How & Why To Use Audience Segmentation to Maximize (Listener) Demand Across Digital Music Portfolio

TL;DR

This paper examines how audience segmentation can be used to optimize listener demand and revenue in digital music streaming, considering dynamic listening patterns and demand models.

Contribution

It introduces new strategies for demand prediction and optimization based on audience segmentation in the context of digital music distribution.

Findings

Demand is driven by affinity, boredom, and attention.

Segmentation improves demand forecasting accuracy.

Optimized strategies can enhance revenue across digital music portfolios.

Abstract

Digital delivery of songs has radically changed the way people can enjoy music, the sort of music available for listening, and the manner by which rights holders are compensated for their contributions to songs. Listeners enjoy an unlimited potpourri of sounds, uniquely free of incremental acquisition or switching costs which have been replaced by subscription or rentier fees. This regime shift has revealed listening patterns governed by affinity, boredom, attention budget, etc.: instantaneous, dynamic, organic or programmatic song selection. This regime shift in demand availability -- with the commensurate translation of revenue implications -- deprecates current orthodoxy for content curation. The impulse to point-of-sale model is insufficient in a regime where demand revenue is proportional to demand affinity and each are strongly dependent time series processes. We explore strategies & implications -- which are generalizable to any media rights holding firm -- from a prediction & optimization point of view for two straightforward demand models.