Songbird provides a suite of predictive models designed to empower artists, record labels and distributors with predictions at every stage of a track’s lifecycle, from pre-release to long-term performance analysis.
These models leverage advanced machine learning techniques, combined with music audio analysis and artist performance history, to offer probabilistic predictions of streaming performance.
Songbird’s predictive models help you:
Models Available in Songbird:
Songbird offers distinct models tailored for various stages of a track’s lifecycle:
Pre-Release Analysis:
Post-Release Analysis:
Demand Analysis:
How Songbird’s Models Work:
Songbird’s models combine several key data sources:
Outputs and Interpretation:
Each model provides a probability distribution across different streaming tiers or predicted streaming counts, along with additional metrics like velocity and confidence intervals. These outputs are presented in an intuitive format within the Songbird platform, allowing for easy interpretation and actionable insights.
Next Steps:
By leveraging Songbird’s suite of predictive models, record labels can gain a competitive edge by making data-driven decisions throughout a track’s lifecycle, maximizing its potential for success.
Songbird provides a suite of predictive models designed to empower artists, record labels and distributors with predictions at every stage of a track’s lifecycle, from pre-release to long-term performance analysis.
These models leverage advanced machine learning techniques, combined with music audio analysis and artist performance history, to offer probabilistic predictions of streaming performance.
Songbird’s predictive models help you:
Models Available in Songbird:
Songbird offers distinct models tailored for various stages of a track’s lifecycle:
Pre-Release Analysis:
Post-Release Analysis:
Demand Analysis:
How Songbird’s Models Work:
Songbird’s models combine several key data sources:
Outputs and Interpretation:
Each model provides a probability distribution across different streaming tiers or predicted streaming counts, along with additional metrics like velocity and confidence intervals. These outputs are presented in an intuitive format within the Songbird platform, allowing for easy interpretation and actionable insights.
Next Steps:
By leveraging Songbird’s suite of predictive models, record labels can gain a competitive edge by making data-driven decisions throughout a track’s lifecycle, maximizing its potential for success.