Pizza Hut - Store Admin
When Pizza Hut sunset its legacy store management platform, the U.S. business needed a new operational experience without disrupting day-to-day restaurant operations.
Working within an aggressive timeline, I designed foundational workflows that enabled franchise operators to manage store availability, fulfillment, inventory, notifications, and operational controls while extending Maverick, Pizza Hut International's existing design system, with new patterns for U.S. operations.
Impact
6,800+ stores
Timeline
12 months
Role
Senior Product Designer
01
PROJECT OVERVIEW
Building the Operational Backbone for 6,800+ Pizza Hut Restaurants
As Pizza Hut prepared to sunset its legacy store management platform, Quikorder, the U.S. business needed a new operational system capable of supporting thousands of franchise locations.
Working within aggressive timelines, I designed Store Admin, the operational internal tool that allowed stores to adapt to changing business conditions while extending Maverick, the design system used by Pizza 6,800+ stores supportedHut International.
My Role
Instead of recreating the legacy experience screen-for-screen, I partnered across Product, Engineering, and Operations to build a new Store Admin experience using the Maverick design system as foundation.
02
APPROACH & USER RESEARCH
Designing for operational reality.
Working closely with franchise operators, operations teams, product managers, and business stakeholders, several themes consistently emerged:
Operations Are Messy
Restaurants constantly adjust to staffing shortages, promotions, inventory constraints, and local conditions.
Edge cases are the product in operations
Split hours, temporary closures, neighborhood delivery restrictions, and payment limitations weren't exceptions, they were everyday operational needs.
Visibility Creates Confidence
Managers needed to quickly understand what changed, who changed it, and whether action was required.
03
THE CHALLENGE
Balance speed, flexibility, and consistency.
The project presented three competing constraints:
Replace a business-critical legacy platform without disrupting thousands of stores.
Deliver quickly by leveraging Maverick while expanding it to support entirely new operational workflows.
Build enough flexibility to handle real-world operational complexity without making everyday tasks harder.
This meant solving for dozens of interconnected workflows that all needed to feel like one cohesive product.
Bento Box
Carousel Prominence
Deal Propensity + Merchandising
04
THE PIVOT
I stopped designing features and started designing operational systems.
As new requests continued to come in from franchisees and store operators, it became clear these weren't isolated problems.
They all represented different ways operators needed to control their business.
Instead of creating one-off solutions, I identified reusable interaction patterns that could scale across the platform.
—-> example of the notification hub? we were taking one scenario one by one and just redesigning it under maverick, realized i can use the patterns and leverage but make better
Phase 1
Tile Foundation
Improve decision confidence
Stronger price prominence
Deal summary
Clear deal hierarchy
Phase 2
Deal Discovery
Support exploration
Discovery Badges
Intent-based browsing
Filters & Sorting
Phase 3
Brand Experience
Increase engagement
Rich merchandising
Enhanced imagery
Campaign moments
Phase 4
Personalization
Scale relevance
Adaptive modules
Behavioral ranking
Recommendations
05
EXECUTION
Content
Content
Content
A/B testing the tile foundation design before scaling the experience.
Before investing in richer merchandising or personalization, I wanted to understand a foundational question: can customers understand what's in a deal?
Hypothesis
If customers understand what a deal includes at first glance, they will require fewer exploratory clicks while maintaining (or improving) downstream conversion.thesis
PROCESS
Working with CRO and product teams, I created four concepts to isolate the impact of information hierarchy, deal summaries, and price prominence for Phase 1.
Metrics Tested
CVR, CTR, RPV, AOV
06
RESULTS
Before
Deal contents hidden until entering the builder
Repeated exploratory clicks to compare offers
High cognitive effort before selecting a deal
After
Deal summaries surfaced directly within each tile
Faster value comparison during browsing
Higher decision confidence before checkout
0.76%
-
Exploratory CTR
Fewer exploratory interactions indicated stronger decision confidence and less comparison effort.
0.88%
+
Conversion Lift
Clearer deal comprehension translated into stronger downstream conversion.
0.86%
+
Revenue per Visitor
Customers generated more value despite spending less time exploring.
0.73%
-
Average Order Value
High-value offers naturally attracted more attention, creating an opportunity to improve merchandising distribution in future iterations.
Impact beyond Phase 1
The experiment established an experimentation framework the team could build on. Each following phase introduced one new behavioral hypothesis while preserving what had already been validated.
07
WHAT'S NEXT
Building on the foundation.
Phase 1 proved that clarity improves decision confidence. The remaining roadmap focused on layering discovery, merchandising, and personalization without reintroducing cognitive load.
Phase 1 -> Done
Foundation
Help users understand deals.
Phase 2
Discovery
Help users discover more without thinking harder.
Phase 3
Brand
Create emotional moments after trust has been established.
Phase 4
Personalization
Surface the right deal at the right time.
08
REFLECTION
Clarity changed behavior.
THINGS I BELIEVE NOW
Start smaller than you think.
The biggest improvements came from strengthening the smallest building block of the experience.
Measure customer behavior, not just clicks.
Metrics rarely tell the full story in isolation. Lower exploratory clicks initially appeared negative, but customer behavior revealed this was due to greater customer confidence and reduced decision friction.
Build systems, not screens.
The most valuable outcome wasn't a single winning interface, it was an experimentation framework the team could continue learning from.
