Company
iCHEF
Time
3 months (From research to launch)
Team
2 Designers, 1 PM, 9 Engineers (FE, BE, iOS, QE)
Role
Sr. Product Designer (Led end-to-end UXUI, user research, product definition, and cross-functional execution)
I led the end-to-end UX for scheduled orders, aligning 4 roles and rebuilding time logic to ensure clarity and accuracy. This enabled restaurants to accept online scheduled-orders and reduce staff burden—resulting in 53% adoption within the first month.
My Impacts
Enabled 12,000+ Restaurants
53% Adoption in One Month
71% Boost in Daily Activation
2+ Weeks Saved in R&D
The Problem
No Scheduled Pickup: Top User Need, High Risk of Order Loss
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.
Business Goal
Increased Order Volume to Drive GMV and Subscription Upgrades
Our hypothesis was that adding the top-requested scheduled-order feature would increase order volume, which in turn could lead more merchants to upgrade their POS subscription plans and drive GMV. This feature also served as a foundational capability to attract new subscribers.
Activation Rate (First Month)
+53%
Activated by 53% of restaurants in month one, with 43% from new subscribers—validating strong demand and the feature’s role in driving subscriber growth.
GMV (First Month)
+1%
Though GMV rose just 1% in the first month, usage data showed a clear shift from ASAP to scheduled orders—indicating long-term operational value and confirming product–market fit for future monetization.
Main Challenge
Handled Cross-Role Flows and Time Logic Integration at Scale
The main challenge was designing for 4 distinct user types with different workflows, while introducing a new time logic for scheduled pickups—without breaking existing operations.
Design Process
Design Plan
Shaped Problem Scope and Aligned Team Through Strategic Mapping
After receiving the brief, I created a Design Plan and Service Plan to align business goals, user flows, and technical constraints—clarifying scope and reducing miscommunication. In parallel, I ran targeted research on system logic and restaurant workflows, building the domain context needed for high-impact, cross-role design decisions.
Initial Research Stage
Framed the Right Problem Through System-Level Discovery
I led research across restaurants, customers, and system behavior to uncover mental models and workflow needs. These insights shaped our strategy and helped prioritize timing logic and role-specific flows.
Initial Research | Restaurant
01 | Why This Approach
Strategic Research to Uncover Restaurant Behaviors and Mental Models
I led a layered research process to frame the problem space and surface real-world constraints. By combining user feedback, interviews, and behavioral data, we uncovered operational pain points and clarified priorities. This approach reduced blind spots and focused the team on high-impact features.
Step 1 | Feedback Analysis
Reviewed 84 feedback entries to identify recurring pain points and shape targeted interview questions.
Step 2 | Interview In Depth
Conducted 8 in-depth interviews and observations to uncover workflows, motivations, and edge cases across restaurant types.
Step 3 | Behavioral Data Analysis
Analyzed usage data to validate patterns and reveal system-level constraints not surfaced in interviews.
02 | Key Insights
❶ Restaurants Fear Missed Orders, Losing Revenue Opportunities
Restaurant Motivation
Customers often place orders during off-peak hours, especially at night—highlighting risks of missed revenue and operational gaps. By understanding how restaurants make scheduling decisions, I identified where design could reduce workload and increase revenue. This insight led me to address cases where restrictive pickup settings blocked orders, informing spec updates that improved conversion without disrupting operations.
❷ Manual Workflows Increase Risk and Staff Overload
Restaurant Workflow Journey & Opportunity
Current workflows rely heavily on individual staff memory and manual coordination, making operations prone to miscommunication and prep errors. By mapping the end-to-end store journey, we pinpointed breakdowns in flow and identified system-level opportunities to streamline task ownership and reduce variability.
❸ Pickup Timing Varies by Operating Model, Requiring Flexible Configuration
Typical Restaurant Operating Models
Business hours and operating models (e.g., split shifts, single shifts, 24/7) directly shape how pickups are managed. To reflect real-world workflows, the system needs flexible pickup logic—anchored to business hours and auto-adjusting during closures—to ease staff load and prevent missed pickups.
Initial Research | Online Ordering
01 | Competitive Benchmarking
Grounding UX Decisions in Competitive Benchmarking to Drive Familiarity and Reduce Friction
To accelerate product decisions on this foundational feature, I conducted targeted benchmarking across regional leaders and adjacent platforms—given that scheduled ordering is a widely supported baseline capability.
Since most iCHEF subscribers are Taiwanese restaurants, I analyzed leading local platforms—Foodpanda, Uber Eats, Ocard, and Nidin—to identify proven UX patterns and scheduling logic. These insights grounded key specs in familiar mental models and helped reduce user friction and decision fatigue.
Selection Criteria
02 | Key Insights
Design Principle: Use Contextual Time Design to Reduce Friction and Improve Clarity
Benchmarking Highlights
I adopted best-in-class UX patterns for time display, store status, and pickup slot formatting. This included combining date + time + label (e.g., “Now” or “Tomorrow”), showing next availability for closed stores, and using concise microcopy to clarify status—all to reduce ordering errors and support fast decision-making.
Initial Research | Product System
01 | Why This Approach
Auditing System Dependencies to Prevent Conflicts and Scale Scheduled-Order Logic
To ensure the Scheduled Order feature didn’t conflict with existing logic, I conducted a system-level audit across all time-related dependencies. This approach ensured the system remained scalable and conflict-free as we layered new functionality.
Mapped system dependencies to eliminate logic conflicts and support scalable scheduling.
02 | What the Audit Uncovered
Identified a Logic Conflict Causing Order Loss—And Removed It to Enable Long-Term Scheduling
One major conflict surfaced: the system auto-cancelled any order not accepted within 10 minutes—a rule originally designed for ASAP orders. I first investigated the rationale behind this behavior, then mapped its technical implications, evaluated trade-offs, and surfaced relevant user feedback. Many operators reported losing valid orders during peak hours due to this timeout. I ran a risk assessment and led stakeholder discussions to propose retiring the rule. After aligning with engineering and operations, we successfully removed it—reducing order loss risk and enabling sustainable Scheduled Order support.
Strategy Stage
Defined Strategy That Balanced Business Goals With Real-World Constraints
I translated business goals into actionable design plans grounded in real-world constraints. This enabled early alignment with PMs and engineers on feasibility, scope, and priorities.
Strategy | Business to UX
Defined High-Impact UX Priorities Through Top-Down Strategy Mapping
To avoid scope creep and misalignment, I mapped business goals to UX priorities to identify high-impact flows. This shaped a focused MVP and, later as team lead, evolved into a reusable framework that improved team alignment and reduced design churn.
Transformed top-down goals into product decisions that improved outcomes without bloating scope.
Strategy | MVP Feasibility Planning
Evaluate Feasibility Early to Prevent Scope Creep
To avoid scope creep and wasted effort on low-impact specs, I created a prioritization framework to help the team assess user needs and technical feasibility early. By weighing core value, dev cost, and available workarounds, we focused only on what truly mattered—enabling faster, more aligned decisions.
Transformed top-down goals into product decisions that improved outcomes without bloating scope.
Strategy | Early Team Sync Up
Clarified MVP Scope Through Role-Based Scenario Mapping
To surface key scenarios across roles and system states, I mapped the end-to-end user journey to clarify scope and uncover edge cases. For example, I evaluated how the same feature impacted restaurants with or without scheduled orders and flagged POS upgrade needs. This enabled real-world prioritization and early focus on what mattered most.
Mapped use cases to reduce surprises and align MVP feasibility across teams.
Strategy | User Persona
Built Personas to Bridge Insights With Real User Behaviors and System Familiarity
To design with clearer guidance and less friction, I turned early research into practical personas focused on user familiarity, system constraints, and behavioral patterns. This helped me identify where guidance was needed and tailor flows to match each role’s mental model—improving clarity without overloading the UI.
Design Solution
Designed One Unified Experience Across Online and In-Store Operations
This solution unified online and in-store ordering by redesigning not just the interface but also the timing logic and role-based entry points. I led the redesign across operators, staff, and customers, reducing friction, improving clarity, and enabling scalable cross-channel operations.
Solution 01 | Navigation (Merchant Platform)
71% Growth in Daily Activation by Realigning Navigation with User Mental Models
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.
Solution 02 | Fast Setup (Merchant Plaform)
Faster Pickup Time Setup with Import Logic Button Based on User Insights
Manual Setup and Outdated UI 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 “Import 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.
Solution 03 | Smart Scheduling (Merchant Plaform)
Avoid Missed Orders by Smart Scheduling
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 store types to ensure robustness and operational fit, reducing revenue loss without disrupting user flow.
Confirmed fit across key restaurant types.
Example Scenario
A restaurant only opens on weekdays and allows scheduled orders one day in advance.
If a customer places an order on Saturday, what should the system show?
Solution 04 | Compact Layout (POS)
A Compact Interface for Quick Scanning and Fast Decisions
POS is the core tool in daily restaurant operations, especially in high-pressure environments like Asia where staff must act within seconds. The original layout was misaligned with real-world workflows, which slowed decision-making. I prioritized a full layout redesign over minor UI tweaks—clarifying order hierarchy, pickup time, and pricing to support faster, more confident actions. Grounded in usability insights, I preserved existing button positions to reduce cognitive load without disrupting habits—enabling smoother adoption and boosting in-store efficiency with minimal training.
Solution 05 | Print-Later Button (POS)
The “Accept (Print Later)” Button: Flexible for Diverse Store Operations
One Button, Two Workflows: Balancing Flexibility
Based on research, some restaurants skipped the “Accept (Print Later)” button due to print habits misalignment. I kept the button with a disable option to support flexible workflows—balancing visibility and control without disrupting operations.
01 | Design Highlight
❶ Keep the Flow Familiar
Added a “Print Later” button without changing existing logic—minimizing disruption for stores that confirm orders in under 10 seconds.
Accept = confirm & print now
Accept (Print Later) = defer printing
❷ Add Flexibility, Not Friction
Launched as default-on to improve discoverability during onboarding, with a simple opt-out for stores with different workflows.
Solution 06 | Time Format & Mechanism (Online Ordering)
Time Format and Mechanism Redesign to Enhance User Understanding
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.
Pilot Test Stage
Pilot Test is the key to the new feature, it aims to identify and minimize any major issues that could disrupt restaurant operations.
Pilot Test
Identify key issues before Launch with 4 Pioneering Restaurants
We collaborated with 4 restaurants that had requested the feature—each with distinct operational models—to help uncover real-world issues. Their feedback, including concerns about navigation and wording, guided us in refining the experience through further iteration.
Cross-Function Collaboration
Streamlined Cross-Functional Alignment to Speed Up Delivery
Illustrated timeline to align order touch-points across PM, engineering, and QA teams.
