How many paid subscribers does Spotify have? As of the third quarter of 2025, Spotify had 281 million premium subscribers worldwide, up from 252 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.

As of March 2018, Spotify’s user base was dominated by Millennials, with ** percent of its users aged 25 to 34 and ** percent aged between 18 and 24 years old. The streaming giant has permanently altered how consumers discover, engage with and share music, and according to a 2018 survey, Spotify reaches almost **** of 16 to 24 year olds in the United States each week. The power of SpotifySpotify’s popularity is undeniable, accumulating millions of premium subscribers worldwide each quarter and hundreds of millions of unique visitors to Spotify.com every month. In the United States, Spotify is one of the most commonly used apps for listening to podcasts, and despite being in constant competition with Apple Music, remains a large part of U.S. music listeners’ lives. A survey revealed that Spotify is also the preferred music streaming service among 18 to 29-year-olds, which may seem unremarkable given the data on Spotify’s user base, but serves as further evidence of Spotify’s popularity among younger users. Whether Spotify’s growth will last forever, only time will tell, particularly as Apple Music continues to put up a good fight and smaller but increasingly popular services such as Deezer begin to make their mark. But with the company recording a profit in early 2019 for the first time since its inception, Spotify remains very much a market leader and firmly on the path to future success.

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 dataset contains survey data on the usage habits and preferences of Spotify users from different ages, genders, and regions. 1)Age - Age group of user?

2)Gender - Gender of user?

3)spotify_usage_period - How long have you been using Spotify?

4)spotify_listening_device - Which of the following devices do you primarily use to listen to Spotify?

5)spotify_subscription_plan - Which Spotify subscription plan do you currently have?

6)premium_sub_willingness - Are you willing to take a premium subscription or willing to continue with premium subscription in future?

7)preffered_premium_plan - If premium or willing to take premium, what amount do you pay for the subscription?

8)preferred_listening_content - What do you prefer to listen more?

9)fav_music_genre - What genre(s) of music do you enjoy the most?

10)music_time_slot - What is your favourite time slot to listen to music?

11)music_Influencial_mood - When it comes to listening to music, which of the following moods or situations most strongly influences your choice of music?

12)music_lis_frequency - When do you listen to music more often?

13)music_expl_method - How do you discover new music on Spotify?

14)music_recc_rating - How do you rate the spotify music recommendations?

15)pod_lis_frequency - How often do you listen to Podcast?

16)fav_pod_genre - What genre(s) of Podcast do you enjoy the most?

17)preffered_pod_format - What podcast format you generally prefer?

18)pod_host_preference - Are you more inclined to listen to podcasts from unknown personalities, or do you prefer podcasts hosted by well-known individuals?

19)preffered_pod_duration - Do you prefer shorter podcast episodes (under 30 minutes) or longer episodes (over 30 minutes)

20)pod_variety_satisfaction - Are you satisfied with the variety and availability of podcasts on Spotify?

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...

In the third quarter of 2025, Spotify had *** million monthly active users, which represents a new record high in the examined period. Meanwhile, this figure represents an increase from ****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 *** million active users in Q1 2025, marking a surge of ** million users in just one year. This growth encompasses both ad-supported and premium subscribers, with the latter reaching *** million in the same quarter. The platform's expansion is particularly strong in Europe and North America, which together account for ** percent of Spotify's premium subscribers. Europe remains Spotify's strongest market, representing ** 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 **** 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 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

📊 About the Dataset

This dataset captures the global music streaming trends on Spotify for the year 2024. It provides valuable insights into user preferences across various countries, top-performing artists and albums, streaming hours, and listener behavior patterns. It is designed to support data analysis, machine learning models, and business intelligence dashboards in the music and media industry.

With over 500 rows of clean, non-duplicated, and realistic entries from countries around the world, this dataset is ideal for uncovering:

• Global music popularity patterns
• Listener engagement across genres and demographics
• Artist performance across countries
• Revenue forecasting and content recommendations

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Key Music Streaming App StatisticsTop Music Streaming AppsMusic Streaming RevenueMusic Revenue by FormatMusic Streaming MarketshareMusic Streaming Subscribers by AppMusic Streaming Users by AppMusic...

This dataset consists of 51,000+ user reviews of the Spotify application from the Google Play Store (January - July 2022). The reviews have been scraped to analyze user sentiments and feedback towards the app. Each review has been labeled as either "Positive" or "Negative" based on its sentiment. The distribution of sentiments within the dataset is as follows:

**Positive Reviews: 56% of the total reviews.

-Negative Reviews: 44% of the total reviews**

This dataset can be used to perform various analyses such as sentiment analysis, trend analysis over time, and feature extraction to gain insights into user experience and satisfaction with the Spotify app. It serves as a valuable resource for researchers and developers who want to explore user perceptions and identify areas of improvement for the application.

In the third quarter of 2025, Spotify reported having ** percent of their total monthly active users (MAUs) located in Latin America, which translates to around ****** million MAUs in the region, up from around *** million a year earlier – an annual increase of over *** percent. Overall, the region accounts for over one-fifth of Spotify’s MAUs worldwide.

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 dataset contains records of the user's Spotify streaming history, with each row representing a specific instance of a played track. The data includes various attributes providing insights into the user's music listening habits.

Columns:

1. ts (Timestamp):

   • The timestamp when the track was played.

2. platform:

   • The platform or device used for streaming (e.g., Windows 10).

3. ms_played:

   • The duration in milliseconds of how long the track was played.

4. conn_country:

   • The country code indicating the user's location during streaming (e.g., LK for Sri Lanka).

5. master_metadata_track_name:

   • The name of the track played.

6. master_metadata_album_artist_name:

   • The artist of the album to which the track belongs.

7. master_metadata_album_album_name:

   • The name of the album containing the track.

8. spotify_track_uri:

   • The unique Spotify URI for the track.

9. reason_start:

   • The reason for starting the track (e.g., play button clicked).

10. reason_end:

    • The reason for ending the track (e.g., track done).

11. shuffle:

    • Indicates whether shuffle mode was enabled (True/False).

12. offline:

    • Indicates whether the track was played offline (True/False).

13. offline_timestamp:

    • Timestamp indicating when the track was played offline (if applicable).

14. incognito_mode:

    • Indicates whether incognito mode was enabled (True/False).

Purpose:

This dataset is suitable for performing detailed Exploratory Data Analysis (EDA) to uncover patterns, trends, and insights into the user's music-listening behaviour. Potential analyses could include the distribution of listening durations, favourite artists and tracks, exploration of geographic listening patterns, and examination of usage patterns across different platforms.

Visualization tools such as Matplotlib and Seaborn could be utilized for a more in-depth analysis to create visual representations of the findings. This dataset aligns well with your interest in data science, offering opportunities to apply analytical techniques to real-world streaming data.

About this dataset

Music is ubiquitous in today's world-almost everyone enjoys listening to music. With the rise of streaming platforms, the amount of music available has substantially increased. While users may seemingly benefit from this plethora of available music, at the same time, it has increasingly made it harder for users to explore new music and find songs they like. Personalized access to music libraries and music recommender systems aim to help users discover and retrieve music they like and enjoy.

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.

How to use this dataset

• Create music recommendation engine
• Explore most played tracks, artists, playlists and their graph associations
• Find the coolest tracks/playlists for everyone to hear!

Highlighted Notebooks

• Your kernel can be featured here!
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

Citation

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.

License

CC BY 4.0

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Icon by Fathema Khanom on FlatIcon.

The Million Playlist Dataset (Documentation updated Aug 5, 2020)

The Million Playlist Dataset contains 1,000,000 playlists created by users on the Spotify platform. It can be used by researchers interested in exploring how to improve the music listening experience.

What's in the Million Playlist Dataset (MPD) The MPD contains a million user-generated playlists. These playlists were created during the period of January 2010 through October 2017. Each playlist in the MPD contains a playlist title, the track list (including track metadata) editing information (last edit time, number of playlist edits) and other miscellaneous information about the playlist. See the Detailed Description section for more details.

License Usage of the Million Playlist Dataset is subject to these license terms

Citing the Million Playlist Dataset To use this dataset, please cite the following paper:

Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 2018.

Getting the dataset The dataset is available at https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge

Verifying your dataset You can validate the dataset by checking the md5 hashes of the data. From the top level directory of the MPD:

% md5sum -c md5sums This should print out OK for each of the 1,000 slice files in the dataset.

You can also compute a number of statistics for the dataset as follows:

% python src/stats.py data The output of this program should match what is in 'stats.txt'. Depending on how fast your computer is, stats.py can take 30 minutes or more to run.

Detailed description The Million Playlist Dataset consists of 1,000 slice files. These files have the naming convention of:

mpd.slice.STARTING_PLAYLIST_ID_-_ENDING_PLAYLIST_ID.json

For example, the first 1,000 playlists in the MPD are in a file called mpd.slice.0-999.json and the last 1,000 playlists are in a file called mpd.slice.999000-999999.json.

Each slice file is a JSON dictionary with two fields: info and playlists.

info Field The info field is a dictionary that contains general information about the particular slice:

slice - the range of slices that in in this particular file - such as 0-999 version - - the current version of the MPD (which should be v1) description - a description of the MPD license - licensing info for the MPD generated_on - a timestamp indicating when the slice was generated. playlists field This is an array that typically contains 1,000 playlists. Each playlist is a dictionary that contains the following fields:

pid - integer - playlist id - the MPD ID of this playlist. This is an integer between 0 and 999,999. name - string - the name of the playlist description - optional string - if present, the description given to the playlist. Note that user-provided playlist descrptions are a relatively new feature of Spotify, so most playlists do not have descriptions. modified_at - seconds - timestamp (in seconds since the epoch) when this playlist was last updated. Times are rounded to midnight GMT of the date when the playlist was last updated. num_artists - the total number of unique artists for the tracks in the playlist. num_albums - the number of unique albums for the tracks in the playlist num_tracks - the number of tracks in the playlist num_followers - the number of followers this playlist had at the time the MPD was created. (Note that the follower count does not including the playlist creator) num_edits - the number of separate editing sessions. Tracks added in a two hour window are considered to be added in a single editing session. duration_ms - the total duration of all the tracks in the playlist (in milliseconds) collaborative - boolean - if true, the playlist is a collaborative playlist. Multiple users may contribute tracks to a collaborative playlist. tracks - an array of information about each track in the playlist. Each element in the array is a dictionary with the following fields: track_name - the name of the track track_uri - the Spotify URI of the track album_name - the name of the track's album album_uri - the Spotify URI of the album artist_name - the name of the track's primary artist artist_uri - the Spotify URI of track's primary artist duration_ms - the duration of the track in milliseconds pos - the position of the track in the playlist (zero-based) Here's an example of a typical playlist entry:

{
  "name": "musical",
  "collaborative": "false",
  "pid": 5,
  "modified_at": 1493424000,
  "num_albums": 7,
  "num_tracks": 12,
  "num_followers": 1,
  "num_edits": 2,
  "duration_ms": 2657366,
  "num_artists": 6,
  "tracks": [
    {
      "pos": 0,
      "artist_name": "Degiheugi",

...

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.

Dataset Title: Top 5,000 Spotify Artists Metadata (2025)

Description:

This dataset contains metadata for up to 5,000 unique artists from Spotify, collected via the Spotify Web API in June 2025. The data includes key attributes such as artist name, Spotify ID, popularity score, genres, total followers, and profile image URLs, providing a comprehensive snapshot of prominent artists across various genres.

The dataset was created by querying the Spotify API for artists in popular genres (e.g., pop, rock, hip-hop, jazz, and more) and filtering for unique entries based on Spotify IDs. Artists are sorted by popularity to approximate the "top" artists on the platform, based on Spotify’s popularity metric (0–100), which reflects streaming activity and listener engagement.

Key Features:

Columns:

name: Artist’s name (string). id: Unique Spotify artist ID (string). popularity: Spotify popularity score (integer, 0–100). genres: Comma-separated list of genres associated with the artist (string). followers: Total number of followers on Spotify (integer). image_url: URL to the artist’s profile image, if available (string, nullable). Rows: Up to 5,000 unique artists. File Format: CSV (top_spotify_artists.csv). Data Source: Spotify Web API, accessed via the Spotipy Python library. Collection Date: June 2025.

Potential Uses:

Music Analytics: Analyze artist popularity trends, genre distributions, or follower demographics. Recommendation Systems: Build models to recommend artists based on genres or popularity. Visualization: Create visualizations of artist networks, genre overlaps, or popularity rankings. Market Research: Study the music industry’s top artists and their audience engagement. Notes:

The dataset is an approximation of "top" artists, as Spotify does not provide a direct "top artists" endpoint. Data was gathered by searching across multiple genres and sorting by popularity. Some artists may lack image URLs or genres due to incomplete Spotify profiles. Users should comply with Spotify’s API Terms of Use when utilizing this dataset. License: This dataset is shared under the CC BY-SA 4.0 license, with attribution to the Spotify Web API as the data source.

Acknowledgments:

Data sourced from the Spotify Web API. Collected using the Spotipy Python library.

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.

Introduction

Music streaming statistics: To put it simply, music streaming is one of the most popular industries today, with an amazing 713.4 million paid subscribers globally and a market generating over $28.6 billion in recorded music revenue. Streaming has fundamentally changed how we discover, consume, and interact with different songs and music. It now accounts for over 67% of the entire music industry's earnings, a figure that has multiplied more than 15 times over the past decade.

Leading the charge are giants like Spotify, which boasts over 626 million total users, and YouTube Music, which leverages a user base of over 2 billion (with YouTube). The overall market for music streaming apps was valued at $49.5 billion in 2025 and is on an aggressive upward trajectory, with forecasts predicting it will smash the $100 billion mark by 2030. Countries like Nigeria are at the forefront of this digital wave, with an incredible 91% of its population engaging with digital music. From Gen Z's listening habits to the per-stream payout rates for artists, the numbers tell a fascinating story of this in progress.

So, let's dive straight into the most comprehensive collection of music streaming statistics 2025. If you are a music artist, a developer, or a business person, this will help you out. Let’s get into it.