Project Overview
Objective
Help restaurants manage orders efficiently and give diners a smooth pickup experience, while boosting GMV for the online ordering platform.
Challenges
Create a consistent journey across diners, staff, and owners that works for diverse restaurant models, while embedding scheduled ordering into an existing real-time system without disrupting user habits.
Contributions
Delivered MVP in 4 months through research-to-launch design with PMs and engineers. Integrated user interviews and behavior metrics to map cross-role journeys and shape product strategy.
Outcome
53% adoption in the first month; 44% of new subscribers enabled the feature. Reduced staff workload and improved diner satisfaction.
The Problem
Restaurants Lost Sales and Made Errors Without Pre-Ordering
iCHEF served 12,000+ restaurants across Asia, but its online ordering system only supported ASAP orders. We received 100+ monthly merchant requests for scheduled pickup, as ASAP-only orders caused manual workarounds, errors, and incorrect performance metrics.
Online Ordering Pain Points
❌ Pickup time entered manually
❌ No way to schedule future orders
❌ Higher risk of missed or disputed orders
Kitchen Workflow Issues
❌ Duplicate prep times triggered meal errors
❌ Wrong sales dates led to misleading performance data
Business Outcome
53% Adoption in the First Month, Scaling to 16K+ Restaurants and 2M+ Consumers
Adoption (First Month)
+53%
GMV (First Month)
+1%
We assumed pre-ordering would drive GMV and subscriber growth. and the result shows two insights:
Strong demand & growth — In the first month, 53% of existing restaurants adopted the feature, and 43% of new subscribers enabled it.
Shift in user behavior — Most orders moved from ASAP (immediate pickup) to Scheduled (pre-booked pickup time), validating product–market fit and future monetization.
Design Process
Key Challenge
Handled Cross-Role Flows and Time Logic Integration at Scale
❶ 4 distinct user types with different workflows.
❷ A new time logic for scheduled pickups—without breaking existing operations.
Design Plan
Clarifying the Feature Scope Through System Mapping
Based on the brief, I created a directional design and service plan to align business goals with early user journeys—clarifying product intent and reducing cross-functional misalignment.
In parallel, I scoped a research framework to gather behavioral, workflow, and system data—building domain context to guide MVP scope and UX prioritization across roles and channels.
Initial Research
Effective Research Methods for Different User Facings, Supporting Our First-Hand Strategy and Enhancing Project Efficiency
Restaurants
Method: Interviews & Feedback
84 feedback analyses and 8 interviews revealed restaurant contexts and user motivations
Insights
Restaurants fear missed orders and lost revenue as customers often place orders during off hours
Manual work leads to more mistakes and staff overload
Pickup timing varies by operating model, requiring flexible configuration
Diners
Method: Benchmarking
I benchmarked 4+ local delivery and pickup platforms like UberEats, food panda and etc to speed up decisions on this foundational feature.
Insights
Used familiar ux patterns to clarify pickup time and reduce errors
Product System
Method: System Function Mapping
I led a system audit to map time-related dependencies, ensuring new features didn’t break existing logic.
Insights
Sunsetted the existing mechanism—"10-minutes order auto-cancel" to prevent order loss and enable long-term scheduling
Strategy | Spec Prioritization
Clarified Scope and What We Focused On Through Cross-Functional Strategy Mapping
01 | Find High Impact UX through Business Goal Breakdown
Translated business goals and user insights into high-impact UX flows to define a lean, focused strategy in alignment with PMs. Later scaled this framework across the team to drive alignment and accelerate decision-making.
02 | Specs Feasibility Filters For First Realease
We prioritized MVP specs through a feasibility discussion grounded in user pain points and supported by competitive benchmarks—helping teams align early and avoid over-design.
Solution | Merchant Plaform 01
Faster Setup with One-Click Import Based on User Insights
"Manual setup and complex interaction slowed activation."
The original setup flow was overly manual and confusing—restaurant owners had to input every time slot from scratch, delaying activation and risking missed orders.
To work within budget, I skipped a full UI redesign and proposed a low-effort, high-impact fix: a one-click “Copy from Business Hours” button based on observed user behavior. This solution aligned with existing workflows, improved speed and consistency, required minimal engineering, and was later reinforced through onboarding support.
Prevented Missed Orders with Smart Scheduling Logic
"Gap in logic risked missed orders and revenue."
Some restaurants only accepted next-day scheduled orders and closed on weekends—causing pickup gaps when customers ordered on Saturdays. To address this, I designed smart scheduling logic to skip invalid dates and suggest the next valid pickup day. I validated it across common restaurant types to ensure robustness and operational fit, reducing revenue loss without disrupting user flow.
01 | Smart Scheduling Scenario
If a restaurant closes on weekends and accepts scheduled orders one day in advance,
What should the system suggest when someone orders on Saturday?
✅ Next valid pickup day
❌ No pickup option
📌 If pickup time options strictly follow the physical calendar day, it may result in no available slots being shown—creating a risk of missed sales.
02 | Quick Validation & Team Alignment
Validated the logic across common restaurant types to reduce edge cases and engineering risk—and used it as a shared reference to align logic understanding across teams.
Solution | POS 01
Compact POS Layout for Faster Staff Decisions |
"Original POS layout didn’t match how staff actually work—causing delays and confusion."
POS is mission-critical in fast-paced Asian restaurants, where staff must act within seconds. The original layout misaligned with real workflows, slowing decisions. Grounded in usability testing, I redesigned the layout to clarify order hierarchy, pickup time, and pricing—while preserving familiar button positions to reduce cognitive load and support smooth adoption.
01 | Tuned for Speed
User tests confirmed staff relied on the existing layout for fast scanning—so I optimized key details without disrupting the current layout and flow.
❶ Preserve Button Position
Keep layout familiar.
❷ Highlight Pickup Time & Payment Status
Improve visibility for faster decisions.
❸ Enable Swipe Interaction
Allow seamless order browsing.
02 | Usability-Driven Pivot
Early usability testing showed my first design was too complex. Real usage habits helped me pivot to a cleaner, more intuitive layout.
Solution | POS 02
Workflow-Aware Flexibility with Print-Later Button
"Not every restaurant works the same way."
Based on research, some restaurants confirmed large meal orders only after checking with the kitchen, while others skipped the “Accept (Print Later)” button due to mismatched printing habits. I retained the button with a disable option to support flexible workflows—balancing visibility and control without disrupting operations.
01 | Accept Button Strategy
Originally, I considered revising the logic of the “Accept” button to better match its dual action—accepting and printing—since the label only says “Accept.”
However, usability tests showed that existing users had already internalized the current behavior. Changing it would risk confusion and disrupt well-established workflows.
To avoid unnecessary friction, I preserved the original logic and introduced the “Print Later” option to support diverse restaurant habits without forcing retraining.
Accept = confirm & print now
Accept (Print Later) = confirm & defer printing
02 | Strategy for Diverse Workflows
Originally, I considered only showing the “Accept (Print Later)” button for scheduled orders, based on user interviews. However, after evaluating the logic complexity and engineering overhead, I decided to apply it to all order types for maintainability.
To support different store habits, I designed a simple configuration with three clear options—based on observed patterns.
This approach balanced onboarding visibility with long-term flexibility—without introducing unnecessary logic burden for the team.
Clarified Time Logic to Reduce Pickup Errors
"Pickup time logic did not match the user mental modal."
Customers were confused when scheduled orders appeared earlier than ASAP due to flawed pickup-time logic—ASAP was based on the store’s accept time, not the order time. To fix this, I redesigned the logic to reflect real-time behavior from the user’s perspective and used text-based formats and storyboards to align teams across edge cases. This improved customer trust and ensured stakeholders clearly understood time logic without disrupting store flow.
01 | Cross-Cultural Time Format
✅ Text Date: December 9
❌ Numeric Date: 12/09
Different cultures interpret numeric dates differently—e.g., “12/09” could mean December 9 or September 12. To avoid this ambiguity, I adopted a text-based time format using “Today,” “Tomorrow,” or “December 9” to improve global readability and reduce the risk of misunderstanding in multilingual contexts.
02 | Storyboard to Align Time Logic
To enforce the logic that ASAP must always be earlier than scheduled pickup, I created storyboards showing how users encounter pickup options in real-world flows.
This helped clarify logic dependencies and synced cross-functional teams around expected system behavior.
03 | Pickup Logic Redesign
Before
ASAP Order Pickup Time=
Order Accept Time + Meal Prep Time
After
ASAP Order Pickup Time=
Order Placement Time + Meal Prep Time
Redesigned ASAP pickup logic to reflect real-time behavior and reduce drop-offs. Previously, the pickup time was based on when staff accepted the order, which could lag behind scheduled pickups—confusing users and increasing cart abandonment.
I shifted the logic to calculate pickup time from the moment of order placement, better aligning with user expectations and ensuring ASAP orders always appear sooner than scheduled ones. This change was validated by behavioral data showing that 98% of stores confirm orders within 3 minutes, minimizing the risk of late pickups.
Pilot Test
Realigned Navigation to User Mental Models, Driving 71% Daily Activation Growth
Before
💔 Hard to find — took 5 steps to access, leading to low feature activation.
After
✅ Moved to main menu with clearer label — boosted discoverability and enabled future scale.
"Pilot testing showed most users missed the feature, reducing activation."
The feature was buried under “Order Settings,” so most users missed it. I moved it to the main menu with a clearer label—where people naturally look and where future delivery flows would scale. Daily activations jumped 71%, with zero engineering effort.
Cross-Function Collaboration
A Clear Design Doc, Time-Logic Handoff, and Workflow Optimization Accelerated Teamwork
I led the illustration of a timeline to align order touch-points across PM, engineering, and QA teams—preventing misaligned expectations and accelerating QA coverage.
❶ Concise Design Documentation
Simplified docs to improve team alignment and speed up development.
❷ Unified Time Format
Reduced inconsistency by standardizing time formats across 5 teams.
❸ Streamlined UX Writing Process
Partnered with FE & QA to resolve translation gaps and define workflow rules—saving 1 week of dev-QA handoff time.
Leadership & Mentorship
Mentorship that Scales Design & People
I view mentorship as a collaborative and tailored process. With our junior designer, I started by understanding his interests and strengths—particularly his curiosity about user research. I invited him to co-plan and run an interview session, where he led documentation and contributed to early user insights.
As we moved forward, we co-broke down specs into modular flows. I assessed task complexity and worked with him to scope assignments that aligned with both his growth and project safety—delegating non-critical tasks while shielding core business logic. Throughout, I provided hands-on feedback, reviewed design decisions regularly, and built a space for thoughtful learning and ownership.
My Learning