iHeartMedia | My Favorites Radio Improvement

My Favorites Radio (MFR) is an iHeart personalized radio station generated by machine learning based on a user's thumbed tracks, listening history, and followed stations. During my internship, I teamed up with Product Management and UXD to devise a strategy to better surface MFR on iHeart's beta web platform Polaris.

Some background about MFR: Unlike iHeart’s other personalized content offerings such as Made For You playlists, MFR is not surfaced on the Polaris homepage. It can only be accessed via search, the user’s Library, or the Recently Played carousel. MFR also does not have a profile page, which was an opportunity area we wanted to explore.

Our objective was to to increase engagement and stream start on MFR, predicted to increase CUME and total listening hours (TLH) in the long run. We identified two pain points users frequently have in regards to the station: they do not know what it is and they don’t know how to navigate to it.

I developed design solutions to address these through user education and improved value propositions, including revised description copy, naming convention, tooltips, a profile page, ideas of where to surface MFR, and explorations of ways to better communicate to users the relationship between thumbed tracks and MFR playback. We proposed a phased approach to improve the MFR product after conversations with client engineering, backend engineering, MLE, and product about engineering lift and long-term product goals.


This is an ongoing initiative. Questions? Happy to chat more about my internship @ iHeart :)