Spotify
MBTI Music Recommendation Case Study
Visualizing a MBTI feature on Spotify Music to engage users and offer personalized playlists, songs, podcasts, and artist recommendations that align with unique personalities
January - May 2022
Project Timeline
Mobile web design
Project Type
Figma, UX Research, UI Design, Prototyping
My Impact
As a music listener, I want to deviate away from my usual listening patterns and hear new suggestions.
But I can’t do that well because:
Computer generated playlists can recommend music that has strayed too far away from my usual music taste.
Random recommendations do not feel sincere coming from an app that caters towards different kinds of users and therefore, the suggestions do not add to my personal brand and taste
The Problem
User Interview
Research
I conducted interviews with various users from diverse demographics, with a focus on age groups. I investigated the role that music plays in their lives and what these users enjoy and dislike about Spotify as a platform.
User Personas
My Insights
Spotify users do not like to explore too much out of their current music taste unless a friend has given them a song suggestion. Users prefer personalized music recommendations over computer generated ones.
Users often gravitate towards their own liked songs and playlists, rather than the Spotify curated ones. Users like to stick to what they are already familiar with, rather than try something new.
Users sometimes dislike Spotify playlists when trying to discover new music. Users find the playlists radically different from their usual taste and feel uncomfortable with trying to branch out.
Most users enjoy Spotify’s unique efforts of celebrating a user’s taste. It allows the users to discover their individualized taste and their listening habits.
Users’ listening experiences do not change by follower’s taste, unless directly told by that follower to explore a certain song. This is due to the appreciation that one may feel when one offers them a personalized suggestion.
“Sometimes the playlists that they make are so random that they don’t align with the current music I listen to and I’m not really sure what algorithm they choose.”
The Social Butterfly
Listens to Spotify everyday for 4-5 hours, especially for daily, routine tasks
Likes to share music with friends as a way to bond and find new music
The Curator
Listens to Spotify everyday for 2-3 hours
Likes to spend time creating playlists that adds to their personal aesthetic
The Independent
Listens to Spotify everyday for 1-2 hours
Finds music listening to be an independent activity, rather than a social one
Ideation and
Opportunity Spaces
Intimate connections: Urging friends to send each other new song recommendations directly through the app.
Community: Encouraging users to take up new recommendations that a community of listeners are interested in.
Gamify: Creating challenges and games to encourage users to explore new music — create an entertaining experience to push users out of their musical comfort zones.
Solution
Spotify asks the user personality questions (based off the Myers-Briggs Type Indicator Test) and offers song suggestions based on the results.
1. This feature offers a personalized music experience that exposes users to music they are not familiar with. This feature offers recommendations that speak to the personal brand of each user, helping their personal style be seen and recognized by Spotify.
2. This feature creates a space where Spotify could encourage new music listening without requiring them to rely solely on the activity of other users. This is important because users could find sending recommendations burdensome and users do not find Spotify as a social app.
3. This feature faces small threats like the user’s fear of publishing personal information regarding their personality type, which does not prove as a large challenge to the feature’s implementation.
Low Fidelity Designs
I got to creating information hierarchies to envision where this feature would be accessed.
I was able to come to understand that because music exploration is closely connected to the original goals of Spotify, this feature can be seen in many parts of the app.
Home dashboard
Settings
Recently Listened
(first 6 widgets)
Recommended albums,
songs, podcasts
Top mixes / Made for (username)
Follow Playlists and artists
Liking or downloading
the song / playlist
Profile
Playlists
Followers
Following
New feature
Recommended albums,
songs, podcasts
New feature
Search
Browse all (genres)
Featured Podcasts
Top Genres
Search Bar
New feature
Search Bar for your library
Your personal playlists
New Episodes for
following podcast
Liked songs
Create a new playlist
Your Library
Settings
Profile
Playlists
Followers
Following
Mid Fidelity Designs
I was able to explore different entry, middle, and end points of the new feature.
I focused on creating different ways for the user to discover the feature within the interface, which I was then able to choose the two most effective entry points: on the homepage and on the search page.
I decided to place the feature on the home dashboard and under the search bar because these entry points seemed the most natural and easiest for users to seek out when looking for new music recommendations.
Entry Point
Middle Points
For those who know their Type
End Points
High Fidelity Designs
Conclusion
Although it took a lot of time, energy, blood, sweat, and tears, I really enjoyed creating an in-depth case study on one of my favorite apps. Through this process I have found that human-centered design is imperative to solve any issues that we face, no matter how big or small they are. Even if my feature helps one user, I would feel ecstatic to see that I helped facilitate music exploration for a struggling Spotify customer.