This capstone project was started with the broad goal of improving streaming music services for music listeners. To tackle this concept, I researched current and past music listening products (over 100 exemplars) and interviewed people about where, why, and how they listened to recorded music. Based on literary analysis of other work in this field, I made sure to interview and do a diary study with a self-identified "taste influencer" - a college radio DJ and music team leader.
One interesting use case of recorded music that kept coming up in interviews was in-person, controlled, music listening or playing music from a streaming service or other "on-demand" source with two or more people. While people mentioned this happening at home or in public sharing headphones, the most common occurrence of this was hooking up a phone to a car's entertainment system.
In fact, a major pain point brought up for many interviewees including the taste influencers was the anxiety around choosing good music for a group to listen to in the car. People mentioned the fear around being "handed the aux cord" by the driver on a long drive.
While there is an amount of fear surrounding sharing music tastes with other people, my primary research and academic sources show that music taste is highly influenced by a person's friends and family. Many people even cite a recommendation from a friend as the first time they heard what became their "favorite artist/band". Music taste is dynamic and can change over time. Often these changes are either a result of hearing related artists (something music streaming services have gotten particularly effective at) or a recommendation from a friend or family member. Through interviews, I found that often a recommender will give a brief verbal "priming" before recommending and playing a song to another person. This often contains a personal story about the music, some background/historical information about the artist or album, and often a mention of some sort of music the recommender thinks is related that the other person already likes.
After several rounds of quick ideation, I decided to pursue the ideas in JamSquad further. I wanted to design a prototype/system for the in-person group music listening experience that caters to the taste influencers as well as the people who feel anxiety about choosing music for groups of people.
The application uses users' listening metadata from their streaming service as a design material. Because of this, it requires a connection to Last.fm or Spotify with permissions to access information such as top songs and artists for a user and their "squad".
A user would open the application, sign in, and add people to their squad that they are about to listen to music with in-person. Ideally, for privacy sake, users would have to have added each other as friends on the service beforehand but this also could be used to plan out a playlist for a future music listening session!
A key feature of JamSquad are the auto-generated playlists that are based on the music data of the users in the "squad". These playlists are meant to take away some of the social responsibility that comes with picking the music in a group situation.
The playlists range from songs that most of the group has listened to (Venn Diagram), to Top listened songs from each member of the squad (Taking Turns), to songs that are similar to music that several people have listened to but possibly songs that no one in the group has heard (Shared Discovery). These playlists are meant to provide different experiences for the users.
Inside a playlist, the user can see the songs, what members of the squad have listened to them, and related artists that squad members have listened to as a way of providing context if a squad member has not heard the song before.
The search feature is designed to help a user give a music recommendation to their squad. When they search for a song, they can see who else in the squad listens to the artist and what related artists members of the squad listen to. This can help the recommender know what artists to mention if they brief the members of the squad on the song before playing it.
The recommender can also see a faint, color-coded evaluation of how similar the song is to music that the squad listens to. This is not meant to discourage any recommendations, only to let the recommender know that they might need to do a bit more briefing!
Finally, on the player page, the same information about related artists and listeners is shown for when a recommender may want to talk during the song and further recommend the song or artist to members of the squad.
User testing through the process of creating the prototype led to several interface changes and comments and observations through these tests led me to include options to show the squad's related artists to a song at every step of the process. Testers noted that the use case of the application is somewhat rare as the majority of their listening time is not with people they are trying to recommend music to. As streaming services become more and more mainstream, I believe more specialized apps and features will become part of the landscape and more "niche" use cases like this will become key parts of music streaming service design.
Feel free to look over the poster, booklet, inVision prototype, and of course video walkthrough that were created for this project's capstone presentation below!