Assignment 3: Data Report: Spotify
Harley Dark, Lachlan Janes, Taya McElligott, Hannah O’Reilly
Introduction
Spotify tops the music streaming service charts in terms of profit and users, enabled by coming into the market at the perfect time, surpassing all other competition, innovative use of datafication, and engagement with critical data. This essay will examine the app’s background, its growth, its use of datafication, and the impact that the service has in terms of critical data. Through analysis of these fields, it will be argued that Spotify outpaces its competition in music streaming services by every metric.
Background
Source: Spotify User Stats
Spotify is an internationally used audio streaming service with over 615 million users (Spotify, 2024), making it the world’s most popular audio streaming platform. Spotify works as a subscription-based platform, streaming music, podcasts, audio books and more. The platform’s current net worth is at $59.50 billion (Stock Analysis, 2024) as of May this year.
In 2006, Spotify was founded by Martin Lorentzon and Daniel Ek (Colón, 2024). The Swedish entrepreneurs created Spotify with the goal of providing an audio streaming service which allowed people to legally listen to copyrighted music. The platform was also created as an interactive space, so that users could engage with each other’s profiles and share files all within the same space. After founding Spotify in 2006, Lorzentzon and Ek launched the company two years later in 2008.
A significant reason for Spotify’s initial popularity derives from the fall of streaming service Napster. Prior to the launch of Spotify, Napster served as a platform to share digital music files. Similarly to Spotify, Napster allowed users to share files between themselves, allowing music to become interactive online. First emerging in 1999, Napster’s illegal transferring of copyrighted material meant it shut down in 2001 after the Recording industry Association of America filed a lawsuit against the company for unauthorised distribution of copyrighted material (Harris, 2023). This ultimately meant that when Spotify launched just a few years later, Napster users could engage with a similar interactive streaming platform, meaning the same amount of engagement that Napster received was transferred to Spotify, quickly resulting in a large amount of attention which may not have been seen if Napster hadn’t first existed.
Spotify utilises data analytics so that each user has a unique experience with the platform. Spotify does this by collecting a multitude of interaction data points, including listening habits such as removals and additions to playlists, liking and disliking of music, as well as geographic streaming data, relevant to location-based preferences (Mulkers, 2023). Spotify also offers unique services relevant to users personal preference, such as ‘Daylists’, Discover Weekly playlists, ‘On Repeat’ Playlists, and yearly Wrapped breakdowns of what each individual user has engaged with the most over a year, transferring that data into personalised playlists which you are able to then save and continuously interact with over time.
When analysing Spotify’s use of data, it is important to understand their use of processing and analysing. Spotify uses semantic search in natural language processing (Mulkers, 2023), so that users can be catered to, according to their personal preference. In order to advance continuous user preference, Spotify has used Big Data analytics, machine learning algorithms as well as natural language processing (Mulkers, 2023). By using machine learning algorithms, Spotify is able to accomplish an efficient and effective response to its users data. Machine learning algorithms do this by using computational methods to ascertain information directly from data, which avoids relying on predetermined equations (MathWorks, 2024).
Spotify additionally uses advanced machine learning algorithms including BaRT algorithm optimization which sufficiently contributes to the platform’s ability to adapt to users behavioural patterns. Spotify continuously updates its machine learning model, which is a program that finds patterns and makes decisions from previously unseen datasets (Databricks, 2024), allowing the platform to efficiently expand user satisfaction as a result.
Growth
Spotify Usage and Revenue Statistics (2023)
Spotify’s arrival in 2008 marked a major shift in music consumption. Spotify offered a vast, on-demand music library from any device. The launch of Spotify was immediate, with the platform reaching 10 million users within its first two years on the market. The growth of Spotify only continued to grow throughout the following decades, with Spotify reaching over 70 million users by 2017 (Dean, 2024b). As of today, Spotify remains the leader in the music streaming market, with a staggering 600 million monthly active users and over 200 million paying subscribers (Spotify, 2024)
While Spotify may be the top choice for streaming music, there are still other services that pose as competition to Spotify. Apple Music stands a primary rival to Spotify, with an estimated 80 million subscribers (Sukhanova, 2023). Other platforms such as Amazon Music, YouTube Music and Tencent Music also contribute significantly to the global streaming market. With Spotify making up approximately 37.2% of global music streaming market share and Apple Music following by with approximately 17.6% (Iqbal, 2024)
Spotify leverages two main revenue streams to enhance their growth, these are seen through subscribers and advertisements. Free user experience advertisements between songs, whilst the premium subscribers enjoy uninterrupted listening. Spotify’s revenue has grown exponentially over the years, with their 2023 revenue reaching over $14 billion, which is a 16.4% increase from 2022. Spotify is expected to have an estimated 18% increase at the end of 2024 (Spotify Technology Revenue 2018-2022 | SPOT, n.d.)
Looking ahead, Spotify has identified key areas to solidify their position as the leader in music streaming. One strategic move involves being heavily invested in podcasts. Since launching podcast to their app in 2015 (Gupta, 2021), Spotify saw the potential to dominate the podcast world and plan to do so through acquiring exclusive shows and deals with the major creators of podcasts, further making Spotify a one -stop shop for both music and podcasts (Zhang, 2024) Another Key focus area for Spotify is to expand their market to target a new young population in areas such as Southeast Asian and parts of Africa, areas known to Spotify as having the least users in (Zhang, 2024)
Keeping up with technological advancements is a constant priority for Spotify’s continued success. Spotify is continuously introducing new features that embrace new technology, with their launch of personalised playlists back in 2016 (Goldrick, 2021) and their latest update being an AI playlist tool this year in April (Tencer, 2024). These are all testament to the commitment Spotify has to keep up with the latest trends in technology. In correlation to new technology, Spotify has seen the importance of creating features that cater to both the artist and the fans. Through creating exclusive content releases and direct-to-fan communication tools (Zhang, 2024)
In conclusion, Spotify’s journey in the music streaming market has been an impressive one. By offering the first on-demand music library, they led the world with hundreds of millions of users worldwide. With their growth focusing on podcast dominance, market expansion, and embracing new technologies, Spotify has the great potential to maintain their top position, while further enhancing the music listening experience.
Datafication
Spotify is the world’s premier music streaming platform, as has been argued with statistics in this paper, and a contributing factor to this is the datafication of users’ listening habits. The datafication of music preferences fuels an effective algorithm for recommending music, and the ability to visualise the resulting data in Spotify Wrapped creates an annual viral social media trend. Spotify creates a data story that is enjoyable and accessible, using datafication as a marketing tool, alongside improving ease of listening on the service.
Datafication is the quantification of human life and activity into usable data, often for profit, to gain insight into peoples’ behaviour and thinking (Mejias & Couldry, 2019). This process is vital for large companies, and relies on databanks to analyse, which Spotify fills with collected user data. The kinds of data Spotify collects include but are not limited to: account registration data (names, ages, email addresses), songs played, playlists created, time spent listening to songs and artists, most played genres, GPS location, at what time users listen to music, and where users listen to music (Jennings, 2019). Spotify uses this data to advertise to users with partnered companies, but also to recommend songs, albums, music videos, artists, playlists, and concerts. The more time that users spend listening, the more data Spotify has to analyse and use to recommend products and songs, therefore Spotify is incentivised to keep users listening. This encourages ‘ubiquitous listening’, listening to music in the background whilst attending to other activities, which provides Spotify with more advertisement revenue as well as data (Pederson, 2020). Spotify uses datafication as a process to turn the way that users listen to music into profit for the company through advertisement, an algorithm to keep users listening, and a database from which to analyse and increase the quality and efficiency of the service rendered.
Spotify Wrapped is such a viral success for Spotify that it has been emulated by companies in a multitude of different industries, from music streaming platforms to video game companies, and even dating apps (Peck, 2023). This feature utilises collected user data, from January 1st to around October 31st annually (Pequeño, 2023), to create personalised data visualisation for each user, made from their taste profiles. Taste profiles are built from user listening habits, analysed with audio models (to assess lyrical content), and compared with ‘nearest approximate neighbours’ to expand user recommendations (Walker, 2023). Spotify Wrapped originated from Spotify’s “Year in Review”, but went viral as Wrapped with its incorporation of quirky features such as ‘audio auras’ and ‘listening personality types’, which capitalise on user’s desire to express their individuality and personality to friends and followers on social media, with the help of easy built-in share buttons (Guzman, 2023). Spotify Wrapped is a use of datafication that surprises and delights users, publicises the service and its artists, encourages listeners to incorporate the application into their identity, and is an example of an incredibly successful use of datafication and data stories.
Spotify’s use of datafication, and the way in which it presents and shares data stories, is incredibly successful. This is proven by the widespread emulation of its Wrapped feature, and how socially viral the feature is. The extent to which Wrapped affects users’ perceptions of themselves and normalises defining oneself by Spotify, however, is a contentious subject that will be explored next as part of an exploration of Spotify’s engagement with critical data.
Spotify’s use of data mostly reinforces the traditional power structures and dynamics between companies or customers, and especially in the internet age, between services and users. These days, it is seen as commonplace for users to sacrifice their personal information and the privacy of their browsing or streaming activity for the privilege to use online services, and especially when they’re free, it can even be perceived as a fair trade – especially when that very data is used to optimise a service.
Critical Data
A critical data studies approach is most concerned with how data can alter or reinforce power structures, and Craig Dalton, Linnet Taylor, and Jim Thatcher argue that critical data studies “calls attention to subject formation within […] data regimes, for a critical examination of where the interpellation of the individual emerges in algorithmic culture” (Dalton et al., 2016, p. 1). Within the algorithmic culture of Spotify, and other contemporary internet services, where can one’s individuality be found? It is worth questioning whether Spotify’s algorithm has a homogenising effect on the choice of music that users engage with. For only A$14 a month, or for free with advertisements (Spotify, n.d.), Spotify not only sells the availability and accessibility of seemingly the entire history of recorded music – it also pushes the service’s now famed algorithmic curation and recommendation, making discovering new music easier by reducing the user’s agency, and taking away those difficult decisions.
As mentioned prior, a social aspect has been intrinsic to Spotify’s model even since its inception, and it primarily engages with this through a use of data. It is not just a music streaming service but a form of social media too. Users of Apple Music (and other services) can attest to a feeling of being left out on much of the discourse surrounding Spotify as a whole, whether it be the curated ‘Discover Weekly’ playlists, and of course, Spotify Wrapped. It offers largely an identical service – a library consisting of much of history’s recorded music – but Apple Music can be seen as a less worthy competitor for its lack of visible engagement with data.
Spotify recognises that there is a draw to the data it collects about users, naturally for their own purposes – advertising, selling data, improving their algorithm – but also for its users. Simply put, its collection and display of streaming data – how many monthly listeners an artist has, how many times a song has been streamed – posits popularity as objective. This very system, as well as its algorithmic recommendation, strives to make taste quantifiable. The appeal of such accessible listening data is that it allows you to feel part of a community, and compare yourself to the listening activities of others. And it also serves to empower the user, who can directly see their very own streaming habits affecting the success of certain artists and songs.
Prior, the divide between artist and listener felt much greater. This is despite the fact that musicians are making less money than they ever have through the sales (or rather streams) of their music. In fact, as the primary audio-streamer, Spotify’s biggest disruptive effect was how it altered the music industry, changing the revenue model from the sale of physical albums to the streaming of individual tracks. According to Billboard and RIAA (Recording Industry Association of America), 150 on-demand streams is considered to be the equivalent to the sale of one track, and thus, 1500 streams is the equivalent of 10 tracks, or a single album sale (RIAA, n.d.). Spotify and the further datafication of the music industry has altered the metrics of artist success from units sold to the (previously unmeasurable) amount of times a song, or album, has actually been engaged with, but to an unreasonable degree. Under the more traditional, pre-streaming model, many artists today would have much higher sales numbers, and would be earning a higher income accordingly.
Conclusion
By analysing Spotify’s history, its economic and cultural growth, the impact of its algorithm and Spotify Wrapped, and the way in which it utilises critical data, this essay has argued for Spotify’s dominance in the music streaming service industry. Spotify has changed the music industry from purchase to streaming-based, and led the market with its subscription-based model, whilst pioneering use of user data to generate suggestions and playlists. This essay has provided evidence and analysis that contends that the factors of Spotify’s creation, development, success, data collection, and use of data, have culminated in the company’s ability to consistently be more profitable and used than any other music streaming service.
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