Deals Experience
I REDESIGNED PIZZA HUT’S DEALS EXPERIENCE TO IMPROVE DEAL COMPREHENSION, PROMOTIONAL DISCOVERY, AND CONVERSION THROUGH PHASED EXPERIMENTATION.
Role
Senior Product Designer
Project Impact
Timeline
2026
The Opportunity
Fashion ecommerce has become remarkably efficient at helping consumers purchase more.
It has become far less effective at helping consumers purchase well.
Consumers today face:
endless product catalogs
accelerated trend cycles
rising return rates
decision fatigue
economic uncertainty
closet overwhelm
Research revealed a recurring behavioral pattern:
Users weren't necessarily looking for more recommendations.
They were looking for more certainty.
Many described:
purchasing clothes they rarely wore
difficulty translating inspiration into reality
uncertainty around fit
guilt around impulse purchases
wardrobes filled with disconnected pieces
One insight became foundational:
Consumers don't want AI to replace taste.
They want AI to reduce uncertainty.
Reframing The Problem
Most AI fashion products ask:
What should I wear?
Or:
What should I buy?
Styllo explores a more interesting question:
How should I show up?
Instead of treating outfits as isolated recommendations, the system understands the broader context surrounding a user's life.
The outfit becomes the output.
The user's identity, goals, wardrobe, calendar, travel plans, and preferences become the inputs.
The Agent Ecosystem
Identity Agent
Maintains a continuously evolving understanding of:
Kibbe-inspired silhouette preferences
color season analysis
visual proportions
contrast levels
fit preferences
aesthetic evolution
Rather than assigning rigid style labels, the agent updates its understanding over time as user behavior changes.
Taste Intelligence Agent
Learns from:
Pinterest boards
Instagram saves
moodboards
outfit likes
rejected recommendations
shopping behavior
Instead of asking users to complete lengthy quizzes, the system passively learns through visual behavior.
The result is a dynamic taste profile that evolves alongside the user.
Wardrobe Agent
Creates a living inventory of:
owned clothing
wear frequency
outfit combinations
category gaps
underutilized pieces
Rather than treating a wardrobe as storage, the agent treats it as a dynamic system.
This enables:
closet utilization insights
outfit generation
wardrobe gap analysis
purchase necessity scoring
Calendar Agent
One of the most compelling explorations within the project.
By integrating with calendar systems, the agent develops awareness of:
work meetings
speaking engagements
weddings
travel
vacations
social events
For example:
A user might have:
a speaking panel in Mexico City
a wedding in Austin
a birthday party in Lisbon
Rather than generating generic outfit recommendations, the system proactively prepares personalized outfit plans based on:
event context
weather forecasts
travel logistics
wardrobe inventory
personal style preferences
Users can further refine recommendations by adding contextual prompts:
"The event brand uses terracotta and cream."
"I want to appear more authoritative."
"There will be outdoor networking."
The system incorporates these constraints into its decision-making process.
Outfit Planning Agent
Generates:
daily outfit recommendations
travel packing plans
occasion-based styling
weather-aware combinations
The objective isn't simply creating attractive outfits.
The objective is helping users feel prepared and confident.
Shopping Agent
Perhaps the most unconventional part of the system.
Most commerce platforms are designed to maximize purchasing behavior.
The Shopping Agent is designed to challenge it.
Before recommending a purchase, the agent evaluates:
wardrobe compatibility
outfit versatility
usage likelihood
overlap with existing items
long-term value
In many cases, the recommendation becomes:
Don't buy this.
or
You already own three pieces that fulfill the same role.
This reframes the system from a sales engine into a decision-support tool.
Resale Agent
To further support intentional consumption, the platform explores integration with secondhand marketplaces.
When a user identifies a desired item, the agent searches for:
pre-owned alternatives
similar silhouettes
archived versions
resale opportunities
Rather than optimizing exclusively for retail transactions, the system encourages more sustainable purchasing behavior.
Confidence Agent
Tracks outcomes after purchases and outfit recommendations.
The agent continuously learns through feedback loops such as:
Did you wear it?
Did it fit?
How did it make you feel?
Would you wear it again?
Did you receive compliments?
Did you feel confident?
This creates a style memory system that moves beyond transactions and begins understanding emotional outcomes.
Rethinking Reviews Through Similarity Modeling
One opportunity I explored involved improving how users evaluate clothing before purchasing.
Traditional product reviews lack context.
For example:
"Runs large."
Large for whom?
The system instead prioritizes feedback from users with similar characteristics:
height
proportions
body shape
fit preferences
sizing history
This creates significantly more relevant purchase guidance and reduces uncertainty before checkout.
This is where final designs would go!
Behavioral Frameworks
The project ultimately became an exploration into behavioral commerce psychology.
Certainty vs Exploration
How might AI balance:
familiar recommendations
adjacent discovery
aspirational experimentation
without overwhelming users?
Aspiration vs Reality
Users often save outfits that don't align with what they actually wear.
I explored how AI might bridge that gap gradually rather than forcing dramatic style changes.
Purchase Confidence
Rather than optimizing for conversion, I explored a system optimized around:
How confident is this purchase decision?
New metrics emerged:
Purchase Confidence Score
Wardrobe Utilization
Cost Per Wear Projection
Style Alignment Score
Regret Risk
Reflection
Styllo began as an exploration into AI-powered styling.
It evolved into a much larger question:
What if AI could help people make better decisions, not just better purchases?
The project challenged traditional assumptions around personalization, consumption, and recommendation systems.
Rather than optimizing for more shopping, the vision became helping people build wardrobes—and relationships with clothing—that feel more intentional, sustainable, and aligned with who they are.
In a future increasingly defined by AI agents, I believe the most valuable systems won't simply recommend.
They'll help people decide.