Spotify is a digital music platform with millions of users worldwide. Spotify mines data on user listening habits broken down by date and time, and artists and their songs (tracks) measured in milliseconds. Users can retrieve their data by requesting a data file online. The file is emailed to users within 30 days of their request.
I analyzed the data for this project for four family members, including myself. The data I requested from Spotify ranges from October 2023 to September 2024. The data collected provides the aggregated listening habits of all family members. Initially, this project’s scope included analyzing the different music preferences between family members. However, due to the aggregated data, the project shows the overall listening habits of the family.
To make the listening time data more accessible to interpretation, I converted milliseconds into minutes and hours. These alternative time formats provide a clearer perspective on listening time, making it more straightforward to understand and analyze the amount of time dedicated to music.
Most Listened to Artist
Spotify tracks music playtime in milliseconds. Aggregating track playtime data by artist gave me a list of hundreds of artists. Using Tableau, I extrapolated the more popular artists in the family’s Spotify history to create a bubble graph. A bubble chart provides a visual representation of data, where words or phrases that appear more frequently are displayed in larger circles, often called bubbles. For this project, it provided a more digestible format to view aggregated artist data.
The bubble chart below highlights the top 8 most-played artists. Mariah Carey is the most-played artist, with 26.46 hours of listening time, double that of each of the other top 8 artists. The remaining artists each have a total listening time ranging from 10.31 to 13.19 hours. By far, the family’s most-played song by Mariah Carey is “All I Want for Christmas Is You.” Due to the over-representation of the song, which included different versions, it was removed from further analysis/visual representations.
Most Popular Songs
A word cloud is a visual representation of text data, where words or phrases that appear more frequently are displayed in larger, bolder fonts. It provides a quick, visually intuitive way to identify dominant themes or popular items within a dataset. From the word cloud analysis, the most-played song was “Time of Our Lives” by Pitbull featuring Ne-Yo. This wasn’t a surprise, as it’s my go-to track for motivation, whether when I’m heading to the gym, going out with friends, or just needing a boost. Another Ne-Yo song, “Let Me Love You (Until You Learn to Love Yourself),” also appeared prominently. Ne-Yo, unbeknownst to me, is one of my favorite artists.
“This is for everybody going through times
Believe me, been there, done that
But every day above ground is a great day, remember that”
Time of Our Lives, Pitbull featuring Ne-Yo
Interestingly, the word cloud highlighted some newer songs from more recent artists like “Nonsense” by Sabrina Carpenter, “I Like You (A Happier Song)” by Post Malone featuring Doja Cat, and “Good Luck, Babe!” by Chappell Roan. These tracks align with the tastes of my younger sisters, who are in their teens. Meanwhile, my other sister’s love for late ’90s and early 2000s R&B is reflected by tracks like “Diggin’ on You” by TLC, “Touch My Body” by Mariah Carey, and “When I See U” by Fantasia. The word cloud below visualizes the aforementioned analysis.
Most Played Artists and Tracks
After examining the bubble chart and word cloud visualizations, I wondered if there would be any overlap between the most-played tracks and the top 8 artists. To explore potential correlations between the two visualizations, I created a bar graph in Tableau, using the “details” function to break down the most played songs by artists. Additionally, I built a table to view the number of songs played per artist.
Unsurprisingly, the charts confirmed the top 8 artists, aligning with the insights from the bubble chart. However, it was interesting to see the tracks played by each artist across the family’s listening habits. Notably, the popularity of some of Mariah Carey’s songs is tied to cultural trends. For example, “Obsessed” has seen a resurgence in popularity, partly fueled by a TikTok dance trend and new releases that sample her music (such as “Big Energy” by Latto, which samples “Fantasy”).
Visualizing the top 8 artists in a bar graph with desegrated track data provided insights into how certain songs played were aligned with cultural trends and highlighted less popular tracks. For instance, songs by Nicky Jam showed that our family was listening to tracks that weren’t necessarily on the Billboard 100, reflecting unique listening preferences beyond mainstream popularity. On the other hand, the tracks by other artists played by the family generally align with popular trends.
Listening Habits Over Time
The final data I analyzed from Spotify includes detailed information on the dates and times tracks were played. The data is presented in hours to facilitate interpretation. The data displays a peak in listening activity during the summer months. This seems to be aligned with my family’s yearly schedule, as four of my siblings live in Connecticut, where the school year ends in early June and resumes in late August. The additional free time during the summer, which correlates with fewer academic commitments, reflects a seasonal shift in my family’s music consumption.
Interestingly, the data also reveals that listening activity during the holidays is relatively high, with November totaling 49.40 hours and December close behind at 44.60 hours. This pattern corresponds with the family spending more time together, celebrating, and engaging in holiday traditions. The holiday season often brings about shared listening experiences through festive gatherings, road trips, or just relaxing at home. This trend highlights how music plays a significant role in seasonal and familial moments.
Trends, Patterns, and Limitations
By analyzing my family’s Spotify listening data, I observed several interesting trends and patterns. The data revealed our top 8 most-played artists and highlighted seasonal shifts in listening habits. By converting the listening data from milliseconds to minutes and hours, it became easier to interpret the time spent listening to tracks, especially when comparing monthly listening habits, such as increased activity during the summer and holiday months.
However, this project faced several limitations. One major constraint was the accuracy and completeness of the data collected from Spotify, as certain variables—like multiple versions of a song—could inflate an artist’s play count. Another limitation is the lack of user-specific data.
If I were to approach this project again, I would consider incorporating additional data points, such as the context of each play session or the inclusion of playlists, to capture more nuanced listening behaviors. The lack of context, such as the mood or activity associated with each listening session, could provide a deeper understanding of listening patterns. I would also consider expanding the scope of the data collected to compare individual vs. shared family listening habits to distinguish unique tastes within a household. By refining these aspects, I could achieve a more holistic view of my family’s music consumption.
