Pandora Station Tile Artwork

Case Study

The Problem with Pandora Stations

While most station types on our platform have long-standing guidelines for artwork,
song and artist station types default to the image provided by the creator, with no additional design treatment - despite artist stations accounting for over 70% of station listening time.


In multiple moderated user testing sessions, participants expressed confusion around the lack of dedicated artwork denoting these stations, and those who were not already familiar with the platform often failed to correctly identify an artist or song station.



Why Product Design?

At Pandora, station artwork has historically been created and managed by the Brand & Marketing team.
Our team approached theirs after identifying the confusion in users, and it was agreed that a number of factors meant that this project was right for the product design team to drive.

There are a theoretical infinite number of artist & song stations — therefore the design needs to be deeply integrated with the product backend and accommodate any image that already exists in our library.

We were concurrently working on a new Station Builder feature for Pandora, where users can select multiple artists to generate custom stations based on their choices. This would require an additional layer of generative design, where content from multiple artists comes together in artwork for every station created.

In addition, there is currently a concentric circle overlay on all Pandora stations, which has been marked for removal based on engineering scope. The technical constraints involved meant that the most efficient design process would be to work alongside engineers to implement a new artwork system, while removing old + outdated features.


Design Process
Competitive Analysis
Shape Study

Layout exploration — 
placing multiple circles within a square tile ensuring minimal overlap to maximise visual legibility


Colour Study

Pandora currently uses dominant colour picking in various situations, particularly within the Now Playing experience for users subscribed to our Premium tier.

Initial work explored generating gradient backgrounds automatically built based on an input image’s dominant colour.

Common colour algorithms would be used to determine complimentary values for appealing gradients.

Various gradient types were tested, with consensus landing on Angular Gradients.