional pianists play three pieces one after the other: one by Bach, one by EMI , and one by Larson himself. The audience would then be asked to vote who composed which piece. Larson was convinced people would easily tell the difference between soulful human compositions, and the lifeless artefact of a machine. Cope accepted the challenge. On the appointed date, hundreds of lecturers, students and music fans assembled in the University of Oregon’s concert hall. At the end of the performance, a vote was taken. The result? The audience thought that EMI’s piece was genuine Bach, that Bach’s piece was composed by Larson, and that Larson’s piece was produced by a computer.”

When an audience is not biased, listeners can hardly tell the difference between Bach, an AI or an unkown writer.

Can an AI write a masterpiece? Yes. You may argue that AI are trained based on a given dataset (e.g. a set of songs), depriving them from free will as to what is actually produced. However, AI can be trained to learn from the best composers, exactly like a human would have various musical influences and attend masterclasses taught by virtuoses.

One fundamental difference that still remains is joy and creative flow. A machine will hardly derive as much joy out the creative process as much as we do.

Jeff Mills teaches astrophysics but when will he actually DJ on Mars?

Jeff Mills

Jeff Mills teaches astrophysics but when will he actually DJ on Mars?

I really loved Mixmag’s last April Fools: Jeff Mills is going to be the first DJ to play in space.

“The Detroit musician really is taking a trip to the stars in his latest musical venture. He’ll be playing across three turntables, a throwback to his earliest performances. “A DJ playing in space is so obviously the future,” The Wizard told Mixmag. “So I wanted to balance that with analogue technology in its purest form: three perfectly calibrated Technics 1210s.” MixMag

Given Jeff Mills passion for astrophysics and science fiction, this joke came as no surprise. He recently teamed up with NTS to produce a radio show, The Outer Limits, using music and narratives to create an immersive experience about space exploration.

Although, the idea of DJing in space raises a few interesting questions…

Can you hear sound in space?

My naive guess was that you’ll never hear a sound in space since that would require soundwaves to reach your inner ear. Soundwaves need a medium to travel through and there are so few particules in space that sounds would fade way too quickly. Although, recent research pointed out that gravitational waves do travel in space, so hope is not entirely lost.

How does it feel like to play an instrument without gravity?

Okay, you may not play tomorrow in space directly, but you could play in a spacecraft. NASA already experimented and musical instruments have been brought to space.

“When you play music on the shuttle or the station, it doesn’t sound different, say the astronauts. The physics of sound is the same in microgravity as it is on Earth. What changes is the way you handle the instruments.” NASA

Carl Walz and Ellen Ochoa, two astronauts, shared their experience playing in microgravity. “When I played the flute in space,” says Ochoa, “I had my feet in foot loops.” In microgravity, even the small force of the air blowing out of the flute would be enough to move her around the shuttle cabin.

As for guitar, says Walz, “you don’t need a guitar strap up there, but what was funny was, I’d be playing and then all of a sudden the pick would go out of my hands. Instead of falling, it would float away, and I’d have to catch it before it got lost.”

Can we communicate with alien civilization with music?

We have been sending music into space for a while. In 1977, NASA sent two phonograph records aboard both Voyager spacecraft. These records contain sounds and images highlighting diversity of life and culture on Earth, featuring songs from all over the world. They are considered as a sort of a time capsule.

The Golden Record cover shown with its extraterrestrial instructions on how to read it. Credit: NASA/JPL

This year, for its 25th birthday, the Sonar Festival sent out 33 separate 10-second clips of music by electronic artists such as Autechre, Richie Hawtin and Holly Herndon, to Luyten’s Star, which has an exoplanet, GJ273b, believed to be inhabitable.

Well, we haven’t heard back yet!

Sources and inspiration