The encyclopedia · Product & Design · Technical decision · 2020–2024
iHeartMedia optimized playlists to play hits without burning them out
iHeartMedia pairs song-popularity forecasting with an optimization engine so 24/7 playlists are strong today and still have tomorrow's hits in rotation.
iHeartMedia
the move
iHeartMedia programmes hundreds of radio stations, and each needs a playlist that is commercially strong yet still rotates new music before listeners tire of a hit.
The team built models that predict current and future song popularity — a lifecycle curve — from song and artist data and listenership, then used a mathematical optimization engine to produce compliant 24/7 playlists that balance strength and diversity.
The engine removed guesswork from which songs to add, raise into power rotation and retire, cutting the time and labour needed to programme many stations at once.
why it works
- A playlist that only plays proven hits burns them out quickly
- Predicting the lifecycle curve identifies which songs are rising
- Optimization applies the same scheduling rules across every station
- Automating capture and rotation decisions lowers programming cost
what transfers
When a product has a lifecycle, curate by predicted age as well as current popularity; optimizing strength and diversity together keeps today's audience without spending tomorrow's.
what came after
iHeartMedia's playlist optimization was named a finalist for the 2024 INFORMS Daniel H. Wagner Prize. The models and engine are used to programme stations across many markets from a single platform.
references
- iHeart Media
- Introduction: 2024 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research
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