If you open Spotify on a random weekday, you may be greeted with curated playlists like Discover Weekly or Release Radar. Convenient? Yes, but also a masterclass in turning data into strategy.
Although Spotify presents itself in the business of streaming, it could be argued that the true intentions englobe capturing and leveraging information from users. Every skip, replay, followed artist, genre repetition, playlist, queues become a data point. Over time, Spotify identifies your different moods, music for happiness, music for studying, focusing, running. This behavioral data feeds the algorithms that curate your experience and makes you engaged with the platform. Features like autoplay, queuing endless similar songs as well as AI beta programs such as DJ Livi ensures that there is no natural stopping point, where said engagement is engineered, not accidental (Morris and Powers 2015: 112-113).
Ultimately, this business model is shaping the music industry itself. Record labels are spending heavily on Spotify playlist placements, some are accused of buying spots in autoplay to promote their artist’s music. Similarly, like TikTok, some artists start crafting music with streaming in mind, shorter intros, catchy hooks within thirty seconds to count as an official stream before it gets skipped. Success is no longer about radio airplay or physical sales; it’s about fitting into Spotify’s recommendation logic.
Strategically, this illustrates the strength of platform ecosystems. Spotify connect listeners with artists, labels and advertisers, but the glue is information. By controlling how music is discovered and consumers, artists with the help of Spotify can understand to whom promote their music, in which playlists should they be placed at, calculate fan expectation with pre-save options, etc. This data-driven personalization is not just a feature, it keeps competitors and partners locked in. The business is now run by algorithms that detect patters in the users listening habits and curates their experience filtering millions of options that will lead to a more focused engaged consumption (Bonini and Gandini 2019).
Even though the user might think they’re in control in the platform, what may feel like a choice, could easily be what the algorithm “decided”. In essence, the message is clear; in the digital economy, information isn’t a byproduct, it is the product.
Morris, J.W. and Powers, D. (2015) Control Curation and musical experience in streaming music services. Creative Industries Journal: 8(2) 106-122.
Bonini, T and Gandini, A. (2019) “First Week Is Editorial, Second Is Algorithmic”: Platform Gatekeeping on Spotify. Social Media + Society, 5(4).
This is a very interesting topic, thank you for diving deeper into it!
I think many people are not aware of how much data is generated by their listening habits and how this data in turn is used to generate personalized playlists.
As we are moving away from more “traditional” ways of listening to music, such as the radio or on CDs, these algorithms gain more and more importance. However, one disadvantage that I see with these algorithms, is the lack of discoverability for small and up-and-coming artists. It might be much harder for them to get discovered organically as people often stay within their own music bubble. While Spotify already has playlists that highlight these smaller artists, it might also be important for them to be included in people’s regular generated playlists.
Great blog. Personally I use Spotify a lot and I find the personalized playlists like Discover Weekly very helpful, since they stop me from hearing the same songs on repeat and allow me to discover new artists.
I had not really thought about how much our listening is influenced by algorithms until now. What feels like a personal choice is actually part of a bigger system that collects data and shapes how I and other people listen, but also how artists create their music to fit the platform. This makes me wonder how much freedom we really have in our music taste, and how much is guided by Spotify itself.