Perfect Fit
Mobile iOS App
Perfect Fit is a shopping app that helps users find the right clothing size, color and style with the help of AI. The app takes your specific measurements, style preferences and color analyses, so it can make recommendations based on color theory, popularity and budget. I led the product design, research and UX/UI experience for the app alongside 4 other UX designers.
Project Overview
ROLE
Project Lead, User Research
UI Design, Information Architecture, Prototyping & Testing
Sep 2023 - Nov 2023
THE PROBLEM
Online shopping is frustrating due to unclear sizing charts, clothing that often doesn’t meet expectations, and a lack of personalized style recommendations, leading to returns, wasted time, and overall dissatisfaction with the shopping experience.
THE SOLUTION
The Perfect Fit app simplifies styling and shopping by using your measurements to find your size and an AI stylist to recommend outfits tailored to your features and wardrobe. Enjoy fewer returns and more confidence!
Prototype
Research
Survey + Interviews
Literature Review
Competitive Analysis
Card Sorting
Personas + Affinity Mapping
Tree Testing
Surveys + Interviews
Interview Insights
Poor fit
Users recalled many bad experiences with shopping online and having their item not fit them well. This deterred them from shopping from that brand or online altogether.
1.
89% of users reported having an issue with ordering clothes online that did not fit well
2.
Trouble matching sizes
Users may know their size for one brand but it may not be the same for another brand. This usually requires the user to comb through reviews or to actually measure themselves to ensure a good fit.
Many users shop in-store so that they can assure that the sizing is accurate.
Wants clothes to suit body type
Our surveys and interviews not only revealed users’ problems with sizing but also their personal style. Many interviewees stated they have trouble finding outfits that flatter their bodies, even if the sizes are correct.
3.
Users are open to the adoption of softwares that can help them shop for their body types.
User Personas
Based on our research, we recognised that there were 3 key user archetypes that our product tried to solve problems for: The Realistic, The Trend-Setter and The Researcher. Each archetype’s behavior informs our strategic approach, such as providing information and comparison tools.
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Affinity Mapping
Based on our research, we recognised that there were 3 key user archetypes that our product tried to solve problems for: The Realistic, The Trend-Setter and The Researcher. Each archetype’s behavior informs our strategic approach, such as providing information and comparison tools.
Card Sorts
We conducted 3 card sorts: 2 hybrid and 1 closed sort, starting with broad participant input and later focusing on specific problem areas in our app's navigation, informed by user associations with key elements like 'account' and 'privacy settings.' . This iterative process, culminating in a closed sort with 30 participants, helped us streamline our app's design and navigation, addressing uncertainties and aligning with user expectations.
Tree Testing
We conducted two rounds of tree testing; the first identified issues in tasks such as AR try-on and purchasing saved items, while the second showed improvements in most tasks except Avatar Try-On and Review Fabric Match Information.
The tree tests revealed that unclear question wording and incorrect destinations initially led to low success rates, which improved significantly after adjustments, such as adding a missing destination and renaming confusing features. For example, revising labels and questions increased success rates from 26% to 53%. We also learned that the 'PerfectFit Passport' label was confusing, prompting us to replace it with clearer titles like 'Edit/View Measurements.'
