Make new features usable.
A persistent “?” hub introduces features, offers quick practice, shows eligible beta experiences, and lets the listener choose interface complexity.
Frank Cadle
Close project
Helping listeners discover what Spotify can do—and express what they want to hear through five favorite tracks and a clear set of choices.
Personal observation
I discovered swipe shortcuts for queueing and saving songs through use rather than a clear introduction. That experience raised a design question: how could Spotify help listeners learn useful actions while they are doing the task?
I also wanted to describe a listening session through several favorite songs, then shape its tempo, genres, familiarity, and length. These observations are the starting point for research; they do not establish how frequently other listeners encounter the same friction.
A persistent “?” hub introduces features, offers quick practice, shows eligible beta experiences, and lets the listener choose interface complexity.
Five favorite tracks anchor a mix. Visible choices let the listener prioritize BPM, related genres, familiar favorites, or new discoveries.
Current product context
Spotify Radio starts from an artist, album, or song. Prompted Playlist already supports requests and rules. Running Mode includes controls for tempo, genre, familiarity, and duration. Blend is a social playlist that combines different listeners’ tastes.
FAVFIVE explores a visible five-track starting point and a structured way to explain preferences. The learning hub explores whether those tools and shortcuts can become easier to find, understand, and practice.
The proposed one-hour Free and two-hour Premium limits are product hypotheses for this concept. They are not Spotify’s current playlist limits.
Product context reviewed October 1, 2026. Availability varies by plan, platform, and market.
FAVFIVE / Working concept
A one-hour mix shaped by Frank’s five songs. Choose a direction, then explore the first three matches.
Match the five seeds’ editorial genre and mood tags, favor handpicked deeper cuts, and limit repeats to three songs per artist. Emphasizing a pool increases its weight; the mix keeps all three pools represented and adds full tracks up to one hour.
Track titles and lengths come from artist catalogs. The tags and deeper-cut choices are editorial judgments, not measured popularity. This small catalog does not check listening history, analyze audio, play songs, or create a Spotify playlist.
Catalog sources: Loyle Carner, Noname, Cleo Sol, Yazmin Lacey, Parcels, SAULT.
Short lessons. A layout that fits how you use it.
In this proposed lesson, swipe right to queue and left to save. You can also use the buttons.
Practice without changing your library.
A proposed beta hub would list only experiments available to that listener, with clear opt-in and a way to leave.
Your listening brief
This is a preview of the request, not a generated playlist.
Design decisions to test
The “?” entry stays top-right. Brief contextual cues can point out a new action, while lessons remain dismissible and available to revisit.
Simple, Standard, and Advanced change guidance and visible tuning. Listeners can switch levels without losing their choices.
Five seeds anchor the request. Tempo, genres, and discovery express intent. When preferences conflict, explain which priority shaped the result.
A beta hub provides eligibility, an explanation of what changes, explicit opt-in, and a clear way to return to the familiar experience.