What it does: Predicts short-term streaming performance (two weeks post-release) on Spotify for released tracks.
Why use it: K5 helps optimize early-stage marketing campaigns and identify potential breakout tracks shortly after release. It provides actionable insights to:
When to use it: Use K5 immediately after a track’s release, with at least three days of streaming data available.
How to use it in Songbird:
Upload Audio: Upload the track’s audio file (MP3, WAV, or M4A). The filename must be the Spotify Track ID. (Include a clearly annotated screenshot of the upload area in Songbird).
Input Metadata: Provide the track’s release date (YYYY-MM-DD), artist ID, and the last three days of streaming data from Spotify. Also, specify if the track is a focus track (Yes/No), any marketing alerts or initiatives, and your streaming goal. (Include a screenshot of the metadata input fields with labels).
Run K5 Analysis: Click “Run Analysis” to generate predictions. (Include a screenshot highlighting the button).
Interpret Results: Review the predictions, visualizations, and causal recommendations (if applicable) in Songbird. (Include a screenshot of the results page).
Key Inputs:
Key Outputs and Interpretation:
Predicted Daily Streams: K5 predicts daily streams for the next 7 days after the initial 3 input days. (Include a line chart visualization, like the one generated by Songbird, showing predicted vs. actual streams over time. Annotate the chart clearly.) Example: In the provided example output, the prediction
field shows the predicted streams for each day (target_day), while actual_streams
show the real streams from Spotify. Visualize this as a line chart inside the product.
Upper and Lower Confidence Bounds: The model provides confidence intervals (“upper” and “lower” bounds) for each prediction, indicating the range within which the actual stream count is likely to fall.
Velocity: Rate of change in streaming performance, indicating momentum. Higher velocity suggests faster growth. (If shown in the Songbird output, include an explanation and visual representation, perhaps a separate line on the chart showing velocity).
Recommendations (If applicable): For selected labels, K5 includes specific marketing interventions and their estimated impact (“lift”) on streaming performance. These are shown in Recommendations
section.
For example, running a TikTok influencer campaign has an estimated lift of 1,425,020 streams, while running press/PR only has an estimated lift of 300,000 streams, meaning it is likely to cause a lower lift in streams. (Include screenshot)
Limitations:
Example Scenario:
A label releases a new track and uses K5 to monitor its performance. After a few days, they observe that the track is underperforming compared to predictions. K5’s causal inference module recommends increasing investment in a specific marketing channel (e.g., TikTok influencer campaign).
The label implements the recommendation and observes a significant positive impact on streaming numbers, demonstrating the value of K5’s insights for real-time campaign optimization. After the initial two weeks, they switch to the K7 model for long-term performance forecasting and strategic planning.
What it does: Predicts short-term streaming performance (two weeks post-release) on Spotify for released tracks.
Why use it: K5 helps optimize early-stage marketing campaigns and identify potential breakout tracks shortly after release. It provides actionable insights to:
When to use it: Use K5 immediately after a track’s release, with at least three days of streaming data available.
How to use it in Songbird:
Upload Audio: Upload the track’s audio file (MP3, WAV, or M4A). The filename must be the Spotify Track ID. (Include a clearly annotated screenshot of the upload area in Songbird).
Input Metadata: Provide the track’s release date (YYYY-MM-DD), artist ID, and the last three days of streaming data from Spotify. Also, specify if the track is a focus track (Yes/No), any marketing alerts or initiatives, and your streaming goal. (Include a screenshot of the metadata input fields with labels).
Run K5 Analysis: Click “Run Analysis” to generate predictions. (Include a screenshot highlighting the button).
Interpret Results: Review the predictions, visualizations, and causal recommendations (if applicable) in Songbird. (Include a screenshot of the results page).
Key Inputs:
Key Outputs and Interpretation:
Predicted Daily Streams: K5 predicts daily streams for the next 7 days after the initial 3 input days. (Include a line chart visualization, like the one generated by Songbird, showing predicted vs. actual streams over time. Annotate the chart clearly.) Example: In the provided example output, the prediction
field shows the predicted streams for each day (target_day), while actual_streams
show the real streams from Spotify. Visualize this as a line chart inside the product.
Upper and Lower Confidence Bounds: The model provides confidence intervals (“upper” and “lower” bounds) for each prediction, indicating the range within which the actual stream count is likely to fall.
Velocity: Rate of change in streaming performance, indicating momentum. Higher velocity suggests faster growth. (If shown in the Songbird output, include an explanation and visual representation, perhaps a separate line on the chart showing velocity).
Recommendations (If applicable): For selected labels, K5 includes specific marketing interventions and their estimated impact (“lift”) on streaming performance. These are shown in Recommendations
section.
For example, running a TikTok influencer campaign has an estimated lift of 1,425,020 streams, while running press/PR only has an estimated lift of 300,000 streams, meaning it is likely to cause a lower lift in streams. (Include screenshot)
Limitations:
Example Scenario:
A label releases a new track and uses K5 to monitor its performance. After a few days, they observe that the track is underperforming compared to predictions. K5’s causal inference module recommends increasing investment in a specific marketing channel (e.g., TikTok influencer campaign).
The label implements the recommendation and observes a significant positive impact on streaming numbers, demonstrating the value of K5’s insights for real-time campaign optimization. After the initial two weeks, they switch to the K7 model for long-term performance forecasting and strategic planning.