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:
  • Optimize Resource Allocation: Prioritize marketing spend and promotional activities based on predicted performance.
  • Identify Strong Performing Tracks: Discover breakout tracks early on and capitalize on their momentum.
  • Refine A&R Strategies: Inform A&R decisions by identifying promising artists and tracks.
  • Set Realistic Goals: Establish data-driven streaming targets and measure success accurately.
  • Adapt to Market Trends: Adjust strategies based on real-time data and predicted future performance.
Models Available in Songbird: Songbird offers distinct models tailored for various stages of a track’s lifecycle:
  • Pre-Release Analysis:
    • K2: Pre-Release Streaming Prediction: Predicts first-week streaming performance for unreleased tracks, enabling informed pre-release decisions.It can also, identifies potential singles / focus tracks and quantifies the likelihood of a track becoming a focus track, aiding in campaign prioritization and resource allocation.
    • K8: Trajectory Analysis: Pinpoints peak performance, growth trajectory and decay of a song based on how long a song has been on Spotify
  • Post-Release Analysis:
    • K5: Short-Term Post-Release Performance: Predicts short-term streaming performance (2 weeks after release) to optimize early marketing campaigns.
    • K7: Long-Term Post-Release Performance: Forecasts long-term streaming performance (up to 1 year after release) for strategic planning and catalog valuation.
  • Demand Analysis:
    • Demand Model: Analyzes market demand for specific music styles and identifies opportunities for new releases, enabling data-driven A&R and marketing decisions.
How Songbird’s Models Work: Songbird’s models combine several key data sources:
  • Audio Embeddings: Advanced audio analysis captures the sonic characteristics of each track.
  • Artist Performance History: Leverages historical streaming data and artist metadata to contextualize predictions.
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:
  • Explore the individual model documentation pages for detailed information on each model’s inputs, outputs, and usage within Songbird.
  • Review the “Use Cases and Workflows” page for practical examples of how to apply Songbird’s predictive models in your daily operations.
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.