INFO4400 Qualitative User Research and Design Methods Final Project
FitMatch: Reimagining Online Shopping for Young Women
Overview
This project explored how college-aged women (19–22) navigate online clothing shopping and how design can address challenges of fit uncertainty, limited social validation, and untrustworthy reviews. Using rapid ethnography, contextual interviews, and co-design workshops, we uncovered both practical frustrations and emotional needs that shape shopping confidence.
Our outcome is FitMatch, a community-driven app that combines personalized fit visualization with peer validation and body-positive support, empowering young shoppers to make confident purchase decisions.
Why It Matters
E-commerce dominance: Young women make up a significant share of online fashion shoppers but face high indecision rates due to poor fit cues and unreliable reviewsINFO4400_ T4 (7).
Beyond transactions: Shopping is deeply emotional — tied to identity, peer validation, and confidence. Current platforms lack trustworthy, relatable input from real people.
Design gap: Existing tech (AI recommendations, VTOs) often ignores social and emotional dimensions
Research Process
Method I: Rapid Ethnography + Contextual Interviews
4 participants, ages 19–22.
Observed live shopping in natural environments (dorms, apartments).
Follow-up interviews to compare stated vs. actual behavior.
Insights: reliance on peer validation, Instagram/Pinterest for inspiration, heavy use of detailed reviews/photos, long decision times due to return hasslesINFO4400_ T4 (7).
Method II: Co-Design Workshops
4 new participants.
Activities: review critique, ideal interface sketching, scenario-based prototyping.
Insights: strong desire for fit-matching avatars, advanced filters, and diverse body representation
Key Findings
Social Validation is Central: Friends and influencers drive trust more than brand ratings.
Fit & Visualization: The biggest pain point — users want real photos from bodies like theirs.
Deliberate Decisions: Shopping is cautious, with significant time spent comparing items.
Platform Loyalty: Trust and consistent sizing (e.g., UNIQLO) drive repeat purchases
Design Solution: FitMatch App
Personalized avatars via body measurements or scan.
“Fit for You” feed showing posts from users with similar body shapes.
Community validation: Comments, supportive peer interactions, and anonymized photo sharing.
Reward system: Points for posting reviews/photos, redeemable for discounts.
Impact & Discussion
Shoppers don’t just need better sizing charts — they need authentic, socially integrated tools that reflect real bodies and experiences.
FitMatch builds trust through representation and community, addressing both practical fit and emotional confidence.
Limitation: still can’t replicate tactile qualities (fabric texture, stretch).
Challenge: sustaining user contributions and accurate body data over time