Turning Up The Music With iHeartMedia

With over 850 live broadcast stations in 160 markets, iHeartMedia partnered with Aviana to optimize music programming operations using AI-based technologies. The results include an Aviana model that predicts song popularity, building recommended playlists tailored to specific genres, markets, and song mixes, increased efficiencies in the time it takes to build playlists, and reduced labor costs through the improved management of music programming for multiple stations.

Listening To The Data

Model – Research Response:

Utilize song and artist-level local & national data from research conducted by stations, radio play, song surveys and various, on-demand streaming and chart rankings to create a “consensus, data driven” research response estimate.

Use Cases & Benefits:

  • Provide more stable music data that is less subject to “wobbling” from test to test
  • Provide research information for more songs each week
  • Provide research data when station-level research is not available

Creating The Perfect Playlist

Model – Song Popularity

Utilize song and artist-level local & national data from research conducted by stations, radio play, song surveys and various, on-demand streaming and chart rankings to predict song popularity today, and in the future, to derive recommended play lists tailored to specific user profiles and geographies.

Use Cases & Benefits:

  • Predict the song “lifecycle” curve to supplement gut-feel with data.
  • Identify what are the best songs to add to rotations.
  • Identify which songs to move into a power position.
  • Get the most out of a popular song and not move a song out of rotation too soon. Identify which songs should be moved out of rotation.

Predicting The Next Big Hit

Model – Early Indicator

Predict early in the song lifecycle that a song will be a hit.

  • SPSS Modeler was used in all exploratory data analysis, data wrangling and the creation of models in the projects.