Alias Visual Search

Company
Alias, a selling platform within the GOAT Group e-commerce ecosystem.

Project Details
Launched and refined a sneaker listing tool to improve discovery, scanning, and listing success across multiple iterations.

Timeline
Phase 1: Jun 2021 - Dec 2021
Phase 2: Aug 2023 - Mar 2024

Role
Product Designer

Problem + Context

Problem

Business Problem

  • Post-COVID, leadership wanted to differentiate the app and modernize the sneaker selling experience. Sellers were limited to text search, requiring prior knowledge of items and reducing discovery.

Goal

  • Support sneaker and apparel discovery with ML-powered search

  • Modernize the selling experience

  • Differentiate the app in a competitive resale market

Solution

  • Phase 1: MVP with photo search to validate seller value

  • Phase 2: Design system updates and usage insights (barcode scanner emphasis) to improve scanning success and seller adoption

Impact

  • Phase 1: MVP widely adopted; app rating increased from ~4.0 → 4.4

  • Phase 2: Visual Search now accounts for 13% of searches (up from 5%); UPC scanning ~90% success, image search ~60%

Phase 1: Process

Data Science Collab

Worked with the data science team to determine the optimal number of guesses for sellers → top 4 predictions

Field Research

In-store observations; tested photos at various angles and lighting to validate model performance

Competitive Analysis

Reviewed AR/photo recognition features to inspire and inform interaction patterns

Early Exploration

  • Photo capture + upload became primary methods; decided image URL could be optional

  • Placed image alongside results for easy comparison

  • Used current sneaker templates for ML model accuracy

Initial exploration showing image next to results for easy comparison, leveraging sneaker templates for model accuracy.

Iteration

  • Added feedback loop and “rate the app” CTA

  • Integrated barcode upload; clarified sneakers vs apparel input

  • Adopted tab layout and instructional copy from GOAT app inspiration

Refinements based on manager and stakeholder feedback, emphasizing accuracy and usability while addressing multi-product complexities.

MVP Final Touches

  • Simplified UI; removed unnecessary tabs

  • Prepared copy team for launch

  • Ensured stakeholder alignment

Final refinements to streamline user flow, clarify input guidance, and prepare for launch.
MVP Visual Search experience — seamless photo and barcode input with dynamic results and seller feedback loop.

Takeaways

Outcomes + Results

  • QA with engineering ensured accurate implementation

  • Beta field testing validated happy/unhappy paths

  • Optional photo harvesting step added to continue training MLM, gaining adoption

  • Sellers widely adopted MVP; app rating rose 4.0 → 4.4

Reflections

  • Collaborating with the data science team and testing in the field highlighted the challenge and reward of designing a seamless ML-powered experience for sellers

Lets transition to Phase 2…

As a recently promoted mid-level designer, I took greater ownership and partnered with PM, Ben. Together, we re-examined seller needs and emphasized barcode scanning for sneakers while maintaining photo capture/upload support.

Phase 2: Process

Seller Survey

  • 80% rated Visual Search 4/5–5/5

  • Secondary use: 24% for price-checking

  • Main pain point: inaccurate scans

Competitive Research

  • StockX: validated sneaker photo search standards

  • Yuka: inspired engaging scan interactions (haptic feedback)

Early Exploration

Structure: barcode scan tab + photo capture tab + results page
Early feedback: add scan line animation, simplify toggle and background gradient

Iteration

  • Shifted to barcode scanner primacy

  • Simplified toggle; refined the new results sheet to match both barcode + image result pages

  • Feedback: adjust sizing info, improve image spacing, consistent CTAs

Final Design

Execution & Collaboration

  • Briefed Brand team to create a unique motion asset emphasizing sneakers + UPC scanning

  • Collaborated with copy team via annotated Figma brief

  • Prototypes were presented to engineering teams to ensure smooth development, and aided QA teams for support on edge cases

Overall Takeaways

Outcomes + Results

  • Managed design QA with PM; tracked engineering implementation

  • Impact Metrics:

    • Visual Search = 13% of alias searches (up from 5%)

    • UPC scan success ~90%; image search ~60%

Reflections

  • Phase 2 allowed me to grow in responsibility, efficiency, and brand-aligned UI thinking

  • Finally, seeing adoption metrics rise from Phase 1 while delivering a polished, usable feature was incredibly rewarding