Predictive systems are gaining ground in music curation, giving industry professionals additional tools to identify trends and assess artists and tracks that could reach different audiences.
Systems based on neural networks are already being used to analyse large volumes of information and identify patterns associated with the performance of songs, artists and content.
These tools are intended to support artists and repertoire (A&R) professionals, record labels, producers and platforms in several tasks:
- Evaluating demos.
- Tracking trends.
- Identifying artists and tracks with potential to reach different audiences.
Alongside individual judgement and historical market performance, music curation now also draws on data-based projections.
By analysing music consumption information, these tools can indicate trends before they become established on platforms or social media.
Audience response remains fundamental to the process. Fandom, listeners’ identification with artists and songs, and a work’s ability to create a connection cannot be reduced to statistical patterns alone.
