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

KEY FEATURES

Experience the full TasteBud user journey from swiping to booking

Swipe & Match

See how users discover restaurants and match with friends

Social Features

Watch group planning and chat interactions in action

Quick Booking

Experience seamless table reservation flow

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

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.

INFORMATION ONBOARDING

Organizing app structure and navigation flows for intuitive user experience

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

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

PHASE 1 (Q2 2025)

• Beta launch with core matching features
• A/B test different swipe mechanics
• Gather quantitative usage data
• Refine recommendation engine

PHASE 2 (Q3 2025)

• Implement advanced group features
• Add restaurant badge system
• Introduce dietary preferences filters
• Partner with 50+ restaurants

PHASE 3 (Q4 2025)

• AI-powered personalization
• Social sharing features
• Loyalty program integration
• Multi-city expansion