The Valuation of Songwriting Techniques: An Analysis of How Song Elements Affect Song Value

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.