GCP – StreamSight: Driving transparency in music royalties with AI-powered forecasting

In an industry generating vast volumes of streaming data every day, ensuring precision, speed, and transparency in royalty tracking is a constant and evolving priority. For music creators, labels, publishers, and rights holders, even small gaps in data clarity can influence how and when income is distributed — making innovation in data processing and anomaly detection essential.

To stay ahead of these challenges, BMG partnered with Google Cloud to develop StreamSight, an AI-driven application that enhances digital royalty forecasting and detection of reporting anomalies. The tool uses machine learning models to analyze historical data and flag patterns that help predict future revenue — and catch irregularities that might otherwise go unnoticed.

The collaboration combines Google Cloud’s scalable technology, such as BigQuery, Vertex AI, and Looker, with BMG’s deep industry expertise. Together, they’ve built an application that demonstrates how cloud-based AI can help modernize royalty processing and further BMG’s and Google’s commitment to fairer and faster payout of artist share of label and publisher royalties. 

“At BMG, we’re accelerating our use of AI and other technologies to continually push the boundaries of how we best serve our artists, songwriters, and partners. StreamSight reflects this commitment — setting a new standard for data clarity and confidence in digital reporting and monetization. Our partnership with Google Cloud has played a key role in accelerating our AI and data strategy.” – Sebastian Hentzschel, Chief Operating Officer, BMG

From Data to Insights: How StreamSight Works

At its core, StreamSight utilizes several machine learning models within Google BigQuery ML for its analytical power:

For Revenue Forecasting:

• ARIMA_PLUS: This model is a primary tool for forecasting revenue patterns. It excels at capturing underlying sales trends over time and is well-suited for identifying and interpreting long-term sales trajectories rather than reacting to short-term volatility.

• BOOSTED_TREE: This model is valuable for the exploratory analysis of past sales behavior. It can effectively capture past patterns, short-term fluctuations and seasonality, helping to understand historical dynamics and how sales responded to recent changes.

For Anomaly Detection & Exploratory Analysis:

• K-means and ANOMALY_DETECT function: These are highly effective for identifying various anomaly types in datasets, such as sudden spikes, country-based deviations, missing sales periods, or sales reported without corresponding rights.

Together, these models provide a comprehensive approach: ARIMA_PLUS offers robust future trend predictions, while other models contribute to a deeper understanding of past performance and the critical detection of anomalies. This combination supports proactive financial planning and helps safeguard royalty revenues.

Data Flow in Big Query:

Finding the Gaps: Smarter Anomaly Detection

StreamSight doesn’t just forecast earnings — it also flags when things don’t look right. Whether it’s a missing sales period; unexpected spikes or dips in specific markets; or mismatches between reported revenue and rights ownership, the system can highlight problems that would normally require hours of manual review. And now it’s done at the click of a button.

For example:

• Missing sales periods: Gaps in data that could mean missing money.

• Sales mismatched with rights: Revenue reported from a region where rights aren’t properly registered.

• Global irregularities: Sudden increases in streams or sales that suggest a reporting error or unusual promotional impact.

With StreamSight, these issues are detected at scale, allowing teams to take faster and more consistent action.

The StreamSight Dashboard:

Built on Google Cloud for Scale and Simplicity

The technology behind StreamSight is just as innovative as its mission. Developed on Google Cloud, it uses:

• BigQuery ML to run machine learning models directly on large datasets using SQL.

• Vertex AI and Python for advanced analysis and model training.

• Looker Studio to create dashboards that make results easy to interpret and share across teams.

This combination of tools made it possible to move quickly from concept to implementation, while keeping the system scalable and cost-effective.

A Foundation for the Future

While StreamSight is currently a proof of concept, its early success points to vast potential. Future enhancements could include:

• Adding data from concert tours and marketing campaigns to refine predictions.

• Include more Digital Service Providers (DSPs) that provide access to digital music, such as Amazon, Apple Music or Spotify to allow for better cross-platform comparisons.

• Factoring in social media trends or fan engagement as additional inputs.

• Segmenting analysis by genre, region, music creator type, or release format.

By using advanced technology for royalty processing, we’re not just solving problems — we’re building a more transparent ecosystem for the future, one that supports our shared commitment to the fairer and faster payout of the artist’s share of label and publisher royalties.

The collaboration between BMG and Google Cloud demonstrates the music industry’s potential to use advanced technology to create a future where data drives smarter decisions and where everyone involved can benefit from a clearer picture of where music earns its value.