As digital becomes at the forefront of all entertainment sectors, we are faced with a
conflict of over-saturation of content. Gaming, sports, music, audio, video, and social content all compete for not only the same time and attention, but also capacity of fandom. As we transition to a greater world of AI-generated content, this dilemma of over-abundance of content presents an issue of devaluation of content, and ensuing the risk of participating in the ever-growing ‘doom scroll’. Simultaneously, the rise of ‘attention inflation’ is the phenomenon that consumers
are maximizing their attention resource by consuming multiple formats and mediums, spurring a rise in asynchronous consumption, and valuing different formats of content differently.
In the context of the music industry, the disruption of music streaming services now a decade later has evolved into powerful algorithmic recommendation engines. Now combined with social media platforms and algorithms, algorithmic entertainment platforms have skyrocketed the digital evolution and become the most important knowledge technologies and information systems in the music industry.
Search engines, recommendation systems, and edge algorithms are not only driving music discovery in a digital ecosystem, but are also shaping taste, identity, social networks, and online discourse around music identity (Data Society). This also means that algorithm-driven entertainment platforms like platforms like Spotify, Instagram, YouTube and TikTok are not the places where consumers go to hear or watch what they want, but to learn what they want.
The digitization of music consumption dates back to the rise of streaming. Over a decade ago, the music industry was on the brink of collapse. Peer-to-peer file sharing platforms like Napster and Limewire triggered the precipitous decline in US recorded-music revenue from an all-time high of $14.6 billon in 1999 to $6.7 billion in 2015 (RIAA).
Rather than physical formats like CD or cassettes, these illegal services were the first to bring about the consumption music as digitized sound files accessible with the click of a mouse. A whole generation was growing up with the idea that access to music should essentially be free, and the rampant spread of these music privacy services was at a rate the music industry failed to grasp (Robinson, 2020).
The brainchild of Swedish founders Daniel Ek and Martin Lorentzon, Spotify was able to save the recorded music business. The easily searchable site offered a library of music to last more than a lifetime for just 9.99$ a month. The platform launched in 2011 in the US and opened the floodgates. From 7% of the US market in 2010, the platform became 83% of total market share by the end of 2020. In 2020 as well, the recorded music industry saw their fifth year of consecutive revenue growth, topping $12.2 billion according to the RIAA.
It’s no doubt that Spotify became a global music consumption market leader and led the charge towards economic stability and growth that the music industry can experience today (Robinson, 2020). Simultaneously, the shift in technology now presents digital music listeners to new ways that music is discovered, stored, and consumed. Reporting 489 million monthly active users, Spotify’s access to huge repositories of user data brought about a powerful music recommendation system that is the heartbeat of music discovery on the platform (Spotify, n.d.a).
On the contrary, with a latest report of 100,000 new songs a day, users are becoming increasingly reliant on algorithmic recommender systems and automated discovery features to discover and curate their music listening experience (Barna, 2017).
Much like Google, in semantic search, algorithmic engines use behavior and language indicators to understand what the user is actually searching. Most search engines also take advantage of the “Knowledge Graph” which is to anticipate what a user might search for after a given query. Using predictive search, digital service providers of audio and video can help to recommend the next piece of content based on your current interest. Through algorithmic and human curated in-house, or ‘editorial’ playlists, Spotify has been able to build playlist brands that have become key to artist strategies in promotion (Morgan, 2020).
With a growing library of music (Spotify boasts more than 70 million songs), users increasingly interact with or rely on algorithmic filtering, curation and recommendation to find music (Spotify, n.d.a). Spotify uses machine learning to analyze patterns of acoustic, user, contextual, behavioral and semantic user knowledge, which include search habits, skip rates, track engagement, and more to create ‘personalized predictions’ to the user in the form of algorithmic curation in playlists, radios, or in the “queue”. Spotify offers a multitude of personalized predictions for their users in a series of playlists in the “Made For You” sections, such as Daily Mixes, Discover Weekly, and Release Radar. In a recent “Made to be Found Report”, Spotify reported that “33% of all new artist discoveries happen through "Made For You" recommendation sessions”.
As platforms fight for consumer time, the application of machine learning and algorithms within these platforms have simultaneously optimized their by servicing content that the user will like. Asher Tobin Chodos summarizes the effect of the Spotify recommendation engine and implications on the future of personalization succinctly: “Spotify is not a machine that delivers requested goods for a fee; it is an open-ended, benevolent, and exploratory experience in which it is assumed that the data surveilled from your behavior can only enrich your relationship with the
program and improve the quality of your recommended content” (2019).
From a cultural viewpoint, these powerful algorithms provide DSPs have the potential to influence human decision and discovery, with the possibility of prescribing and enabling the cultural practices and experiences with music to all users (Morris and Powers, 2015). ‘Mood’ and ‘taste’ are now quantified to algorithmic and commercial data points for advertisement and discovery rather than subjective human traits and experiences (Prey, 2019). Within this infrastructure, previously subjective human processes of curation and discovery are increasingly
mediated and substituted by algorithmic systems.
While the short-term benefits of recommendation engines are well known, the long term benefits are more abstract as it relates to the cultural landscape. As users are prescribed preferences by the algorithm, the cultural landscape is experiencing a steady erosion of mass market, and instead the rise of more niche, cultural groups of fans that identify with similar interests.
Music fandom and identities are fragmenting to be concentrated in micro-markets of the internet era, that are often built on identity, and shaped by the prescribed content and preferences service by the recommendation algorithm across the media and content they consume on a daily basis.
References
Chodos, A. T. (2019). What Does Music Mean to Spotify? An Essay on Musical Significance in the Era of Digital Curation. INSAM Journal of Contemporary Music Art and Technology,
Freeman, S., Gibbs, M., & Nansen, B. (2022). ‘don’t mess with my algorithm’: Exploring the relationship between listeners and automated curation and recommendation on music streaming services. First Monday. https://doi.org/10.5210/fm.v27i1.11783
Laudon, K. C., & Laudon, J. P. (2020). Essentials of MIS (14th ed.). Pearson Education (US).
https://online.vitalsource.com/books/9780136501046 Reintroducing scarcity How entertainment can find value amid the growing digital clutter.
MIDiA Research. (n.d.). Retrieved March 7, 2023, from https://www.midiaresearch.com/
Robinson, K. (2021, April 13). 15 years of Spotify: How the streaming giant has changed and reinvented the music industry. Variety. Retrieved March 7, 2023, from
https://variety.com/2021/music/news/spotify-turns-15-how-the-streaming-giant-has-changed-and-reinvented-the-music-industry-1234948299/
Shin, D., Chotiyaputta, V., & Zaid, B. (2022). The effects of cultural dimensions on algorithmic news: How do cultural value orientations affect how people perceive algorithms? Computers in Human Behavior, 126, 107007. https://doi.org/10.1016/j.chb.2021.107007
T. Bonini and A. Gandini, 2019. “‘First week is editorial, second week is algorithmic’: Platform gatekeepers and the platformization of music curation,” Social Media + Society (21 November). doi: https://doi.org/10.1177/2056305119880006, accessed 30 December 2021.
E. Barna, 2017. “‘The perfect guide in a crowded musical landscape:’ Online music platforms and curatorship,” First Monday, volume 22, number 4, at
https://firstmonday.org/article/view/6914/6086, accessed 28 December 2021. doi:
https://doi.org/10.5210/fm.v22i4.6914, accessed 30 December 2021.
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