Music Business

How Data is Redefining A&R’s Role In The Modern Music Industry

Although A&Rs are still looking for the same qualities in an artist as they were fifty years ago, what they’re looking for in that same artist’s data – and they way they’re looking for – it has changed dramatically.

Guest post by Tommaso Rocchi from Chartmetric

Editor’s Note: Tommaso Rocchi is a 2020 Master of Arts graduate of The Global Entertainment and Music Business program at Berklee College of Music in Valencia, Spain. As a former college radio Music Director at, Rocchi focused on copyright law, new business models, and data analytics.

Data-driven A&R has been a buzzword for quite some time in the music industry, but also one of its most guarded secrets. Even before the acquisition of Sodatone by Warner Music Group, major and big indie record labels started to switch their mindsets and focus on the advantages of a data-driven approach.

Compared to the classic “gut-feeling” expertise of a senior A&R, data analysis allows today’s A&Rs to validate their intuition and justify talent acquisition with predictive modeling. According to the IFPI Global Music Report of 2019, record companies are investing more than one-third of their global revenues ($5.8 billion) in A&R and marketing each year. With such a significant share of recording company budgets invested into artists, their main objective is to minimize revenue loss, and data can help them make the right bets. The second reason why the industry is going full-data is volume: As stated by Spotify itself, nearly 40K tracks are uploaded on the DSP every day. With such an impressive number of records, it is impossible to use only a “gut-feeling” approach.

Due to these factors, record labels are extremely careful about how much they share regarding the technology and methods they are utilizing. Saying too much could mean losing a competitive advantage over another label and potentially arriving late to a signing.

New technological advancements have already changed the role of A&R in the music industry today, transforming the relationships between labels, artists, and managers. Still, data-driven A&R is largely a secret kept in plain sight, and very few people have a clear understanding of what A&R is and how it works in the music industry today.

What Is A&R in the Digital Age?

A&R, or Artists and Repertoire, really came into its own with the development of the recording industry. As the commercialization of the phonograph ramped up during the mid-20th century, the music industry was splitting from two sectors into three sectors: publishing, performing, and now, recording.

The technological disruption of recorded music at scale expanded the artist pool, because it opened up a whole other world of song interpretation and, consequently, consumption. The recording artist was born, and A&Rs were positioned to help them succeed.

Originally, the A&R role was tasked with matching artists to the right song and/or songwriter. Before the Beatles, after all, commercially viable artists who wrote their own material were pretty much unheard of.

As the album format reached full maturity with ‘60s/‘70s Rock, A&Rs were increasingly involved, in varying degrees, with every aspect of an artist’s development, from discovery to production, creative direction to career trajectory.

A&Rs weren’t just song pickers or talent scouts: They were the business-minded angels on artists’ shoulders, the bridge between the artist’s wildest creative impulses and the record label’s most stringent budgetary concerns.

A&R has changed since the dawn of the internet: Gone are the days when an A&R scout had to step inside a dark and smelly pub in South London and be the first to find the new Ed Sheeran. Even if this idea still has a lot of charm, these signings are extremely rare today. The job of an A&R scout is extraordinarily analytical and requires having a great understanding of the musical environment. Paul Samuels, Vice President of A&R at Atlantic Records UK, clearly remembers how hard it was to research what records “the cool kids were listening to”:

I am old enough to remember record shops and finding out what records were selling. My first gig with Craig Kallman [now CEO of Atlantic Records] was going around to record shops with records and trading them for independent ones. And that was my first research. We would go to all these shops and find out what people were listening to. So I’d go to Rough Trade Records in London on a Saturday and see what was happening. But most of the time, it was a bust, there was nothing.

The use of data and automation has certainly helped ease this process, and labels are investing many resources in this field as a result. If we look at this recent job posting by Motown Records, we can see a very different kind of professional figure than the usual A&R job description.

Figure 1: Job description for an A&R manager at Motown Records. UMG Careers Website. Accessed May 12, 2020,

One profile that major labels are looking for today is extremely technical, involving extensive knowledge of SQL, programming languages such as Python or R, a background in statistics, and the ability to create predictive models. In this particular profile, there is no mention of any skill related to assessing talent, evaluating production and song structure, or vetting an artist’s branding. As stated by Samuels himself, it can be advantageous to have A&Rs make judgements based on data and not on their preconditioned tastes.

But data can’t solve every A&R problem. While we’ve become extremely good at creating algorithms that can outperform humans on very specific tasks, the job of an A&R involves so many different skills that it’s almost impossible for a machine to replace an A&R. First of all, numbers can lie and aren’t necessarily a universal sign of quality. An artist might just get a lucky playlist placement or strike the right viral chord on TikTok. As a result, successful signings that are purely data-driven are extremely rare.

And then there are the outliers. The fact that some artists don’t have very big numbers at the beginning of their career doesn’t necessarily mean that they don’t have potential. They could be doing something completely different from other musicians, and, most of the time, the artists who don’t conform to trends are destined to become the most successful.

Another big problem of only utilizing data is that it gives no competitive advantage. Using the words of Jerry Zhang,  Co-Founder of Sodatone, major labels “are looking for the 10 artists in a million that have abnormally good performances on different platforms.”

Unfortunately, abnormally good performances on different platforms are all the more visible. Once you see those unambiguous signals of success, other labels that have access to the same dataset have likely already spotted those signals and leveraged their competitive advantage.

So, if a record label’s objective is to arrive before everyone else, how is that even possible when everyone is looking at the same signals? What are the other factors that make a record or an artist stand out, and which of these factors can be assessed in a predictive capacity?

What Do A&Rs Look for in an Artist Today?

What A&Rs look for in an artist, according to Ina Rasinger, is consistency:

I think it’s really important that new artists react in some kind of way on all platforms. Once you find something that’s reactive on Spotify, you also have to check how the streaming performance looks on Apple Music. For example, if there’s someone that has two million streams in two months, but only has 300 Instagram followers, that means that it doesn’t travel. It might work well in a playlist, and people don’t mind it on the background, but it doesn’t engage people enough to go to another platform to find out more about the artist. Personally, when I look at data, I just check if certain things are happening on every platform or if it’s solely on one.

As explained in a previous Beats & Bytes article, monitoring performance on all different platforms can help to forecast an artist’s growth pattern, helping A&Rs decide whether an artist is a potential good signing or not.

For better or worse, the music analytics tools of today’s music industry are largely audience-based. Every like, retweet, or stream can give A&Rs an indication if a musician is reacting with a possible audience. Still, it doesn’t give A&Rs any indication relative to the artistry involved in the project.

But with relentless advancements in the fields of AI and machine learning, could it be possible for a computer to be in charge of the whole scouting process? Below is a conceptual map of the main elements that A&Rs are looking for when trying to assess an emerging artist’s value.

Figure 2: Concept map representing the interactions between the audience, artist, and songs, and what elements can be assessed by a futuristic A&R supercomputer.

Three key players are responsible for either the success or the failure of an act. The song is probably one of the most complex conglomerates due to its high number of variables. However, classification algorithms are getting better and better at categorizing features such as mood and genre. Tools like Music Xray can analyze song structure and offer suggestions for improvement, while platforms like Hit Song Deconstructed provide a granular level of detail regarding the songwriting itself. In theory, a machine could evaluate the potential for a song to be a hit. Nevertheless, lyrics and personal expression are still quite hard to decode, especially when considering elements such as metaphors and figurative language.

While we see some impressive improvements in the field of Natural Language Processing (NLP) and Image Recognition, there’s still the problem of identifying the “it factor” of a great artist. Elements such as charisma, stagecraft, and artistic identity cannot be successfully interpreted by a machine. The way a singer moves on stage, the way they interact with their audience — even the timbre of their voice — are all characteristics particular to an individual and cannot be reduced to a matter of “good” or “bad,” “successful” or “unsuccessful.”

Even if we find individual elements that can technically be assessed by AI, the difficulty is assessing an artist holistically and subjectively. Until we have access to a more advanced AI, humans will have a definitive advantage over machines and the field of A&R is likely to involve a combination of data-driven decision making and talent-driven scouting.

The Future of A&R

The A&R office of the future is likely to be divided into specialized sub-sections with some senior or executive figures that will have competencies in diverse fields to manage and coordinate these divisions.

The data team will be in charge of the scouting process, and it will probably render obsolete the entry-level role of the traditional A&R scout in the record label. This team will be formed by trained data analysts using predictive models and machine learning to monitor unsigned artists and scan any platform for possible reactions or engagement. Major and big independent labels will inevitably develop their own internal tools (as is already the case for Sodatone and Warner Music Group) to keep their competitive advantage over other A&R teams. However, majors and indies are likely to end up using data in two very different ways. While big labels try to get to the artists with the best growth stats, independent labels will focus on getting the right artists that match their brand and sonic identity. This strategy has the advantage of avoiding direct competition with big-budget labels as well as focusing the audience on the whole brand and not on just a single artist. In this way, the label becomes a reference point for anyone interested in that particular niche.

Once the data team has processed all the information and organized it in charts and graphs, it’s time for the Cultural and Production team to step in and identify the real artists. This sub-section would be the most similar to traditional A&R and would be in charge of consolidating the branding and the aesthetic of the artists. In the future, it will be even more fundamental to possess 360-degree knowledge of what’s going on in music and also other industries like media, technology, fashion, arts, politics, and so on. For the Cultural and Production team, community-retrieved metadata can help these researchers see if there’s a particular movement that’s gaining momentum, and they can then use this information to enforce strategic decisions about cover art and production style.

The final piece of the A&R office of the future would be the Artist Relations and Mental Health team. These professionals would be responsible not only for the actual signings and managing relations between artists and their management, but also monitoring the mental health of artists. There are too many examples of brilliant performers and authors that have been pushed to the point of rupture by stress, greed, or pride.

Considering the fact that AI will be even more prominent in the future, it will be fundamental for artists to have an empathetic and trustworthy human being that they can relate to. While we are moving toward a world where everything will be duly classified and indexed, truly devoted human interactions will become even more valuable, especially in an industry that has always been dominated by the survival of the fittest. Even if supercomputers end up ruling the world, A&R is, and will always be, a matter of brain and heart.

To read Tommaso’s research in full, click here.

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