Artist Affinity Score refers to the data science that determines how listeners are targeted with Artist Audio Messages on Pandora.

This methodology looks beyond the overt actions a listener takes which clearly indicate fandom (creating an artist station or thumbing up an artist’s song) and instead studies a listener’s behavior to determine whether or not they are likely to enjoy an artist message in a given context. 

By studying a listener’s behavior, we can better understand their nuanced musical tastes and draw conclusions about which artists they would want to hear from. This behavioral study allows us to determine an individual listener’s affinity (positive attraction) for an individual artist, which then translates their Artist Affinity Score, represented as a numerical value between 0 and 1. 


Musical taste is highly subjective. Knowing whether a listener likes or doesn’t like an artist is helpful, but determining how much they like an artist is far more useful if the goal is to maximize marketing reach. Do they like an artist enough to watch a music video? Enough to stream an album or single? To go to a show? With Artist Affinity Score, we are able to translate a listener’s positive relationship with an artist into a numerical value, and use that to drive much more accurate and effective Artist Audio Message targeting.

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