Signing new musical talent is a complex decision problem for music labels. This paper proposes and tests a framework for the creation of a decision support system based on artificial intelligence, machine learning, and decision science for the process of signing new musical talent by music labels.
In the music industry, the process of signing new musical talent is one of the most complex decision-making problems. The decision, which is generally made by an artist and repertoire (A&R) team, involves consideration of various quantitative and qualitative criteria, and usually results in a low success rate. We conducted a series of mental model interviews with the aim of developing a decision support framework for A&R teams. This framework was validated by creating a decision support system that utilises multi-criteria decision analysis to support decision-making. Our framework and subsequent implementation of the decision support system involving decision rule and weighted sum methods show an improvement in the ability to analyse and decide on greater amounts of talent. This paper serves as a building block for developing systems to aid in this complex decision-making problem.
Link to the paper: https://doi.org/10.1016/j.ejor.2023.06.014
Authors: Aritad (Alan) Choicharoon, Richard Hodgett, Barbara Summers, Sajid Siraj