In the first quarter of 2025, the music streaming service Spotify reached an all-time high with 678 million active users worldwide. This marked an increase of around ten percent in just one year. What is Spotify? Spotify is a music streaming service that offers digital audio content. Basic audio content can be accessed for free whereas premium user subscriptions enable users to access offline mobile content as well as listen to music without advertising. In the first quarter of 2025, the company reported 268 million paying subscribers. Launched in 2008, Spotify originated in Sweden before expanding to European markets and the United States in 2011. Spotify’s U.S. launch was strongly marketed through Facebook, with the music streaming app profiting from the social listening integration via social media. Part of Spotify’s appeal can be attributed to the user- and brand-curated playlists, which can be shared publicly or between friends. Fans may choose what to listen to based on their current mood or preference, and the ability to share such content provides an element of social connectivity ordinarily reserved for networking sites.

In 2008, a small startup in Sweden set out to tackle a big problem, how to give people easy access to music without turning to piracy. That company was Spotify. Fast forward to 2025, and Spotify isn't just a music platform, it’s a cultural cornerstone. Whether you’re discovering indie artists,...

In the first quarter of 2025, Spotify had 423 million monthly active users, which was a little less than the record high of 425 million in the fourth quarter of 2024. Meanwhile, this figure represents an increase from 388 million users in the previous quarter, highlighting Spotify's ability to attract and retain users through it's free, ad-supported tier. The company's strategy of offering a free option alongside its premium subscription service appears to be paying dividends in terms of user acquisition. User base growth and market penetration Spotify's total active user base hit an all-time high with 678 million active users in Q1 2025, marking a surge of 75 million users in just one year. This growth encompasses both ad-supported and premium subscribers, with the latter reaching 268 million in the same quarter. The platform's expansion is particularly strong in Europe and North America, which together account for 63 percent of Spotify's premium subscribers. Europe remains Spotify's strongest market, representing 37 percent of paying users. Revenue challenges and competitive landscape Despite the robust user growth, Spotify faces challenges in monetizing its expanding ad-supported user base. The company's premium average revenue per user (ARPU) has been declining since 2017, reaching 4.73 euros in first quarter of 2025. This trend suggests that while Spotify excels at attracting users, converting them to higher-paying tiers remains a challenge. The company operates in a competitive landscape that includes rivals like Pandora and Apple Music, forcing Spotify to continually innovate and improve its offerings to maintain its market position.

Spotify employs thousands of people around the world, with the majority based in the United States – 3,690 as of 2024, down around 20 percent from a year earlier. Second was Sweden with 1,397 employees, and 874 people working for the company at that time were based in the United Kingdom. The company is legally domiciled in Luxembourg and has its administrative headquarters in Stockholm where the streaming platform was founded back in 2006. Working at Spotify: equality and diversity data The streaming behemoth fascinated interested parties for years before finally going public, and with all eyes on Spotify after its first profit in early 2019, it doesn’t look like the media hype around one of the world’s most major streaming services will die down any time soon. Whilst many argue that all publicity is good publicity, internationally dominant forces like Spotify are hearing loud pleas for transparency, particularly when it comes to its workforce. As minority groups fight for representation, it has become increasingly important for market leaders to not only demonstrate support for diversity in the workplace, but to admit to how their own businesses perform in that regard as well. In July 2018, Spotify published data showing the gender distribution within multiple categories of its business, including how many women work for Spotify as a percentage of total employees, and in which roles. The company revealed that women made up 38.4 percent of managers and 38.7 percent of all persons employed, and almost 32 percent of leadership roles (director level and above) were also filled by women. Spotify also disclosed the ethnicity breakdown of its corporate structure, announcing that just under 15 percent of its employees were Asian, 5.5 percent were Hispanic and 6.1 percent Black. Many will argue that there is still work to be done – and certainly in terms of achieving a more even balance across the board within Spotify, there is. However, transparency among large companies is generally favorable, and a time may indeed come when more of Spotify’s millions of website visitors, users and subscribers grow curious about how one of their favorite streaming services operates from the inside.

Spotify's premium average revenue per user (ARPU) amounted to 4.69 euros in 2024, up from 4.39 euros in the previous year—an increase of seven percent. While ARPU had been declining since 2017, with the most significant drop occurring between 2016 and 2017, it rebounded in 2024.

This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists.

The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where

• user_id is a hash of the user's Spotify user name
• artistname is the name of the artist
• trackname is the title of the track and
• playlistname is the name of the playlist that contains this track.

The separator used is , each entry is enclosed by double quotes and the escape character used is \.

A description of the generation of the dataset and the dataset itself can be found in the following paper:

Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015.

In 2024, around 70 percent of Spotify's revenues went mainly towards royalty payments towards music rights holders and other fees.

nabihaz90/spotify-user-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

In 2024, Taylor Swift was the most streamed artist on Spotify. Her songs were streamed over 28 billion times within the year. The second most streamed artist was The Weeknd with more than 13 billion streams in 2023.

Spotify Million Playlist Dataset Challenge

Summary

The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights).

Background

Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes.

Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.

But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special.

The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists?

By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love.

Dataset

To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology.

In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page.

Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset.

Dataset Contains

1000 examples of each scenario:

Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks

Download Link

Full Details: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge Download Link: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files

The music streaming market refers to the digital distribution of music content, enabling users to access and listen to songs, albums, and playlists on demand via the internet, without the need for physical media. This market has evolved with the proliferation of mobile devices, high-speed internet, and cloud-based services, making it easier for consumers to enjoy music anytime, anywhere. Music streaming platforms such as Spotify, Apple Music, YouTube Music, and Amazon Music dominate the industry, providing users with subscription-based services, freemium models, and ad-supported options. The rise of artificial intelligence and data analytics also plays a significant role in music streaming, offering personalized recommendations and curated playlists based on user preferences and listening habits. Music streaming is revolutionizing the music industry by reducing piracy, offering a wider variety of music, and providing revenue-sharing opportunities for artists, which have become essential for the growth of the global market. Several factors drive the growth of the music streaming market, including increased smartphone penetration, faster internet connections, and the growing popularity of on-demand media consumption. Recent developments include: November 2022: Mercedes Benz automobiles now include Apple Music's highly acclaimed audio with support for Dolby Atmos as a natural experience, according to a joint announcement from Apple Music and Mercedes Benz. This fulfills a shared commitment to provide customers throughout the world with the best music experience., October2021: Amazon has announced that users of the unlimited tier of the service can now stream music blended in dynamic audio from more devices than ever before, including iOS (iPhone Operating System) and Android systems with their existing headphones and select devices that support Alexa.. Key drivers for this market are: Growing popularity of on-demand media consumption. Potential restraints include: licensing agreements with record labels and content providers can limit the availability . Notable trends are: Rising adoption in digital comic is driving the market growth.

In 2024, Taylor Swift was the artist with the most streamed album on Spotify. Her album "THE TORTURED POETS DEPARTMENT" was streamed over 6.6 billion times in 2024. She also entered the top 10 with her album "Lover" in sixth position, having registered nearly 3.3 billion streams on Spotify.

Spotify Revenue, Expenses and Its Premium Users contains the number of premium users, number of Ad-supported users and total monthly active users (MAUs).

MAUs include number of premium users as well as number of Ad-supported users.

Note : Sum of Premium Users and Ad-supported users can have some difference from MAUs. Note : All money figures are in Euro millions except ARPU which is in Euro and as it is. **Note : All users figures are in millions. ** Note : Kindly Ignore the last row.

Following definitions: MAUs : It is defined as the total count of Ad-Supported Users and Premium Subscribers that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated. Premium MAUs : It is defined as users that have completed registration with Spotify and have activated a payment method for Premium Service. Ad MAUs : It is defined as the total count of Ad-Supported Users that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated. Premium ARPU : It is average revenue per user which is monthly measure defined as Premium subscription revenue recognized in the quarter indicated divided by the average daily Premium Subscribers in such quarter, which is then divided by three months. Cost of Revenue : It is expenses done by company.

Photo by Alexander Shatov on Unsplash

This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists. The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists. The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists. Each of the aforementioned entities are represented by unique IDs (Spotify URI). Data is stored into following tables:

album

artist

track

playlist

track_artist1

track_playlist1

album

| id | name | uri |

id: Album ID as provided by Spotify name: Album Name as provided by Spotify uri: Album URI as provided by Spotify

artist

| id | name | uri |

id: Artist ID as provided by Spotify name: Artist Name as provided by Spotify uri: Artist URI as provided by Spotify

track

| id | name | duration | popularity | explicit | preview_url | uri | album_id |

id: Track ID as provided by Spotify name: Track Name as provided by Spotify duration: Track Duration (in milliseconds) as provided by Spotify popularity: Track Popularity as provided by Spotify explicit: Whether the track has explicit lyrics or not. (true or false) preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null uri: Track Uri as provided by Spotify album_id: Album Id to which the track belongs

playlist

| id | name | followers | uri | total_tracks |

id: Playlist ID as provided by Spotify name: Playlist Name as provided by Spotify followers: Playlist Followers as provided by Spotify uri: Playlist Uri as provided by Spotify total_tracks: Total number of tracks in the playlist.

track_artist1

| track_id | artist_id |

Track-Artist association table

track_playlist1

| track_id | playlist_id |

Track-Playlist association table

• - - - - SETUP - - - - -

The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.

spotifydbdumpschemashare.sql contains the schema for the database (for reference): spotifydbdumpshare.sql is the actual data dump.

Setup steps: 1. Create database 2. mysql -u -p < spotifydbdumpshare.sql

• - - - - PAPER - - - - -

The description of this dataset can be found in the following paper:

Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset compiles the tracks from all of Beyoncé's albums available on Spotify, showcasing the evolution of one of the most influential artists in the music industry. It represents a comprehensive array of genres, influences, and musical styles that Beyoncé has explored throughout her career. Each track in the dataset is detailed with a variety of features, popularity, and metadata. This dataset serves as an excellent resource for music enthusiasts, data analysts, and researchers aiming to explore the impact of Beyoncé's music, identify trends in her musical evolution, or develop music recommendation systems based on empirical data.

Scope of the Data:

The focus of this dataset is on providing a comprehensive view of Beyoncé's musical releases on Spotify, specifically tailored to showcase her creative output. To this end, the dataset includes tracks from the following album types: - Albums: Full-length albums released by Beyoncé, encapsulating a range of her musical styles and eras. - Singles: Standalone single releases, highlighting key songs that have been released independently of her full albums. It's important to note that this dataset deliberately excludes compilation albums. Compilations, which often contain a mixture of tracks from various artists or previously released tracks by Beyoncé, are not included to maintain a focus on her original releases and to provide a clearer picture of her artistic evolution.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on albums and tracks by Beyoncé. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing: To process and structure the data, Python scripts were developed using data science libraries such as pandas for data manipulation and spotipy for API interactions, specifically for Spotify data retrieval.

Workflow: - Authentication - API Requests - Data Cleaning and Transformation - Saving the Data

Attribute Descriptions:

• artist_name: the name of the artist (Beyoncé and collaborators)
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with Beyoncé
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for - dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings
• speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Possible Data Projects:

• Trend Analysis in Beyonce's Musical Evolution
• Mood and Musical Elements in Beyonce's Tracks
• Beyonce's Influence on the Music Industry Analysis

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Beyoncé's albums and tracks, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations. - Compliance with Terms of Service: Users should adhere to Spotify's Terms of Service and Developer Policies when utilizing this dataset. - Copyright Notice: The dataset presents music track information including names and artist details for analytical purposes and does not convey any rights to the music itself. Users must ensure that their use does not infringe on the copyright holders' rights. Any analysis, distribution, or derivative work should respect the intellectual property rights of all involved parties and comply with applicable laws. - No Warranty Disclaimer: The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others. - Ethical Use: Users are encouraged to consider the ethical implications of their analyses and the potential impact...

Content

This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly.

  Usage

The dataset can be used for:

Building a Recommendation System based on some user input or preference Classification purposes based on audio features and available genres Any other application that you can think of. Feel free to discuss!

  Column… See the full description on the dataset page: https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset.

It started with a pair of headphones and a curiosity, what's this song playing? In coffee shops, dorm rooms, and crowded subway rides, Spotify quietly transformed how we consume music. Over the years, it’s become more than just a streaming app. It’s a personal DJ, a data mirror of our...

Music streaming has its roots in the peer-to-peer file sharing industry. Napster, Limewire and BitTorrent were at the forefront of changing the way people thought about music, and while they only...

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

As of the first quarter of 2025, Europe accounted for ** percent of Spotify monthly active users. The popular Swedish streaming service had a strong user base in Latin America, accounting for ** percent of Spotify's total *** million MAUs at that time.
Spotify Since its launch in 2008, Spotify has grown into the most widely used music streaming platform in the world, controlling over a ***** of the industry’s global market share. Despite being in direct competition with some of the biggest names in the tech industry, the company has managed to accumulate over *********** million paying subscribers and millions more free and ad-supported users. Spotify has ensured that this massive userbase has led to increasing financial success. The company reported an annual revenue of around *** billion euros in 2018, outpacing its 2017 figure by well over a ******* dollars. Music streaming Music has always been an important form of human expression and entertainment, but never before has it been so easily accessible. Thanks to the growing popularity of smartphones and streaming services, hundreds of millions of people around the world have access to an almost unlimited library of music at the touch of a button. As of 2024, streaming accounted for ** percent of the total music industry revenue in the United States, up from ** percent six years earlier. This highlights the massive influence of companies like Spotify on the music industry as a whole.