Spotify (Re)Wrapped
Helping listeners rediscover the music they saved, loved, and forgot
Overview
Project Type: Academic Product Concept & High-Fidelity Prototype (Stanford CS 377U)
Role: Co-Lead Product Designer & UX Researcher
Team: 4-Person Collaboration (Sophie Chen, Helen He, Anchal Sayal, Jasmine Steele)
Timeline: Apr – Jun 2024 (3 Months)
Tools: Figma, Spotify API, Node.js, Heroku
Challenge & outcome: People save tons of music but rarely revisit it. We built a prototype that resurfaced forgotten songs using real Spotify data, and a field study showed users want rediscovery with control — not a black‑box feed.
Focus: End-to-end product design, UX research & field studies, high-fidelity prototyping, API integration, user agency & transparency
Legal Disclaimer
Note: This was an academic product concept and is not affiliated with Spotify. The “(Re)Wrapped” name was used as a playful framing device for a rediscovery experience.
Problem
Saved music stays available, but it often disappears from everyday listening
Saved music doesn’t disappear. It becomes invisible.
Over time, users accumulate songs tied to moods, memories, and phases of life, but rarely revisit them. The problem wasn’t a lack of music. It was a lack of meaningful ways back to the music they already loved.
This created the product opportunity: how might we help listeners reconnect with songs they once cared about, while giving them enough control for rediscovery to feel personal instead of random?
Why this matters
Rediscovery is emotional, identity‑driven, and deeply personal, but Spotify offers no intentional way to return to meaningful older music.
Why now
Rediscovery is also an increasingly relevant problem as libraries grow and algorithmic feeds prioritize novelty over memory.
Initial interviews
People did not only want new music. They wanted better ways back to old music.
We interviewed five Spotify users with different listening and curation habits, from casual listeners to heavy playlist-makers. Across participants, saved music acted as archive, memory bank, mood tool, identity marker, and background comfort.
Some recurring patterns emerged:
Saved music acts as archive, memory bank, mood tool, identity marker
Users rarely revisit older songs unless prompted
Rediscovery is emotionally rewarding but unpredictable
The strongest insight was that people do not only want new music. They also want better ways to return to music that already belongs to them.
Across participants, saved music served different roles: archive, memory bank, mood tool, identity marker, and background comfort.
“I don’t have time to create playlists anymore… I would make a playlist for everything when I was younger.”
“I’m not particularly adventurous when it comes to music. So finding something that’s similar… that’s what I look for. And it’s comforting in a way.”
“I usually name playlists based on things I’m feeling in the moment.”
Product direction
We named the concept as Spotify (Re)Wrapped, a reversal of Spotify’s famous Wrapped feature.
Spotify Wrapped summarized what users had recently played throughout the year; our (Re)Wrapped would resurface songs they had saved in the past but no longer listened to.
The goal was not to replace playlists or recommendation feeds. It was to create a lightweight rediscovery moment: personal, low-pressure, and easy to act on.
A successful experience needed to:
Resurface older saved songs
Make the rediscovery feel personal
Give users enough control to avoid irrelevant recommendations
Help users decide whether to keep, replay, or recontextualize each song
Early prototyping
The concept resonated, but users wanted more control over what resurfaced.
We started with paper prototypes to test the basic interaction model before building the live version.
The early flow focused on three questions:
What counts as a forgotten song?
How should rediscovered songs be presented?
What should users be able to do with them afterward?
Early feedback validated the emotional appeal of rediscovery, but also exposed the first major risk: users did not want a black box deciding what deserved to come back.
They wanted to understand why a song was resurfaced and adjust what appeared. That pushed the design away from passive automation and toward a more controllable rediscovery model.
Below: Prototype flows showing connection to a participant’s Spotify account and generating rediscovery from their own saved music.
Interactive prototype
To make the concept testable beyond static screens, we built a working prototype using the Spotify API, Node.js, and Heroku.
The prototype connected to a participant’s Spotify account, pulled real library data, and generated a rediscovery experience from their own saved music.
This changed the quality of the research. Prototyping with real listening histories made the research dramatically more personal and specific: something that static screens could never achieve.
Real-world field study
Rediscovery worked emotionally, but not as a black-box feed.
We ran a field study with 11 participants to understand how the prototype worked with real Spotify libraries.
The field study confirmed the appeal of rediscovery, but changed our understanding of the product.
Overall, participants enjoyed encountering older songs again. Many described the experience as nostalgic, surprising, or useful. But the study also showed that rediscovery could not succeed as a purely automated feed.
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For some participants, forgotten meant not played in years. For others, it meant saved but never intentionally revisited. Some thought in terms of time; others thought in terms of mood, genre, artist, or playlist origin.
Design implication: Rediscovery needed user-defined filters, not a single system definition. -
Rediscovery was valuable, but users did not want resurfaced songs to automatically clutter their library or playlists.
Design implication: The experience needed lightweight actions such as replay, dismiss, save to a chosen playlist, or create a temporary queue. -
Some participants hesitated during authorization because they were unsure what data the prototype would access or how it would be used.
Design implication: A personalized rediscovery product needed clearer data explanations before asking users to connect their account.
Product recommendations
The strongest next step was user-defined rediscovery.
The field study showed that rediscovery needed to become more user-directed.
I would prioritize:
Letting users define “forgotten” by time range, listening frequency, genre, artist, or playlist source.
Showing why each song was resurfaced.
Supporting lightweight actions that do not automatically clutter the user’s library.
Making authorization and data access clearer before account connection.
Offering temporary rediscovery sessions rather than permanent playlist changes by default.
Together, these changes would make rediscovery feel less like an opaque recommendation feed and more like a collaboration between the listener and the system.
Impact
This work clarified what rediscovery should feel like: intentional, transparent, and user‑directed, and our findings made that clear. We:
Validated the emotional value of rediscovery
Identified the need for user‑defined resurfacing rules
Demonstrated feasibility through a working API‑driven prototype
Revealed trust and transparency as critical design requirements
Reflection
Looking back, the strongest part of the project was the product insight: music platforms often emphasize discovery of the new, but there is also emotional value in helping users return to what they have already saved and forgotten.
The field study made the concept more mature. We began with a playful wrapper around old liked songs, but learned that the deeper product questions were about control, trust, personalization, and the meaning of “forgotten.”
Personalization only works when users understand and trust it
Because this was an academic project, we moved quickly from initial interviews into concept development and prototyping. In a real product setting, I would spend more time on discovery research before solutioning.
I would want to understand how different listener segments already revisit saved music, where rediscovery breaks down today, and whether “forgotten music” is the right framing for the problem. I would also compare the needs of casual listeners, playlist curators, and heavy library users before defining product requirements.
That earlier discovery work likely would have surfaced the need for control and transparency sooner. Instead of learning during the field study that users did not want a black-box resurfacing experience, we could have used discovery research to shape the concept around user-defined rediscovery from the beginning.
This was one of the biggest lessons of the project: research is not only for testing whether a solution works. It is also for making sure the team is solving the right problem before investing heavily in the solution.