How many paid subscribers does Spotify have? As of the second quarter of 2025, Spotify had 276 million premium subscribers worldwide, up from 246 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalog of music licensed through multiple record labels, as well as create and share playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users can also register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

By 2025, Spotify will have ***** million users, over ****** million more than the **** million recorded in 2020. However, a gradual decline in Pandora's user base is anticipated to continue over the coming years, with the company losing *** to ***** million active listeners per year. Spotify and Pandora – more information Pandora In 2018, Pandora was most popular among users aged ** to ** and users aged 55 or above, whereas adults in the ** to 24-year-old age bracket was by far the company's weakest market segment. Pandora's user base may be flatlining compared to Spotify's, but Pandora’s revenue jumped from **** million U.S. dollars to almost *** billion in a decade, showing incredible and consistent growth during that time. Pandora generates most of its revenue from advertising, although has grown increasingly reliant on revenue from subscription services. Pandora was acquired by Sirius XM in February 2019 for *** billion U.S. dollars. Spotify In 2018, Spotify reported *** billion euros in revenue, following a history of significant growth. Spotify was operating at a loss for years, but reported its first quarterly operating profit at the beginning of 2019. the company has placed a stronger focus on podcasts and will continue to do so according to industry experts and company reports, and is one of the most popular podcast providers in the United States.

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,...

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.

This statistic shows the total number of users of Spotify, Apple Music and Deezer in the Netherlands as of the first quarter of 2018 (in thousands). Exact user numbers for certain apps are rare to find in the Netherlands. According to the source, the total number of Spotify users in the Netherlands was forecast to reach *** million in the first quarter of 2018. Additionally, the source mentions that *** million users are paid users. This is because a part of these subscriptions come from a third party, such as telecom provider KPN where Spotify is a part of their telecom package. As of January 2019, Spotify had an online reach, a percentage of visitors that visited content at least once during a certain period of time, of *** million in the Netherlands in January 2019.

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

A dataset covering Spotify usage and artist performance in 2025, including metrics like monthly active users, premium subscriber counts, demographic breakdowns, and playlist analytics.

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...

In 2017, *** million Italian users listened music or podcasts on Spotify, which were *** million users more than in the previous year. In May 2019 a large quota of users preferred to access it from mobile devices, as the unique daily audience using smartphones was over *** million users. The online reach of Spotify through desktop computer was under ** thousand users.

Users perception on Spotify  

Apart from the usage, users seem to have a positive perception of Spotify as a brand. In the analysis of web conversations, users associated Spotify with positive values. Among the key characteristics, users mentioned utility and quality of the streaming services, together with popularity of the platform.

Beyond Spotify  

Spotify has been the favorite music streaming platform between 2015 and 2016, while the results of a more recent survey in 2018 showed that YouTube was also used as a platform of music streaming, and had a higher preference rate.

Music and Streaming Service Market Outlook

The global music and streaming service market size was valued at approximately USD 28 billion in 2023 and is projected to reach around USD 61 billion by 2032, growing at a compound annual growth rate (CAGR) of about 9%. This substantial growth can be attributed to several factors, including the proliferation of high-speed internet access, the growing popularity of digital content consumption, and evolving consumer preferences towards on-demand services.

One of the primary growth drivers of the music and streaming service market is the increasing penetration of smartphones and other connected devices. As of 2023, over 80% of the global population owns a smartphone, facilitating easy access to streaming services. The convenience offered by smartphones, coupled with the availability of affordable data plans, has significantly bolstered the consumption of digital music and video content. This trend is expected to continue, driving the market further as more users switch from traditional media to digital platforms.

Another pivotal factor contributing to market growth is the shift in consumer behavior towards personalized content. Today's consumers demand tailor-made experiences, which streaming services are adept at providing through advanced algorithms and machine learning techniques. These technologies analyze user preferences to recommend content that aligns with individual tastes. This personalized approach not only enhances user satisfaction but also increases user engagement and retention rates, thereby boosting revenue streams for service providers.

The rise of social media and its integration with streaming platforms is yet another factor propelling market growth. Social media platforms have become a vital tool for discovering new music and artists, significantly influencing the streaming habits of users. Additionally, collaborations between social media giants and streaming services create a synergistic effect, enhancing the reach and visibility of streaming platforms. This interconnected ecosystem of social media and streaming services is expected to play a crucial role in driving the market forward.

In the realm of streaming services, the role of a Background Music Service is becoming increasingly significant. These services provide curated playlists and soundtracks tailored to enhance the ambiance in various settings, such as retail stores, restaurants, and fitness centers. By offering a seamless auditory experience, businesses can create a more inviting atmosphere for their customers, potentially increasing dwell time and customer satisfaction. The integration of background music into commercial spaces is not just about entertainment; it also serves as a strategic tool for brand differentiation and customer engagement. As more businesses recognize the value of auditory branding, the demand for specialized background music services is expected to rise, contributing to the overall growth of the music streaming market.

From a regional perspective, the Asia Pacific region is poised for remarkable growth, driven by the increasing adoption of digital services and the burgeoning young population. Countries like India and China are witnessing a surge in the number of internet users, creating substantial opportunities for streaming service providers. Moreover, the rising disposable incomes in these regions are enabling more consumers to opt for premium subscription services, further fueling market expansion.

Service Type Analysis

Within the music and streaming service market, service types can be broadly categorized into subscription-based, ad-supported, and others. Subscription-based services, such as Spotify Premium and Apple Music, charge users a monthly fee for ad-free and unlimited access to their content libraries. This model has gained significant traction due to the premium user experience it offers. Users are willing to pay for uninterrupted streaming, high-quality audio, and additional features like offline listening.

Ad-supported models, such as YouTube Music and Spotify Free, offer free access to content but include advertisements. This model attracts a vast user base, particularly in emerging markets where consumers may be hesitant to pay for subscriptions. Advertisements serve as a revenue stream, enabling these platforms to monetize their vast user base. Despite the presence of ads, the model remains popular due to its accessibility and aff

In the second quarter of 2025, Spotify had over ** million premium subscribers in Latin America, up from ***** million in the second quarter of 2024 – an annual increase of about ** percent. Meanwhile, the number of Spotify monthly active users (MAUs) in Latin America grew by about *****percent in the second quarter of 2025 compared to the second quarter of 2024. Brazil and Mexico spend the most time streaming music Recent data shows that internet users in Mexico and Brazil devote more than * hours of their daily time to streaming music. However, while Brazil experienced a year-on-year growth of * percent in this regard, in Mexico, music streaming time declined by a fraction of a percentage point in the same period and by close to * percent since 2020. It is yet too early to say whether this trend will be a lasting one. Spotify still the top streaming choice What is certain is that music listeners in both countries rely heavily on Spotify. The company’s dominance is especially prominent in Mexico, where more than ************** of music streamers subscribe to the service. Interestingly though, Spotify has lost some ground in favor of other services such as YouTube Music, Apple Music, and Amazon Prime Music since 2021. In Brazil, Spotify is the leading streaming service, however, the market there is more varied. Here, the difference in usage between Spotify and its competitors such as YouTube Premium, Deezer and Amazon Music is not so pronounced, indicating perhaps an audience more open to less obvious players.

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

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.

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

This statistic shows the online reach of the brand Spotify in the Netherlands, either through a desktop, tablet or smartphone, from July 2017 to August 2020. Reach is defined as the percentage of visitors that visited specific content at least once during a certain period. The Netherlands has two different methods to measure online reach (in Dutch: bereik) of brands: GfK DAM and the "Nederlands Online Bereiksonderzoek" or NoBO. The numbers provided here are from the latter.

According to NoBO, Spotify had an online reach of 33.9 percent in the Netherlands in August 2020. This is an increase when compared to the same period one year before. It was estimated that the total number of Spotify users in the Netherlands was going to reach 6.2 million in the first quarter of 2018.

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

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...

This dataset was extracted from the Spotify platform using the Python library "Spotipy", which allows users to access music data provided via APIs. The dataset collected includes about 1 Million tracks with 19 features between 2000 and 2023. Also, there is a total of 61,445 unique artists and 82 genres in the data.

This clean data has been prepared and utilized for research purposes. Its significance lies in its potential to unravel patterns and predict song popularity prior to its release. This dataset could be used to create various predictive models with machine-learning/deep-learning techniques.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5006553%2F317c91feba2aa097559fd59bf408e944%2Ftable_desc_spot.png?generation=1687699189506270&alt=media" alt="">

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.

💁‍♀️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...