Abstract:
Although the music industry continues to capitalize on the power of big data and
analytics, the job of predicting a song's future value is left to Artists and Repertoire
(A&R) representatives who must trust their experiences and use their gut instinct. There
remains an opportunity for analytics to unearth the science behind what gives popular
music value. This paper analyzes four quantitative structural elements of a song to
determine how they impact a song's value. Using a systematic method of listening and
data mining, each element was measured and tested for a relationship with the song's
sales, radio spins, and streams. These are songs that made appearances on various
Billboard charts between 2015 and 2018. The difficulty of data cleansing, data
accessibility, and data collecting artistic products is emphasized. Certain elements,
including the repeated lyrics and length of the intro, did show some relationship with
song value, and the extent to which this is true is also emphasized. While the model does
not explain all elements that impact value, this paper could serve to start the discussion
on using big data and analytics to guide music labels on predicting a song's value.
