Trekka

Trekka helps shoppers understand shoe fit before purchase by turning mobile 3D foot scans into explainable recommendations.

Instead of relying only on size labels or reviews, users can compare their foot profile with product-specific fit characteristics and make more confident purchase decisions.

Fit-confidence tool for online shoe shopping

Role: Product Designer

Scope: Research, mobile onboarding, foot-scanning flow, recommendation UX

Team: 4 UI/UX Designers

Timeline: 03/2023–05/2023

Focus: Fit confidence · Explainable recommendation · Return-risk reduction

Problem & Constrains

Fit Uncertainty Drives Return Behavior

Fit uncertainty drives return behavior.

Online shoe shoppers often cannot tell whether a product will match their actual foot shape. When confidence is low, they may abandon the purchase, order multiple sizes, or return what does not fit.

Trekka focuses on reducing uncertainty before purchase by making fit more measurable, visible, and explainable.

Returns are expensive for both customers and retailers.

63%-72% of Adults do not wear shoes that accommodate their foot dimensions

-National Library of Medicine

Project Concept

From Shoe Size to Fit Profile

Shoe size alone does not explain fit. Two people with the same size may have different width, arch height, and length proportions.

Trekka uses mobile scanning to create a reusable fit profile and match it against shoe-level characteristics.

The product turns fit from a guess into a comparison.

Finding the Highest-Impact Moment

Trekka focuses on pre-purchase fit evaluation, where uncertainty is highest and return risk begins

Research Evidence

Journey Map
(for both users and Trekka’s side)

Journey mapping showed that fit uncertainty peaks during evaluation and selection.

Users are not only asking “Which shoe do I like?” They are asking “Will this actually fit me?”

Scope Decision

Trekka focuses on the pre-purchase evaluation moment rather than the entire shoe-shopping journey.

This narrowed the product down to three core actions:

Scan the foot → Explain the match → Recommend shoes

Product Architecture

Scan → Explain → Recommend → Learn

To keep the first-time experience focused, I structured the product around one core loop:

Scan foot → Generate fit profile → Explain match → Recommend shoes → Collect feedback

Secondary features such as surveys, reviews, notifications, and settings were placed outside the first-use flow to reduce setup friction.

Design principle: minimal setup, maximum fit confidence.

Recommendation Loop

Effortless scanning for the perfect fit every time

Scan

Find your perfect running shoe match without the hassle of trying them all on

Match

Get personalized shoe recommendations without ever leaving your couch!

Explain

Learns and adapts based on your feedback, ensuring greater accuracy with every use

Learn

Core Experience

Onboarding Sequence

Users fill out basic information, scan their feet, and are presented with some basic recommendations based on their results.

Guided Foot Scanning

The scanning flow is the highest-friction step in the product, so the interface breaks it into clear, guided actions.

Key design choices:

Step-by-step framing
- Users scan one angle at a time instead of being asked to understand the full scanning logic upfront.
Real-time alignment feedback
- Foot outlines, progress states, and success confirmation help users know whether they are doing it correctly.
Privacy statement before scanning
- Users see why scan data is needed before providing sensitive body information.

Building Confidence Through Explainable Fit

Showing why a recommendation fits, not just how much it matches.

A match percentage alone can feel like a black box. Trekka makes the recommendation more transparent by showing how the user’s fit profile compares with product characteristics across width, arch height, and length.

Design impact: Users can understand the reason behind a recommendation before deciding whether to buy.