UberEats
Designing an intuitive dining choice assistant to streamline mealtime decisions
PAUSE
This case study is currently being reconstructed
Feel free to look around at the old version, or take a look at a completed case study!
Motivation
That was me…
As a college junior juggling a full-time internship and online classes this past summer, my 30-minute lunch breaks left little time to prepare meals. Uber Eats became my go-to, however, I often found myself spending an excessive amount of time deciding where to order from—time that I simply did not have. After countless accumulated hours of scrolling, abandoned carts, and rushed lunches, I started to wonder how UberEats could approach this problem. This led me to ask:
How might Uber Eats create a more intuitive ordering experience for overwhelmed users?
Research
Keeping these core questions in mind, I started my research process.
I began by looking at existing studies and data that would bring me closer to a potential solution, primarily focusing on trying to identify when users feel most overwhelmed on the app and what causes it.
These statistics highlight the common struggles users face with decision-making and choice paralysis in food delivery apps:
Cart Abandoment
of orders are abandoned across food delivery services, highlighting indecision during the ordering process.
Decision Fatigue
of individuals feel anxious due to the constant need to make decisions in a digital environment
Cognitive Overload
experience menu anxiety, feeling overwhelmed by the extensive choices available when ordering food online
These interviews revealed key pain points, with one frequently mentioned issue being the lengthy decision-making process when choosing what to order.
Key Takeaways
Simplification
Users need simplified, more intuitive decision-making tools to avoid menu fatigue and discover diverse dining options efficiently.
Familiarity Bias
Users often repeat the same orders due to lack of incentives to try new options, stunting growth opportunities for new restaurants.
Time Efficiency
Users often avoid using UberEats during time constraints, aware that they might spend excessive time making a decision.
The Problem
The Solution
To directly address the challenges faced by UberEats users, I developed a solution within the UberEats app called UberMatch.
UberMatch is a meal-pairing service within UberEats designed to make online ordering both seamless and time efficient for busy users by offering personalized matches based on a preference quizzes and data from past order data. With discount incentives for matched restaurants, UberMatch not only simplifies and diversifies your dining experience, but also provides merchants with opportunities to boost sales and customer engagement.
Design Process
My sketching sessions were focused on ideation, with a goal to prompt productive development by exploring creative approaches to the problem at hand. To guide my designs, I followed three key principles:
🔍 Ease the decision making process
⭐ Incentive users to engage with this feature
📱 Provide a straightforward and intuitive user experience
During this ideation phase, I explored a variety of approaches, evaluating their feasibility and alignment with user needs:
🛠️ Pre-Built Menu Bundles
Grouping popular items into curated bundles to make ordering faster.
Pros:
• Quick to implement and simple for users to navigate.
• Reduces cognitive load by presenting curated options.
Cons:
• Limited personalization; may not appeal to users with specific dietary preferences.
• Similar to existing features on UberEats.
🎮 Gamified Decision Flow
Adding fun, interactive elements to encourage exploration of new options.
Pros:
• Increases user engagement through interactive elements.
• Encourages users to explore new food options.
Cons:
• Overcomplicates the interface.
• Feels out of left field, very random / unnecessary
📦 Subscription-Based Ordering
Suggesting recurring meal plans to reduce decision fatigue.
Pros:
• Eliminates daily decision-making entirely for frequent users.
Cons:
• Requires significant amount of upfront commitment from users.
• Lacks flexibility / spontaneity because of the scheduled meal plan
📝 Quiz-Based Recommendations
Tailored system that offers personalized suggestions based on user preferences.
Pros:
• Highly personalized, addressing individual user preferences.
• Encourages users to explore options they may not have considered.
• Maintains engagement
Cons:
• Requires users to answer a few initial questions, adding a slight delay
• System may take time to learn to make more accurate suggestions
After evaluating these options, I selected the quiz-based recommendation system as the most balanced solution. It provided a personalized, intuitive, and engaging experience that directly addressed the issue of decision fatigue & time constraints.
With this, I was able to start designing. To freely explore different variations, I resorted to good old pencil and paper to sketch out initial concepts.
Low Fidelity Wireframes: Bringing my sketches to life
Although I was under a tight time constraint for this design challenge, I prioritized setting aside time for iteration. Iteration was essential to the design process, enabling me to refine ideas and explore solutions that enhanced the overall user experience of UberMatch.
The initial pop-up design lacked clarity and failed to effectively guide users toward the call to action. With new features, it's easy for users to overlook subtle prompts while scrolling. To address this, I introduced stronger visual hierarchy paired with a clear and compelling call to action.
Early drafts of the Match page was cluttered, and didn't follow UberEat's design system, causing it to feel disconnected from the rest of the app. The lack of design consistency decreased the reliability of the feature. This lack of design consistency reduced the perceived reliability of the feature. To combat this I :
Developed components that align with the Uber Eats Design System, ensuring visual and functional consistency across the platform.
Simplified and organized the interface to enhance intuitiveness, resulting in a more seamless and user-friendly experience.
UberMatch leverages personalized quiz data, daily meal recommendations, and incentivized match points to streamline the food ordering process. It helps users discover meals they’ll love while earning rewards, creating a more engaging and rewarding experience.
Introducing New Feature to Users
Home Screen Promotion Banner
Eye-catching promotional banner on the home screen, focuses the user's attention to the new feature. By highlighting a discount on first orders made through Match, the banner motivates users to explore and engage with the new feature.
Getting to Know Your Preferences!
Match Preference Quiz
To provide a tailored experience, first-time users are invited to take a quick and easy meal preference quiz. The data collected helps UberMatch generate personalized daily meal recommendations that cater to individual tastes and preferences.
Key Features:
Dynamic Updates: Retake the quiz anytime to refine preferences.
Flexibility: Generate matches using most recent quiz data.
Enhanced Personalization: Recommendations evolve with changing preferences, ensuring an engaging and relevant experience.
This adaptive system streamlines decision-making by reducing cognitive overload, offering users a single curated match each day.
Tracking Points & Promotions
Daily Match Page
Each time users order from their Daily Match, they earn points based on their spending. Upon reaching specific point milestones, users can redeem their points for exclusive promotions.
This system not only incentivizes users to engage with the feature by offering tangible rewards but also encourages them to explore new dining options while saving time on decision-making. By simplifying choices and adding value, the feature enhances user satisfaction and keeps the experience fresh and exciting
