The Problem
Millennials often face an exhausting daily decision: What’s for dinner?
I initially assumed the issue was a lack of appealing options.
But through user interviews, I learned the opposite was true—people felt overwhelmed by too many choices, not underwhelmed by too few.
“Even if I see something I like, I keep wondering if there’s something better.”
This reframed the core challenge:
How might we reduce mental load and create a more confident, fun dinner selection experience?
Research & Early Concepts
Understanding the millennial audience was key. They’re mobile-first, time-constrained, and culturally fluent in apps like Spotify and Apple Music.
So I modeled the initial app experience after music streaming platforms:
🎵 Inspired by Music Streaming UX:
Save/skip recipes like songs
A quiz-based onboarding to build a taste profile from Day One
Curated sections like New For You, Trending Now, and Browse By Mood
This concept tapped into something familiar and enjoyable—transforming a mundane decision into a moment of delight.
Designing for Choice Confidence
The app was built to simplify decision-making while providing personalized suggestions, with a strong focus on prioritizing features that deliver immediate user value. Using the MoSCoW method, we identified the "Must Have" features necessary for the MVP to effectively reduce decision fatigue, while flagging other features as "Should Have" or "Could Have" for future iterations.
“This or That” onboarding (Must Have): A fun, quick way for users to define food preferences right from the start, enabling the app to tailor suggestions from day one.
Personalized meal suggestions (Must Have): Only a few curated meals are presented based on the user's preferences, significantly reducing choice overload and supporting confident decision-making.
Mood-based filtering (Should Have): Users can select a mood or vibe (e.g., "Comforting" or "Healthy") to guide the app in serving three relevant, personalized meal options—adding emotional relevance to the browsing experience.
These prioritized features ensured a balance of personalization and simplicity, allowing us to meet the core user need—“what should I eat for dinner?”—without overwhelming them. Lower-priority features like cravings-based suggestions or music app-style browsing were tagged as Could Haves, to be considered in future releases after validating the MVP.
Feature Prioritization Summary (MoSCoW Method)
The Home Screen Pivot: When Familiarity Fails
What I Initially Designed:
A music app-style dashboard with multiple content blocks and wide browsing options.
What Usability Testing Revealed:
“This feels like another endless feed.”
“I’m more stressed after looking through it.”
Users wanted less, not more.
So I redesigned the home screen to focus on:
Top 3 meal suggestions personalized to the user’s mood and profile
Optional entry points to Explore All Meals or Trending Now—but never overwhelming
This update dramatically improved usability scores and aligned the interface with the app’s mission: reducing decision fatigue.
Outcomes
94% of users reported faster, easier decision-making
Users described the experience as “stress-free,” “fun,” and “actually helpful”
Reframing the home screen led to stronger engagement and clarity of purpose
Potential AI/ML Enhancements
To further enhance the user experience, AI/ML could be integrated into the app for more personalized and efficient decision-making:
Smart Meal Recommendations: Using machine learning, the app could learn from user preferences and behavioral patterns over time to recommend meals that are increasingly aligned with their tastes, dietary restrictions, and even time constraints.
Predictive Suggestions: AI could predict the user's meal needs based on their past behavior, adjusting recommendations based on factors like seasonality or how often they’ve eaten similar meals.
These AI/ML-powered features would elevate the app from simply helping users make decisions to becoming an intelligent, adaptive assistant for everyday meal planning.
Reflections
This project demonstrated my ability to:
Challenge assumptions with research, and pivot direction when the data calls for it
Design culturally intuitive experiences, drawing on parallels with everyday tools
Use testing not just to validate, but to evolve ideas
Lead the full end-to-end design cycle independently, from insights to interface