TasteBud
A social food discovery app that makes choosing where to eat or drink as fun as swiping on Tinder and as seamless as booking on OpenTable. Users can swipe on restaurants, match with friends on shared favorites, chat directly in-app, and instantly see availability and make reservations — all in one place.
Team
UX Designer, Product Manager, UI Designer
Tools
Figma, Protopie
Role
Lead UX/UI Designer
Duration
6 Weeks
PROBLEM
Deciding where to eat with friends often turns into an endless loop of indecision, group chat chaos, and lost recommendations. Most food apps focus on reviews and ratings — not on the social experience of discovering and planning together.
SOLUTION
TasteBud is a social food discovery app that makes choosing where to eat or drink as fun as swiping on Tinder and as seamless as booking on OpenTable.
Swipe & Match
Playful restaurant discovery with friend matching
Social Planning
Chat and coordinate directly in-app
Instant Booking
See availability and reserve tables seamlessly
EMPATHY MAP — CHARLOTTE
Charlotte Wong
Age 26 / Dentist / Hong Kong
Think & Feel
- • "I love food, and I already use apps like OpenRice"
- • "Decision-making with friends should be easier"
- • "It'd be great to see what others like first"
Hear
- • "This place is trending right now"
- • "Let's just go somewhere nearby"
- • "I already went there last week"
- • Friends make quick decisions or lose interest
See
- • Endless restaurant lists and Instagram food photos
- • Cluttered apps mixing ads and non-food content
- • Friends' preferences scattered across platforms
Say & Do
- • Says "I'll just check OpenRice"
- • Saves restaurants but rarely revisits
- • Likes visually clean interfaces
Pain Points
- • Planning with friends takes too long
- • Apps feel cluttered and overwhelming
- • Hard to track mutual preferences
- • Social dining decisions are often one-sided
Gains / Needs
- • A socially connected food app
- • Clear, uncluttered UX
- • Smart friend-based recommendations
- • Feels rewarded for exploring new restaurants
USER PERSONAS
Charlotte Wong
Age 26 / Dentist / Hong Kong / Engaged
"It's normally such an individual decision — but TasteBud brings back the fun of looking together instead of deciding alone."
BACKGROUND
Busy dentist who loves exploring cafés. She's an "experienced foodie" familiar with restaurant apps but finds them cluttered and lacking social features.
GOALS
- Discover restaurants without clutter
- Make dining with friends interactive
- Track places clearly
FRUSTRATIONS
- Decision fatigue
- Too many disconnected platforms
- Planning feels lonely
MOTIVATIONS
- Sharing food experiences
- Visual, intuitive design
- Meaningful connections
Declan Park
Age 24 / Grad Student / NYC / New to City
"We usually just wander until we find something — but I wish I had an easier way to discover good places and remember them for later."
BACKGROUND
First-year Columbia master's student new to NYC. Makes spontaneous food decisions with friends. Wants to explore the city more intentionally but doesn't know where to start.
GOALS
- Discover local spots beyond tourist traps
- Make exploring more social and structured
- Keep track of places to try
FRUSTRATIONS
- Doesn't know where to start
- Last-minute group decisions
- Google Maps feels impersonal
MOTIVATIONS
- Social, visual exploration
- Personal recommendations
- Spontaneity with purpose
WIREFRAMES & USER JOURNEY
COMPETITIVE ANALYSIS
Understanding the competitive landscape and identifying opportunities for differentiation
FEATURE PRIORITIZATION
Ranking features based on user needs, technical feasibility, and business value
PRIORITIZATION FRAMEWORK
Features were evaluated using a matrix that balances user impact against development complexity. This helps the team focus on high-value, achievable wins for MVP launch.
HIGH PRIORITY (MUST-HAVE)
Core matching mechanics, basic profile setup, and restaurant browsing. These features directly address the primary user pain point.
MEDIUM PRIORITY (SHOULD-HAVE)
Group chat, booking integration, and saved lists. Important for retention but can be refined post-launch based on usage data.
LOW PRIORITY (NICE-TO-HAVE)
Advanced filters, social sharing, and gamification elements. These enhance the experience but aren't critical for initial validation.
KEY INSIGHT
By focusing on the "swipe to match" core loop first, we can validate the concept quickly while gathering data to inform which secondary features truly matter to users.
STYLESCAPE
Visual direction consolidated on a clean white background.
KEY FEATURES
Summary of core flows alongside wireframes
Swipe & Match
Discover restaurants and match with friends
Social Features
Plan and chat directly in-app
Quick Booking
See availability and reserve quickly
Design Assets
Logo
Typography
Imagery
Color
MICROINTERACTIONS
Microinteraction
TasteBud UI microinteraction detail animation
Ruby's Cafe Info (iPhone)
Detail view interaction on iPhone
Fixed Swipe
Refined swipe interaction for consistent feel
Spoon & Fork Animation
Playful motion to reinforce brand personality
KEY LEARNINGS & NEXT STEPS
WHAT WORKED WELL
SOCIAL-FIRST APPROACH
The swipe-and-match mechanic resonated strongly with users, making restaurant discovery feel collaborative rather than isolated. This validates the core concept of social food planning.
VISUAL SIMPLICITY
Users appreciated the clean, uncluttered interface compared to existing apps. The minimalist design helped reduce decision fatigue during the selection process.
INTEGRATED EXPERIENCE
Combining discovery, matching, chatting, and booking in one flow eliminated platform-switching frustration mentioned by both personas.
OPPORTUNITIES FOR GROWTH
RECOMMENDATION ALGORITHM
Future iterations need more sophisticated personalization based on past behavior, dietary restrictions, and neighborhood preferences to reduce swipe fatigue.
GROUP DYNAMICS
Testing revealed challenges when groups exceed 4 people. Need to explore different matching mechanics and voting systems for larger parties.
ONBOARDING EXPERIENCE
New users need more guidance on how matching works. Consider interactive tutorials or sample matches to demonstrate value immediately.
FUTURE DEVELOPMENT PRIORITIES
- • Beta launch with core matching features
- • A/B test different swipe mechanics
- • Gather quantitative usage data
- • Refine recommendation engine
- • Implement advanced group features
- • Add restaurant badge system
- • Introduce dietary preferences filters
- • Partner with 50+ restaurants
- • AI-powered personalization
- • Social sharing features
- • Loyalty program integration
- • Multi-city expansion