My
Case Study
Role
Product Designer
Tools
Figma / Notion
Status
In Progress
MoodMate Case Study: Mood Generated Playlists Mobile App 🎧 Using Spotify API
Inspiration:
I began this mobile app case study after meeting a product design colleague through a mentorship program — though the core idea behind this app had sparked in my mind years ago. As a long-time Spotify user and music lover, I’ve always appreciated its massive library and curated playlists. But I often found those playlists didn’t quite match my mood — even when sorted by emotion labels. Some tracks or genres just didn’t feel right for me.
Music is deeply personal — it's how many of us process, express, and navigate emotions we can’t always put into words. So I wondered: what if there were an app that could generate mood-based playlists tailored to our actual tastes — not just what mood we select, but the full emotional spectrum, delivered in a fast and intuitive way?
Years later, I came across an experimental algorithm that did just that. By inputting your Spotify username and a mood value between 0.0 and 1.0 (a smooth scale from low to high energy/valence), it uses Spotify’s API to pull from your real listening history, favorite tracks, and followed artists — generating playlists that resonate both emotionally and personally. Voilà!
Knowing this was technically possible, I was excited to turn it into a real design challenge. I conducted user research, explored emotional behaviors around music, analyzed competing apps, mapped flows, sketched early wireframes and designed the initial UI/UX interaction on the core mood log screen. This journey isn’t finished yet — but each step brings me closer to shaping something that feels intuitive, human, and deeply connected to how we experience music and emotion.
My Role
Product Design, Research, UI/UX Design, Interaction Design, Wireframe, Lo-Fi, Hi-Fi, Visual Design, Prototype, User Testing
Tools
Created for
iOS
Status
In Progress
Overview:
MoodMate is a mood-based music companion iOS app designed to help users track their emotions and discover music that fits their emotional state through personalized Spotify playlists, powered by valence-energy-danceability music filtering algorithm. MoodMate focuses on blending emotional awareness with musical discovery, while laying the foundation for future AI-enhanced capabilities.
Problem:
People often struggle to put their emotions into words but can intuitively connect with music that matches how they feel. Existing music apps don’t always make it easy to explore music through the lens of mood and don't offer ways to connect mood to personal music taste. I want to create a frictionless way for users to log their feelings and discover music that resonates with them—without needing to overthink or search manually.
Solution:
Design a lightweight, emotionally intuitive app that allows users to log their mood quickly and receive a customized Spotify playlist that aligns with how they feel, helping them either embrace or shift their emotional state. The UX should feel expressive, not transactional, and require minimal cognitive load.
User Research:
Process:
To better understand emotional listening habits and user pain points, I ran a lightweight Typeform survey and interviewed five users. The survey combined quantitative data with open-ended questions and was sent to 20 early adopters.
Findings revealed a shared emotional dependence on music — not just to reflect moods, but to regulate them. Many respondents said they turn to upbeat or nostalgic songs when feeling low to lift their mood.
However, users found it frustrating to manually search for the right music each time. While Spotify’s algorithm offers broad recommendations, it often fails to match their moment-to-moment emotional states. Traditional mood-tracking apps weren’t popular either — many said they felt “too clinical,” “too data-driven,” or “just another task to check off.”
These findings reinforced my goal: to design a faster, more intuitive, and emotionally attuned experience that bridges mood and music in a more personal way.
Insights: ⭐️
Mood-based recommendations felt more personal than genre-based.
Emotional intention matters — Users don’t just want playlists that reflect their mood; they want playlists that respond to it.
Many appreciated visual ways of expressing emotions (color, emoji).
Users wanted seamless integration with Spotify and willing to pay.
Low-friction design is critical — Mood selecting must be effortless, otherwise they might abandon it quickly.
Personas:
Our target users are emotionally self-aware Spotify Premium listeners who enjoy using music to support their mood. They range from students and creatives to young professionals, often using music as a form of emotional regulation, background focus, or escape.
Name: Alexandra, 28
Occupation: Fashion Designer
Personality: Playful, introspective, expressive
Tech comfort: Moderate–High (loves aesthetic, intuitive apps)
Goals:
Regulate mood and find motivation when feeling overwhelmed
Discover music that aligns with her emotions in the moment
Painpoints:
Spotify suggestions don’t always match how she feels
Name: Rohan, 19
Occupation: Com' Science University student / music enthusiast & influencer
Personality: Smart, fun, outgoing
Tech comfort: High
Goals:
Manage academic stress
Find inspiration for playlists that match his taste
Having collection of personal playlists to share with friends
Painpoints:
Doesn't want to build playlists manually every time
Name: Maya, 37
Occupation: Freelance WFH
Personality: Introvert, private, creative
Tech comfort: Moderate
Goals:
Use music to boost creative flow
To escape and have personal self time
Log her emotional state casually without feeling “too tracked”
Painpoints:
Gets overwhelmed juggling work and parenting, doesn't enjoy using complicated apps
Finds mood tracking apps cold and boring
Competitive Analysis:
I conducted competitive analysis on music streaming apps, none of them except Deezer have mood log and personalized mood based playlist features. While Deezer does have the 'mood selection wheel', their mood granularity is broad 6 zones, but MoodMate algorithm can generate detailed 9-11 zones with custom sub-emotions to offer better personalize playlists. Deezer's UI is also less known and the UX once entered the playlist doesn't feel connected or streamline with the user's mood input anymore. I also looked into mood log apps that aren't too clinical type, but these apps also don't focus on music experience.
Takeaways:
There's an opportunity to bridge the gap between emotional self-awareness and music consumption.
None of the competitors currently provide both mood journaling and direct Spotify playlist creation in one seamless mobile experience.
Personal music preference is largely missing from mood-based soundscape tools.
Most apps don’t visualize moods in a way that's easy to reflect on later (color + emotion + music).
Combines emotion logging, visual mood tracking, and custom Spotify playlists in a single elegant flow.
Gives users an emotionally intelligent music assistant with real personal music taste — not just algorithmic ambient sounds.
Provides value in a more engaging way over time by allowing users to look back at their emotional state through custom playlists archive.
Offers a highly personalized experience without needing users to manually browse or search for playlists.
Lo-Fi / Hi-Fi Sketches & Design Decisions Rationale:
After distilling key insights from my research, I began sketching Lo-Fi wireframes that mapped out typical music app flows (note: early drafts were missing login and secondary screens). My focus from the start was on the core UX and interaction design of the mood selector.
I explored two initial concepts: a traditional slider and a form-based input. The slider, while simple, felt constrained by mobile screen dimensions—it couldn’t represent the emotional nuance I aimed for. It also deviated from iOS interaction patterns. On the other hand, the form-based approach required multiple taps and decision points, which compromised ease of use.
Eventually, I arrived at what I hoped would become MoodMate’s signature experience: a vertical scroll-based mood picker with a central snapping indicator bar. Users scroll through a gradient of color-coded moods, each paired with an emoji and mood label, then tap “Listen to My Mood” to instantly generate a Spotify playlist. The app maps mood ranges to real-time Spotify audio features like valence, energy, and danceability.
This design emphasizes intuitive interaction, dynamic emotion feedback via tooltips, and a clean visual theme using color rows. The goal is to create a seamless feedback loop between emotional self-awareness and music curation—helping users better understand and influence their moods through sound.
I prioritized frictionless mood logging and automated playlist generation as the MVP’s core value. The underlying algorithm filters tracks using predictable emotional inputs, so users receive personalized playlists with minimal effort—just a single scroll and tap. The Archive tab supports emotional continuity by letting users revisit their past moods and corresponding playlists, promoting reflection and music rediscovery.
While advanced features like AI-generated music prompts are part of my long-term vision, I’ve deliberately scoped them for future releases to maintain simplicity in this MVP. However, I’ve already planned the future UX flows to future-proof the design. This reflects a product mindset grounded in iteration, emotional impact, and technical feasibility.
Sample Mood Logic Mapping 🎛
Mood Range | Mood Name | Valence | Energy | Notes |
0.00–0.15 | Overwhelmed | 0.0–0.2 | 0.2–0.4 | Slower, ambient, sad |
0.15–0.30 | Heavy / Numb | 0.1–0.3 | 0.1–0.3 | Minimal beat, soft vocals |
0.30–0.45 | Melancholic | 0.2–0.4 | 0.3–0.5 | Emotional ballads, soft rock |
0.45–0.55 | Meh / Neutral | 0.4–0.6 | 0.4–0.6 | Chillhop, lo-fi, mellow R&B |
0.55–0.70 | Reflective | 0.5–0.7 | 0.3–0.5 | Indie, dreamy pop |
0.70–0.85 | Joyful / Light | 0.7–0.9 | 0.6–0.8 | Upbeat pop, funky chill |
0.85–1.00 | Excited / Euphoric | 0.9–1.0 | 0.8–1.0 | High energy, dance/pop/EDM |
Sample playlists from 0.3, 0.45, 0.65, 0.75 mood ranges.
…And MoodMate’s core screens were born! The app begins with a “system” color theme, but users can explore other fun palettes in the settings tab. The selected mood color dynamically becomes the playlist UI theme — making each mood log feel distinct, memorable, and shareable. Over time, this creates a colorful, emotion-based archive of personal soundtracks.
This mood-color system also opens up creative monetization opportunities — seasonal or artist-inspired themes as in-app purchases.🎁 Imagine a “Summer Glow🌞” palette or a “Lo-Fi Night Mode🌚” limited edition drop. Yep, I'm dreaming big (but grounded).
Users can also toggle dark mode for a more immersive experience, especially during nighttime listening.
Up Next:
More Hi-Fi Screen 📱 Flow Designs
The initial sketches were just the first tunes. I’ll be orchestrating Hi-Fi screens, polish flows, prototype interactions, and user testing to validate interaction patterns. More features — like lyrics view and sharing — are in the pipeline. There’s still so much to explore — and I can’t wait to keep refining MoodMate until it hits just the right note.