Banger for the Buck : Predicting Growth of Music Tracks using Machine Learning

Abstract: The advent of music streaming has made it increasingly important for actors in the music industry to understand if tracks are going to succeed or not. This study investigates if it is possible to accurately classify the growth of the listener base of a music track based on multivariate time series with listener behavior data. 18 popular time series classification algorithms were used to build predictive models which were evaluated in a 10-fold cross-validation. We also examined the algorithms’ potential to deliver business value for a record label. Lastly, the possibilities and challenges of applying a data-driven business model in the music industry were investigated by performing a comparative analysis of a modern and traditional record label. Six algorithms were found to significantly outperform the baseline. Two algorithms based on convolutional kernels, RR and AMini, were found to present the biggest business value because of their accuracy and low time complexity. While it may be necessary for record labels to adopt data-driven business models to flourish in the modern market, there are difficulties regarding the competitiveness of digital solutions and complications in moving the focus from networking to developing technology.