iHeart Media — Ryan Uchi O’Neill

The challenge

How can we automate the job of a DJ?

Ever wonder why you hear some songs more than others? iHeart uses Nielsen ratings, surveys, and interviews to understand how often they need to play a song. They didn’t have a good way of implementing this data across all of their 855 radio stations.

Listener research data feeding into scheduling decisions across the station network

The process

Heuristics reports, interviews, and testing

I started evaluating their current tools using Jakob Nielsen’s 10 Heuristics for Usability. After generating this report I sat down with DJs, Program Directors, and Station Managers to understand their work flows. As I designed workflows we validated them with user testing.

The heuristic evaluation of the existing scheduling tools

Workflow findings from sessions with DJs, program directors and station managers

Automatic categorization

Let the data speak for itself

When music research scores arrive songs are sorted into categories based off of a proprietary scoring system and weighting based on station type.

Incoming research scores sorting songs into weighted categories automatically

The human touch

There is accounting for taste

During interviews I discovered the DJ’s favorite part of their job is looking for the one song that is going to explode. I wanted to make sure they could still grab that song and bump it up the list.

A song being picked up from the list — 1. Select a song and drag

The category menu opening as the song is dragged — 2. Category menu flies out

Dropping the song onto its new category — 3. Drag to the category

Confirmation that the song has been recategorised — 4. Confirmation

The results

Data helps humans take action

By intertwining data and the human touch we created an environment where DJ’s can easily take action on listener feedback and identify songs that are on the verge of breaking out.