Shoebox
A modern eCommerce UX framework built around personalization, purchase confidence, and repeat conversion created as a speculative engagement for an online shoe store.
Service
UX Strategy
Sector
Retail eCommerce
Year
2026
Background
"I've worked on eCommerce systems before, but for the past decade, I've designed IoT platforms where the challenge was making complex sensor data intuitive and actionable. eCommerce presents similar complexity with massive catalogs, diverse user intent, and behavioral data that must be distilled into seamless conversion experiences."
When a conversation with this retail footwear client surfaced, I built Shoebox to make the case on my own terms. Not a portfolio piece retrofitted to fit a job post, but a focused point of view on where their digital experience could go, grounded in current personalization technology and real conversion principles.
The last decade I've spent designing systems where the complexity is enormous and the margin for confusion is zero. Retail commerce is a different domain, not a different skillset. Shoebox was the vehicle for proving that.
The Framework
Shoebox is organized around three mechanics that drive repeat purchase behavior in modern retail. Each pillar has a corresponding set of interactive prototype screens.
Customization
Prediction and curation based on observed and explicit preferences. Make the catalog feel like it was built for this person.
Confidence
Reducing purchase anxiety through social proof and intelligent insights. Answer the questions before the customer has to ask them.
Encouragement
Incentivizing action and removing barriers to purchase. Loyalty, recency, and smart cart behavior working together.
Personalized Homepage + Product Headers
The same prediction models that power personalized headers extend to the homepage hero. A sneakerhead sees limited edition drops. A runner sees performance gear. First impressions are doing conversion work before anyone scrolls. This is table stakes for modern retail and still widely underimplemented.
Profile + Filter for Me
Explicit preferences live on the profile page — size, brands, styles. Behind the scenes, behavioral signals fill in the gaps. A one-tap "Filter for Me" button on category pages turns that profile into instant action, eliminating repetitive manual filtering on every visit. Saved preferences stop being passive data and start driving the purchase path.
AI Review Summary + Fit Data
Nobody reads 400 reviews. AI-powered summary pulls the signal out of the noise (fit, comfort, durability, common complaints) and presents it in digestible form. Paired with structured fit gauges and searchable review filtering, customers get confidence fast. Fewer returns. Higher first-time conversion. Social proof made actually useful.
Loyalty Status at Checkout
A progress indicator above Add to Cart showing proximity to elite status turns an abstract loyalty program into a live behavioral nudge. Customers on the threshold of an upgrade feel urgency that's genuine, not manufactured. Paired with smart replenishment suggestions ("You bought these 6 months ago"), the cart becomes a tool for both conversion and retention.
See It in Action
The full interactive prototype walks through all three pillars with live Figma-site interactions.
The presentation deck covers the strategic rationale in depth.
