Exploring Music Streaming and Song Preferences in the Philippine Music Scene: A Spotify Data Analysis Using Clustering

The emergence of digitalization has brought about notable shifts in the listening practices among Filipino music patrons, leading to profound effects on both the music industry and the audience, manifesting in various ways. Music listeners have turned to online streaming platforms for music consumption, and are manifestly exhibiting different preferences in songs, however not much is known about the underlying stimuli that spike their interest in certain types of music. This exploratory study investigates the music streaming characteristics in the Philippines at the time of the pandemic and identifies the significant features or attributes of streamed songs in the Philippines based on the listeners’ actual streaming behavior. For a deep dive into the music data, correlation analysis, cluster analysis, and regression analysis were used in this study. The analysis covers a unique dataset of the Philippines’ streamed songs on Spotify, from the year 2017 to the 1st quarter of 2022 totaling 392,006 instances with multivariate attribute information. The findings reveal that the artist's popularity correlates with the number of followers and that the four significant attributes for the most streamed music include speechiness, liveness, valence, and tempo based on actual streaming behavior during the pandemic, which could be attributed to their moods, feelings, or emotional state. Furthermore, the findings suggest that the identified music attributes/features could influence the listening behavior among Filipinos. Finally, the music attributes may differently affect the popularity of the song at different times and situations.