Our role: Data Analyst Team at a consulting company
Target Audience: Music Enthusiasts(especially Spotify members)
Our sources of listening to songs have evolved. From the vintage gramophones through radio, our listening medium has now transformed to be an online music-streaming platform. Spotify, Apple Music, Google Play, etc are some of the most popular music-streaming platforms. Spotify occupies one of the top ranks with a huge music database, over 100 million subscribers, and more than one-third of the market share.
Obviously, it is time-consuming and complex to find proper music that is customized to fit users' interests and tastes. Hence, our dashboard would like to provide a visual platform interface for music enthusiasts, especially Spotify members to explore music trends of different songs and artists, filter for high-quality music, and maybe discover new music and new genres, using the Spotify music database.
The spirit of our app is that we would like to create an effective and adaptive exploratory visualization tool for music lovers on Spotify which can save their time and efforts to extract various data they need.
We will be visualizing a dataset of more than 30000 Spotify music tracks. The release year of the song ranges from 1960 to 2020. Each song has 23 associated variables that describe its identity (track_id, track_name, track_artist, track_album_id, track_album_name, and track_album_release_date), the popularity (track_popularity), the genre of the song (playlist_name, playlist_genre, playlist_subgenre), and audio features of the song (such as danceability, energy, key, loudness, speechiness, etc.)
- What are the top 5 trending genres?
- What is the correlation between music features(loudness, danceability) and popularity?
- what is the song recommendation system given genres and popularity range?
Lucy enjoys listening to music and she is particularly intruiged by how much music-streaming has developed over the years. She has her own favorite genres and is also open to explore more new genres. However, she is unsure about how her current likings of genres correlate with her likings of new genres which she hasn't explored before. As the music streaming platform advances, she observes that there are usually customized recommendations, therefore she is curious as to how the music streaming platforms may or may not accurately recommend her likings. Moreover, as a young women with taste in genres that favors danceability and energy, she is also curious about whether these music features are correlated with the popularity of the songs she liked.
Puzzling about these questions, she is interested in an app that offers insights that can solve her puzzling thoughts. When she uses "music explorer", not only can she identify the list of genres trending now, she can also see the correlation and disassociation visually summarized in graphs and tables. This can help her better understand whether or not a music-streaming app is accurate in their customized recommendations. Moreover, she can also discover popular songs of genre interests, and confirm her question about certain music features in her likeings of genres.