Methodologies

• Compartive
• Cualitative

User Interviews

To understand user needs, I conducted interviews with individuals who follow specific diets like keto, vegan, dairy-free, or gluten-free. They shared a common pain point: current food delivery apps make it hard to find meals that match their restrictions.

Affinity Map

User interviews revealed a strong demand for personalization in food delivery. Users want quick ways to apply dietary filters, confidence in ingredient transparency, and the ability to save preferences to avoid repeating the same steps.

Competitor Analysis

I paired these insights with a detailed competitor analysis, reviewing platforms like DoorDash, Grubhub, and Caviar. While these apps offer a variety of features, none fully addressed the need for easy, diet-specific filtering.


Doordash
DoorDash holds the largest market share in the U.S. and appeals to users looking for food and grocery delivery in urban and suburban areas.
Strengths: Versatile services, strong value with DashPass, expanded delivery options.
Weaknesses: High fees on small orders and limited premium dining options.
Subscription: DashPass – $9.99/month.

Grubhub
Grubhub is known for its strong partnerships with local restaurants, especially in urban markets.
Strengths: Good value for frequent users, user-friendly interface, multiple payment options.
Weaknesses: Limited coverage outside cities and inconsistent customer service.
Subscription: Grubhub+ - $9.99/month

Caviar
Caviar focuses on premium, gourmet dining experiences, targeting urban foodies who prefer quality over convenience.
Strengths: Curated, high-end restaurant options, visually rich interface..
Weaknesses: Limited availability, higher fees, no subscription plan.

User Persona

Project Goal

I created this goal alignment map to support the design of a dietary filter feature for Uber Eats. Users want an easy, accurate way to find meals that match their dietary needs, while Uber Eats aims to boost engagement and reach health-conscious audiences. The shared goal is a smooth, personalized ordering experience that saves time and builds loyalty. Technical considerations like filter scalability, clear labeling, and data privacy were key to ensuring a seamless and trustworthy user experience.

Problem Statement

Point of View Statement:

Health-conscious users need a faster, more personalized way to find meals matching their dietary needs on Uber Eats. Without dietary filters and clear labeling, it's challenging for them to quickly discover suitable options, reducing their engagement and loyalty.

How Might We Statement:

How might we create a personalized dietary filter that streamlines the meal search experience, enabling health-conscious users to easily find meals that meet their specific dietary preferences?

User Flow

This user flow illustrates the process of adding a dietary restriction filter to a food delivery app. It covers both quick filtering for immediate searches and personalized dietary profile setup for faster future orders. The flow ensures users can easily find meals that meet their dietary needs, make confident selections, and proceed smoothly to checkout.


View User Flow

Wireframe Low Fidelity

To improve the experience for users with dietary restrictions, I introduced several key features in the wireframes:

• Homepage Dropdown: A scrollable dropdown menu for dietary filters at the top of the homepage makes it easy to access common options like keto or vegan.
• Quick Filter Labels: Chips below the search bar allow users to apply filters instantly with one tap.
• Search Bar Integration: Dietary filters are also available in the search bar for better visibility and quick access.
• Profile Preferences: Users can save detailed dietary preferences in their profile for a more personalized experience.
• Toggle for Saved Preferences: A toggle on the homepage lets users quickly apply or remove saved filters without reselecting each time.

These features aim to make filtering faster, easier, and more personal.

High Fidelity and Prototype

During the high-fidelity and prototyping phase, I designed clean, user-friendly screens that integrated the new dietary filter seamlessly into the Uber Eats interface. The prototype included key flows such as setting up dietary preferences, filtering meals, and confirming whether a dish matches user needs.

Pain Points

• Filter Visibility: Some users didn't notice the dietary filter right away; it wasn't placed in an intuitive location.
• Overwhelming Filter Options: A few users felt overwhelmed by too many choices in the filter menu.
• Unclear Labels: Filter categories lacked brief descriptions, making it harder to understand some options.
• Lack of Confirmation: Users wanted confirmation messages to know their preferences were saved or if a meal matched their dietary needs.
• Browsing Preference: Some users preferred to explore restaurants first and apply filters later, suggesting the need for more flexible entry points.

Effort-Impact Analysis

Based on the user testing interviews, this table summarizes and prioritizes feature updates aimed at improving dietary filtering and meal discovery. The focus is on enhancing clarity, usability, and user confidence when selecting meals.

Iterations

Added a “More Options” button to simplify the filter view and reduce visual clutter.



Added short explanations to filter options to help users understand them at a glance.



Introduced a pop-up message to confirm that dietary preferences were saved successfully, giving users reassurance.



Made the dietary filter easier to find by moving it to the top of the homepage

Final Project and protoype

This high-fidelity prototype showcases the proposed dietary restriction filter integrated into the Uber Eats app. It includes key screens for onboarding, dietary preference selection, meal filtering, and personalized profile settings—designed to enhance the user experience with a clean interface, intuitive navigation, and faster access to diet-friendly meals.


View Final Prototype

Challenge Faced:

One of the biggest challenges was balancing simplicity with the need for detailed dietary information. Users wanted quick filtering, but also needed confidence that a meal truly matched their restrictions. Designing an experience that was both fast and informative required careful prioritization of layout, language, and visual hierarchy.

Lessons Learned:

I learned how vital clarity and trust are when designing for people with specific needs. Even small moments—like confirming that a meal meets a dietary filter—can make or break the user experience. Personalization and reassurance matter just as much as functionality.

What I'd Do Differently:

If I had more time, I would explore ways to integrate more dynamic personalization, such as AI-based suggestions or auto-filtering based on past orders.

What I'm Most Proud Of:

I'm proud of designing a feature that supports real, everyday needs. This solution doesn't just make ordering easier—it helps users stay consistent with their health goals and feel seen in a platform that wasn't originally built for them. Creating that kind of impact through design is what I love most.