Fitsearch
Launching an e-commerce search engine in 6 months
While many of TrueToForm's solutions are B2B, Fitsearch is TrueToForm's consumer solution. Fitsearch is a search engine that pairs with TrueToForm's mobile scan app to find clothing that fits, by collecting garment design files across apparel merchant sites to return highly accurate fit recommendations for shoppers.
Timeline
From explorations to launch in 6 months while working on multiple projects at the same time.
Impact
Fitsearch has signed 33 affiliate contracts since launching and gained 500+ immediate signups following a feature in the Hustle.
Role
I was one of three designers on the team, contributing to:
Early-stage discovery and user research
Design sprints
User flows
Low fidelity design explorations
Instagram and Tiktok content creation
Rolling usability testing for Fitsearch
Team
CEO & Co-Founder
CTO & Co-Founder
Developer
Lead product designer
Product designer
33
Sign affiliate contracts since going live
Chances of increased traffic, reduced return rates, and customer trust have gained the interest of brands and merchants
500+
Immediate signups after being featured in The Hustle
Interest in Fitsearch landed us a feature in the Hustle: Using AI for the perfect fit
Measures of success for Beta & V1
Validate if shoppers would be willing to scan their avatar
Test that Fitsearch could get accurate garment specs
Test that shoppers can scan and connect their avatars to Fitsearch
As a starting point, I conducted discovery research to learn what specific challenges shoppers struggled with when shopping for clothing that fits. I began my research by combing through dozens of reviews for popular clothing brands on trustpilot.com. I found this method helpful for finding honest and unfiltered feedback from shoppers.
I noticed several themes after reviewing shoppers reviews and conducting white paper research online that places increased burdens on both shoppers and retailers.
Confusing size charts and increased burdens on shoppers
Fit and size continue to be the primary reason for online returns, accounting for 42% of returns. There are no universal size charts used by clothing brands which makes it difficult to shop online. In consequence, some shoppers hesitate to shop from new brands or decide to risk ordering the wrong size.
Bracketing
Some shoppers resort to 'bracketing' which refers to buying multiple sizes of one item at one time. This places a burden on both shoppers to return items that don't fit which can drive traffic away from retailers who don't offer free shipping and returns. Retailers also bear the burden for shipping, inspection, and repair costs.
Lack of diversity and inclusion for different body types
Many shoppers find that many existing size charts do not accommodate for the full range of body types. 67% of American women wear a size 14 or above, and most retailers don't carry these sizes.
In addition to reviewing user painpoints online, I also conducted white paper research on our core target audience - Gen-Z and Millennial shoppers. I researched economic, cultural, and technological events that shaped their motivations and spending habits to make sure I was framing for problems experienced by these groups.
The convenience seeker
Shoppers who prioritize convenience only shop online with websites that offer generous return policies, as they often struggle to find the right clothing size. Otherwise, they tend to prefer thrifting or shopping in person to avoid the hassle of paying for return shipping or dealing with time-consuming, complicated return processes.
Social media motivated
Many Gen-Z shoppers and some Millinneal shoppers rely on social media platforms for inspiration when shopping for clothing. Some shoppers struggle to fit within the sizing charts offered by many brands and thus will turn to social media outlets that appeal to their specific body type such as petite or plus size.
Brand loyal
Many shoppers—especially millennials—are often hesitant to try new brands, preferring to remain loyal to those they already know. However, because bodies change over time or through life events such as pregnancy, this loyalty can present new challenges when shopping with the same brands.
I collaborated with two of my design colleagues to map out all the user problems and potential blockers identified during our discovery research. Together, we used the these painpoints to guide our how might we questions to reframe these challenges into opportunity areas. I brainstormed solutions in the group session and afterwards conducted lightning demos to draw more solutions and visual inspiration from a range of web products.
Building trust
How Might Wes
How might we instill trust in our avatar scan process?
How might we increase customer trust and confidence in our fit predictions?
The TrueToForm avatar would be the key to powering fit recommendations but without the willingness of shoppers to scan the search engine would lose it's competitive edge. Would shoppers be willing to go through the effort of downloading the scan app and scanning their body? And what would encourage them to do so?
From my lightning demo research, I found that many websites build trust through social proof by sharing customer testimonials, product reviews, or influencer endorsements.
Educating shoppers
How Might Wes
How might we educate new users on how to scan and connect to the search engine?
For first time visitors of Fitsearch, shoppers would need to understand how to connect their avatar to view their fit predictions. Furthermore, since we would be asking shoppers to use a mobile app outside of Fitsearch we needed to educate users on why they were doing so and how it was tied into their fit predictions.
For lightning demos, I shared snapshots from websites such as Honey, Stich Fix and Short Story, where I found helpful step by step instructions on how to use the services.
Value of scanning an avatar
How Might Wes
How might we communicate the value of making a personalized avatar?
How might we communicate that our fit predictions sometimes may be inaccurate if retailers provide inaccurate garment specs?
For most shoppers, this would be their first time encountering scan app technology. Downloading a new app outside of Fitsearch and learning to scan their body would be a barrier to entry. How could we convince users to scan and create a personalized avatar?
Communicating fit
How Might Wes
How might we communicate how something will fit (loose or tight)?
How might we communicate how something will fit while considering styles of clothing (boyfriend jeans vs. skinny jeans).
What further complicates clothing fit are the nuances in style. For instance, shoppers often search for specific styles of jeans such as boyfriend jeans that tend to have a baggier fit or skinny jeans that have a tighter fit. How would we provide an accurate fit recommendations for the same person for these two styles of jeans?
I developed user flows based on how might we questions and lightning demo research to identify the features and screens required for implementation.
I participated in a design sprint for Fitsearch, brainstorming low-fidelity concepts that informed the product vision. While the lead designer owned the design system and high-fidelity work, she reviewed my explorations, provided feedback, and encouraged “pie-in-the-sky” ideas even as we prepared for the beta launch.
She asked me to brainstorm the following flows:
Avatar scan flow
Fit survey flow
Search & browsing flow
Fit survey & scanning an avatar flow
Since shoppers frequently browse on their phones, I started by exploring design solutions from a mobile-first breakpoint. The essential features for the landing page included:
Option to fill out a fit survey
Option to scan an avatar
A way to run a search
Search Results Flow
The must haves for the search and browsing flow included:
Option to alter the search
Image of garment (pulled from the retailer selling the garment)
Fit score - the CEO and CTO indicated that Fitsearch would recommend items with a 98% Fit score
Price of garment
Name of retailer
The engineering team implemented the MVP designs created by our senior designer and password-protected the website. I worked with my design colleague to test and interview 16 users, conducting rolling usability sessions on Fitsearch and TestFlight during the MVP release and subsequent feature updates. These sessions uncovered usability issues and bugs in real time while capturing both qualitative and quantitative feedback from participants.
In our usability tests, I wanted to validate two objectives that we were using to measure success:
Are shoppers willing to scan their avatars?
Are shoppers able to successfully scan and connect their avatar to Fitsearch?
Being part of this amazing project was an incredible experience. I learned so much from contributing to the development of an end-to-end product. One of the key takeaways was recognizing how valuable and essential it is to engage with users throughout the design process. My skills in conducting interviews and usability testing improved significantly as a result.
After implementing updates based on multiple rounds of usability testing, the product was officially launched to the public by removing the password-protected homepage. Since going live, the project has secured 33 affiliate contracts, which has been exciting and rewarding to witness. Although there is still much work to be done, Fitsearch's next steps are to expand into additional clothing categories and strive for a better product-market fit.