Machine Learning and Personalisation: Tailoring Royalty Experiences for Artists in 2024

The music industry, fueled by streaming, faces challenges in artist monetization. Machine learning provides hyper-personalized royalty reporting, customizable payment calendars, intelligent advance funding, and optimized royalty splits.

PCQ Bureau
New Update
Machine Learning and Personalisation

The music industry has undergone seismic shifts in recent years with the rise of streaming and the democratisation of distribution enabled by the internet. However, even as artists enjoy greater creative freedom and reach than ever before, monetising their art and earning a living remains a significant challenge. This is where machine learning and personalisation come in - by tailoring royalty models and payment flows to suit individual artists' needs and preferences. Music revenue from streaming amounted to ₹5,692 crore in India, so ensuring creators receive their fair share has never been more important.


Hyper-Personalised Royalty Reporting

Darshil Shah, Founder and Director, TreadBinary

Legacy royalty accounting systems provided creators with only vague summaries of their accumulated streaming royalties. However, modern machine-learning algorithms can process months of granular playback data to generate custom royalty reports for each artist. Statements now highlight key details like - top streaming tracks worldwide, top cities and countries driving streams, monthly payment range averages, catalogue highlights by time period, genre and playlist breakdowns, and demographic profiles of top fan bases.


For example, a pop star may receive royalty statements emphasising his/her latest hits and global listening footprint. On the other hand, an emerging indie rapper's statements could spotlight regional listening surges and catalogue highlights by city.

This hyper-personalised reporting allows all tiers of artists to better understand their unique streaming landscapes. The visibility empowers data-backed career decisions - from tour routing to targeted merchandise designs.

Customisable Payment Calendars


Just as no two artists' streaming royalty payments follow identical trajectories, modern systems enable fully customisable payment delivery schedules. Artists can set their own recurring payment frequencies based on individual cash flow preferences - whether weekly, monthly, quarterly or custom intervals.

Sophisticated algorithms assess variables like the artist's typical streaming volumes, seasonal spikes and dips, and streaming history to automatically recommend optimal payment calendars. The AI then calibrates scheduled payment amounts to align with the artist's profile.

Across the industry, streaming royalties went unclaimed due to outdated distribution models. However, automated and personalised payment calibration ensures artists of all sizes receive owed royalties on timetables matching their careers.


Intelligent Advance Funding

Due to processing and reporting latencies, streaming royalty payments can lag weeks or months behind actual track streams. For both emerging and established artists, these built-in cash flow gaps between stream counts and payments can hamper career budgets. Machine learning now enables accurate, real-time predictive modelling for any artist to instantly access royalty advance financing.

Intuitive dashboards allow artists to simply click for an advance loan against their accrued streaming earnings. Sophisticated algorithms evaluate the latest streaming momentum, volatility, seasonal trends, and royalty rate configurations to instantly underwrite personalised advance amounts and repayment terms.


Having instant access to pending royalty capital during crucial streaming spikes is essential to fully capture fleeting momentum. As algorithmic music discovery pathways accelerate, intelligent royalty advances will only grow more integral for sustaining creator careers.

Optimised Royalty Splits

For any collaborative track featuring multiple artists, machine learning can now optimise royalty splits and performance attribution at unprecedented accuracy. Natural language processing algorithms ingest the latest streaming metadata, lyrical credits, contractual terms, and instrumentation contributions to recommend mathematically fair royalty allocation percentages.


Artists receive personalised split recommendations alongside data-backed insights to adjust numbers as needed. After decades of opaque calculations, creators finally have actionable visibility into receiving proper attribution.

In particular, marginalised artists and songwriters have widely praised algorithmic auditing as crucial for dismantling generations of race-based royalty inequities. As cultural appreciation for music creators expands, it is essential that streaming income proportionally rewards the progenitors of beloved art. Disbursement algorithms are at last distributing royalty shares along scientifically equitable lines - helping heal historical income divides.

To conclude


As 2024 unfolds, streaming has captured the vast majority of total music revenues and continues on a growth trajectory. Global royalties have reached new highs annually. This significant expansion highlights the pressing need to guide creators into long-standing streaming success through cutting-edge advancements.

Machine learning provides the breakthroughs required to efficiently guide these revenues into the hands of artists worldwide. Algorithms parse the very streaming metadata fueling industry profits to inform highly tailored, data-backed disbursement experiences. Hyper-personalization reduces opaque reporting, while intelligent financing and advanced conflict resolution establish new creative career sustainability.

By collaborating with algorithms, the music community now circulates billions in royalties to the very artists sustaining its profits. As advanced systems expand in scope, machine learning will propel emerging and established talent alike into an age of equitable creative livelihoods. The future has never looked brighter for music creators to earn their worth.

Author: Darshil Shah, Founder and Director, TreadBinary