Inspire people without saying a single word to them.
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Music has become such an important part of people’s lives. However, discovering new music is something that people find restrictive and limiting. Recommendation engines are predictable and don’t stray from a predefined algorithm.
Identifying the need for a product based on market research, I rapidly came up with some ideas and conducted research to validate and iterate through my ideas. I then created wireframes and prototypes for Muse– a platform which lets users broadcast their music and podcasts to their immediate surroundings for others to discover and listen.
Muse is a platform which lets users broadcast their music and podcasts to their immediate surroundings. Other users can see the music and podcasts being broadcasted around them and listen to the shared audio on their respective devices.
Music has become such an important part of people’s lives– whether they are commuting to work, cooking at home, or hosting a party, music is often an essential element. However, discovering new music is something that people find restrictive and limiting. The main sources of music discovery are recommendations via apps or friends.
It’s difficult to remember what songs and/or podcasts your friends recommend and to keep track of them. The recommendation engines are predictable and don’t stray from a predefined algorithm.
Before thinking of building a product to address the problem, I did some research about the music streaming industry to understand the market and find the right segment to tap into.
Based on my research I found that 64% of 18-34 year olds report that they listen to music everyday*.
The live streaming economy is currently at its peak as demonstrated by companies like Clubhouse, Twitch, Instagram and TikTok live.
I learned that the specified market largely consists of the demographic who is part of the live streaming ecosystem– people who listen to music almost everyday and who are actively looking for new recommendations.
*Source: https://www.statista.com/statistics/749666/music-listening-habits-age-usa/
To validate this information, I sent out a quick survey to about 30 people. The results of the survey motivated me to think of potential solutions that could make the music recommendation process more organic.
As I was listening to music, I noticed that there was an opportunity to add a new way of broadcasting music on the Apple Music app.
This quick mockup of this broadcasting feature doubled as an artifact which helped users visualize the concept and give me feedback.
“The term broadcasting is confusing. I'm not sure what it means and I can't see where my music is being broadcasted”
With this feedback in mind, my intention was to make the next version more explanatory in terms of sharing music. I was inspired by Apple's AirDrop interface and decided to come up with something similar to signify the ability of the user to share their music with everyone around them.
“The option to broadcast your music around you and also see what others are listening to, is something I would definitely use”
Having validated my concept through this rough prototype, I decided to work with a team with a diverse skill set to build the concept. We as a team decided to do more user-centered research to decide what features would be most useful for users.
These quotes by participants helped me come up with a couple of insights-
While these insights may seem slightly contradictory, they impose a constraint on the design which can be a good thing.
Based on our initial research and interviews, we came up with a user journey map which encompasses the different stages in the day of our target user. This was done to highlight pain points and identify opportunities in a user's journey to intervene and improve the user experience.
To represent our target users, we came up with two user personas of our target users, based on the contextual interviews we conducted. These personas helped create a tangible target user to design for and to communicate this user to the developers.
Our research process shaped the journey we took towards finding a solution to this unique problem. To bring the whole team on board with the concept of Muse, I decided to represent our solution through a storyboard.
Before getting into the design and development process, I felt it necessary to do a technology feasibility analysis to validate our concept and ultimately to make it easier to explain to developers how it would work. I worked with another developer to come up with a high-level architecture of our app.
The research and technology feasibility analysis helped us envision a tangible product and directed the wireframing process. We addressed some concerns which users had in our new design of the app.
We established a design style guide to help standardize the various elements that we intended on using to create the high-fidelity prototype.
We chose SF Compact and Montserrat as our fonts because they are legible on mobile screens. The colors serve the same purpose– to provide maximum contrast and maintain the minimal theme of the app.
Creating the final prototype from the wireframes was relatively easy since our wireframes were high-fidelity. My goal in designing the screens was to keep them simple and minimal to focus more on the functionality. This was challenging because I had to prioritize the broadcasting screens as well as the statistics section.
Click to view the prototype
Having designed the product, we set out to develop it to see if it could work in the real world. As we started the development process, we came across a couple of challenges like finding suitable Bluetooth messaging libraries for React-Native and, addressing the security and privacy concerns associated with Bluetooth.
Constraints nurture creativity. To address these concerns and build an MVP, we brainstormed different possible ideas to achieve similar functionality and decided to use location based search instead of Bluetooth.
The new algorithm we devised would collect users’ locations and store them in a database. The system would then parse through all the locations in the database to display the top five active users closest to the broadcaster.
As we went about building and testing Muse with users, I realized that this would be more impactful as a feature instead of a standalone app. The reason we didn't make Muse another music app was because we didn't want users to decide between us and the other well established music apps like Spotify or Apple Music.
Based on the feedback we received when we tested our prototype on users, I decided to work on these in future iterations of the app-