Concept · Aura
I can't justify buying new luxury anymore, but pre-loved never feels like it's really mine.
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Domain
Luxury E-Commerce · AI Strategy
Market
Middle East · Gen Z
Type
Design Concept
Status
Concept
Aura is a strategic design concept reimagining luxury e-commerce for the Middle East. Through discovery research including behavioural data analysis, market studies, and UX benchmarking, I identified a strategic gap: Gen Z luxury shoppers have the highest conversion rates on platform, but pre-loved luxury was invisible on the homepage.
What would take weeks of manual persona research was accelerated, modelled, and validated in hours with AI deep research tools, producing Layla, a data-driven composite persona rather than a fictional character.
Why This Matters Now
Through discovery research including behavioural data analysis, market studies, and UX benchmarking, we identified a strategic opportunity to reimagine the homepage using personalisation, pre-loved and Gen Z as focal points.
Conversion Rate
By Age Segment
Gen Z Luxury
Value Priorities
Market Share Gap
The Persona
Layla
Layla is not fictional. She is a data-driven composite persona built from behavioural research, reflecting emerging patterns among Gen Z luxury shoppers in the Middle East.
Sustainability is the new luxury.
I wake up checking TikTok and Instagram.
Pre-loved? More like re-loved. I am giving it a second story.
When an app knows my vibe it feels like magic.
Show me what is trending for me, not just what is trending.
Layla's Emotional Journey
To identify opportunities for becoming Layla's digital wardrobe.
Discover & Desire
I just saw a Dior bag on TikTok. Where do I even find it?
Curate & Style
I love my Chanel bag, but what would make it pop for brunch?
Revisit & Remix
I have already worn this. Can I wear it a new way?
Shop Smart & Sustainably
I want to build looks without blowing my budget.
Strategic Design Process
The critical components that transform the homepage from browsing to buying.
Align on What Matters
- Pre-loved luxury integration
- Gen Z acquisition and loyalty
- AI-enabled hyper-personalisation
Model the Right Users
- Create data-rich Gen Z personas
- Simulate behaviour via AI-generated journey models
- Identify deep UX friction points
Architect for Scale
- Introduce modular homepage layout adaptable by AI
- Design for new and pre-loved across categories
- Ensure discoverability, trust, and performance
Deliver Measurable Value
- Link homepage KPIs to revenue and retention outcomes
- Build roadmap with release phases and feedback loops
- Enable experiments via A/B hooks
AI-Powered Modules Designed
Curate My Closet
An AI-powered module that remixes past purchases and pairs them with new and pre-loved pieces to complete a look.
Business Value
+ Order Value per Customer
Hero UI
User Flow
Style My Closet
Acts as a personal style coach, analysing uploaded wardrobe items to suggest complete outfits based on colour theory, texture matching, fashion trends, and user preferences.
Business Value
+ Increased AOV through complete looks
Hero UI
User Flow
Style Stack
After adding an item to cart, suggests curated pre-loved accessories that elevate the outfit under a set budget.
Business Value
+ AI Differentiation and reduced Cart Abandonment
Hero UI
User Flow
AI Modules for Long-Term Growth
These modules were not prioritised for MVP, but represent real opportunities to deepen loyalty, reduce friction, and strengthen brand distinction as Layla's behaviours evolve.
AI Visual Search
Help find pre-loved luxury
Image-based search helps users locate exact or similar items powered by computer vision and metadata learning.
AI-Driven Social Proof
Build trust in pre-loved
Highlight styled pre-loved looks and authentic user reviews in a social-card format to reinforce credibility and desirability.
Generative Wishlist
Layla wishes, AI delivers
Users tell the assistant what they want and AI builds a wishlist refreshed automatically.
Sharing Features
Turn taste into influence
Enable one-tap sharing of wishlists, looks, or closet snapshots across Social Media, driving acquisition and brand virality.
AI Stylist
Luxury that knows you
A conversational agent that curates looks based on previous purchases, uploaded wardrobe items, or personal mood boards.
Prioritisation & MVP Delivery Plan
Win Trust
Pre-Loved carousel
AI personalised feed
Dynamic Hero
Pre-Loved badge
Basic Style Stack
Build Loyalty
Style Stack AI cart add-ons
AI stylist chatbot (optional, beta)
Drive Innovation
Advanced generative shopping assistant
Full closet digitisation (uploading wardrobe)
Risk & Tradeoffs
AI Recommendations Must Be Trustworthy
Risk
Poor suggestions, lag, or inconsistent logic erode user trust fast, especially for Gen Z.
Mitigation
Start with narrow AI logic (occasion-based styling only), validate with internal QA and user feedback before scaling.
Visual Search Requires Careful Rollout
Risk
Rushed launches of AI features can create UX dead-ends or tech debt, especially if LLM training is not tailored.
Mitigation
Stage rollout in limited contexts (pre-loved bags only), with feedback loops before full wardrobe integration.
Pre-Loved Requires Brand-Aware Storytelling
Risk
Pre-loved may risk being perceived as lesser or cheap, threatening the luxury positioning.
Mitigation
Pair every pre-loved moment with editorial cues (Styled by AI, Loved Before) and trust signals (authenticity, limited drops).
Business Outcomes
15-22%
Revenue Growth
Revenue uplift through higher Average Order Value and Pre-Loved cross-sell at checkout.
30-40%
User Engagement
Deeper engagement by offering curated styling moments post-purchase action.
15-20%
Operational Efficiency
Efficiency gains through higher cart conversion and lower abandonment rates.