MunchRun - Food Delivery App Design
MunchRun is a 4-month end-to-end redesign of a hyperlocal food delivery app serving urban neighborhoods across 12 cities.
Role
Duration
Tools
Team
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Project Overview
MunchRun is a food delivery platform serving 50,000+ active users across 12 cities, connecting customers with 1,800+ local restaurants. The existing app had grown organically over 3 years, resulting in an inconsistent experience that was hurting retention and order completion rates.
I joined as the sole Product Designer to rebuild the end-to-end ordering experience, from restaurant discovery to post-delivery review, with a focus on speed, clarity, and delight.
50K+
Active Users
1800+
Restaurant Partners
12
Cities Served
Problem Statement
MunchRun's app had a 54% cart abandonment rate and a declining 3.1-star App Store rating. Users were dropping off primarily during the customization and checkout flow due to confusing UI patterns and a lack of real-time feedback.
62% of drop-offs occurred during the item customization step
Users spent an average of 6.8 minutes placing a single order
45% of support tickets were related to order tracking confusion
Repeat order rate had declined from 61% to 44% over 8 months
New user retention at day-7 was at a low 22%
Research & Discovery
I ran a 3-week discovery phase combining in-app analytics, user interviews, and a competitive audit of 6 major delivery platforms.
18
User Interviews
3200
Session Recordings Analyzed
6
Competitor Audits
Key Findings
74% of users found the customization screen overwhelming and unclear
Users expected real-time delivery tracking but received only static ETAs
Repeat customers wanted a faster reorder path but couldn't find one
The restaurant discovery page lacked meaningful filtering, frustrating new users
Ideation
I ran a 2-day design sprint with the product and engineering team. Using how-might-we questions, affinity mapping, and storyboarding, we generated 90+ concepts and narrowed them down to 2 strong directions.
We chose "QuickFlow" - a concept built around reducing decision fatigue by surfacing smart defaults, saved preferences, and a one-tap reorder system at every touchpoint.
User Flow
I mapped three primary journeys: new user discovery, repeat order, and live order tracking. The main goal was cutting the average order time from 6.8 minutes to under 3 minutes.
Discovery to cart - personalized feed with smart filters and restaurant cards showing live wait times
Customization to checkout - single-screen item builder with inline upsells and saved preferences
Post-order tracking - live map view with push notifications and estimated arrival countdown
Wireframing
I designed wireframes for 22 key screens, moving from rough sketches to mid-fidelity layouts across 3 iterations. Each round was tested with 6 participants via hallway usability sessions.
The final wireframes achieved an 88% task completion rate, compared to 51% on the original app.
The final wireframes achieved an 88% task completion rate, compared to 51% on the original app.
Visual Design
The visual direction needed to feel energetic and appetizing while remaining fast and functional. I developed a warm, high-contrast design system using a saffron and charcoal palette with rounded components to keep the tone friendly and approachable.
Prototyping & Testing
I built a high-fidelity prototype in Figma and ran 4 rounds of moderated and unmoderated testing with 28 participants using Useberry for remote sessions.
28 Test Participants 4 Testing Rounds 91% Task Success Rate
"Ordering feels so much faster now. I didn't have to think at all - it just guided me through."
Iterations & Refinements
Iteration 1: Collapsed the 4-step customization flow into a single bottom sheet. Result: 28% reduction in time-on-screen during item selection.
Iteration 2: Added a persistent "Reorder" shortcut on the home screen for returning users. Result: 40% increase in repeat order rate during beta testing.
Final Design
The final design, "QuickFlow," delivers a fast, frictionless ordering experience with a strong visual identity. Key features include:
One-tap reorder with saved preferences and last delivery address
Smart restaurant discovery with real-time wait time indicators
Single-screen item customization with inline add-ons
Live order tracking with animated map and delivery updates
Personalized home feed based on order history and time of day
Full accessibility compliance with WCAG AA contrast and touch target standards
Impact
54%
Reduction in cart abandonment
4.6
App Rating
40%
Increased Order
Lessons Learned
In food apps, speed and confidence matter more than visual richness
Reorder flows are underinvested in most delivery apps despite being the highest-frequency use case
Motion and micro-interactions significantly improve perceived performance
Testing with real restaurant menus (not dummy content) exposed critical edge cases early
Next Steps
Introduce AI-powered meal suggestions based on time of day and past orders
Build a group ordering feature for office and social use cases
Expand the design system into a shared component library with the engineering team
Run a 90-day retention study to measure long-term behavioral impact
