Trekka

Trekka removes the guesswork from shoe fit—helping people buy the right pair with fewer returns.

A mobile-first product that uses 3D foot scanning and fit-aware recommendations to improve purchase confidence and reduce return costs.

Timeline

03/2023-05/2023

Role

Product Designer (Acedemic Project)

I led end-to-end product design, focusing on how users capture fit data, understand recommendations, and make confident purchase decisions. The project was validated through research and prototype testing rather than production launch.

Team

4 UIUX Designer

Problem & Constrains

Why it matters

Shoe returns are often a fit problem disguised as a shopping problem.

When customers lack confidence in fit, they compensate by buying multiple sizes or styles and returning what does not work. This wastes time for shoppers and creates high reverse-logistics costs for retailers.

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

Why try Trekka?

Trekka uses mobile 3D scanning to turn foot data into fit-aware shoe recommendations.

Users scan their feet with a phone camera. Trekka translates the scan into a fit profile and matches it against shoes with fit-relevant attributes, producing recommendations that are more likely to fit—not just popular.

Who Is The Target User?

Work Flow & Use Case: IN-Person Shoe Shopping

Trekka is designed for shoppers who frequently experience uncertainty in fit—especially when buying new brands, new shoe types, or shopping online.

Primary scenario: evaluation and selection. This is the moment when users ask, “Will this fit me?” and either commit, abandon, or buy multiple options to hedge risk. Trekka focuses on reducing uncertainty at this step by turning foot data into fit explanations and recommendations.

Journey mapping showed that the highest cost occurs during evaluation and selection—not discovery.

As a result, Trekka augments decision-making at this stage rather than automating the entire journey. This scope decision shaped the product around accurate scanning and explainable recommendations.

AI Interventions in Shoe Shopping Stages

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

This journey map shows how much time is involved for both users and Trekka during the 2nd experience. The longer the rectangular, the more time is involved.

Try on new shoes
Fill reviews
Refining the user preferences
Return process
Start a new purchase

Send our surveys
Analyzing and learning from feedback
Learning from user activity
Deal with the return situation

Information Architecture

Site Mapping

To keep the experience focused, I structured the information architecture around a single loop: scan → understand fit → evaluate options → give feedback.

I intentionally minimized secondary pages during the first use to help users reach a useful recommendation with minimal setup.

Value Proposition

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