Daina Linton: Using Tech to Ensure You Never Buy Ill-Fitting Clothing Again
An interview with the co-founder of Fashion Metric about her unusual background and the future of fashion e-commerce.
By Jasmine France (Contributing Writer, Women 2.0)
It’s no small feat to win Lean Startup Machine and become an AngelHack finalist within a month, but Daina Linton managed to do just that with the concept for her fashion technology company, Fashion Metric. The company was quick to receive funding from investor Mark Cuban, and Daina and her husband Morgan soon abandoned school and work to pursue the opportunity full-time. Their goal: challenge the status quo and change the way people shop online. I sat down with Daina to talk about how she’s using technology to make shopping easier.
Tell us about yourself. What is your background?
My background is very different from the apparel e-commerce world, but it’s my tech side that helped to inspire the metrics behind Fashion Metric. I grew up in the Toronto area and went to school for engineering while pursuing several research internships at University of Toronto affiliated hospital research labs. After graduation, I moved to Boston to pursue a summer research program in data analytics at MIT. My project focused on developing image-processing methodologies for a new optical imaging technology we were developing. I continued doing that work in Boston for two years after the program ended, publishing several research papers along the way. Prior to Fashion Metric, I was working on my PhD at UCLA developing an imaging modality to detect rare events within a complex network. I started Fashion Metric during my graduate work, leaving my PhD to run the company full time. I really wanted to branch out and do something different since I already spent so much time in academic science.
How did Fashion Metric bloom?
I come from a family of traditional menswear tailors and was always fascinated by both men and women’s fashion. The story of how Fashion Metric came about goes back to when I competed in Lean Startup Machine, a weekend competition all about learning the “lean startup” principles. During the competition we discovered that about 90% of men have a general problem finding clothes that fit their body type right. Afterwards we started to brainstorm around the idea of developing a personalized discovery engine for men to find clothes that fit and match their style to better enable their online shopping experience. The following weekend we competed in AngelHack where we built an initial prototype and eventually secured an angel investment from Mark Cuban. We decided to leave our day jobs (PhD in my case) to grow the company full-time.
How did you make the idea a reality?
After the initial funding, we decided to remain a lean startup and began running tests for a “concierge” service. We were working with customers right away before the website was fully built. We had a simple landing page explaining our value proposition and would follow up with phone calls to service our customers. We initially focused on offering dress shirts, since we found that to be the biggest pain point around finding the right fit.
The process we followed taught us a lot about how feasible it was to develop a technology that would determine how clothes would actually fit a customer. We built an enormous dataset of men’s full body measurements and developed a comprehensive technology to calculate a customer’s detailed set of measurements based on a seed of basic information provided by the customer. We then spent several months building and refining our Fit Technology with our first brand partner that used our data to create made-to-measure shirts. We did this to thoroughly evaluate and improve the Fit Technology, fitting hundreds of shirts exclusive created from the output of our technology and we ended up with no returns.
How does the service work?
We combined our Fit Technology with a platform designed specifically for how men prefer to shop based on what we learned with our early customers. Guys can discover new brands based on what fits them best, eliminating the obligatory puzzle around deciphering size charts. We offer high-quality curation and a concierge service where men have access to a stylist for a personalized experience, much like the in-store experience at Nordstrom. We’ve found that men generally shop in two ways: they buy in bulk a few times per year or they have other people do the shopping for them. Our service offers options for both, and the result is a return rate of just 0.6%, significantly lower than the industry average of 27.5%.
What were some of the challenges you’ve faced?
One challenging aspect was bringing the various brands onboard. We decided to forgo the affiliate model because we didn’t want our customers to leave the website and have a disconnected experience. This meant creating direct relationships with all of our brand partners prior to bringing the beta live. It’s a standard chicken and the egg problem: how do you get brands to get involved with something that’s brand new?
When we brought on our first brand partner to test the Fit Technology, we ended up increasing their sales by 50%. Once that was proven, interested brands began to approach us, many of them reaching out through social media. Today we work with a number of exciting brands including Second Button, Stone Rose, Seven Diamonds, French Connection, PX Clothing, Mavi, 34 Heritage, Jared Lang, Soxfords, Bison Made and Happy Socks to name a few.
What are your future plans for the company?
We started by focusing primarily on upper body measurements: Dress shirts, jackets, sweaters as well as accessories like socks and ties. We have all the data to expand our tech to include lower body measurements and are deploying the technology to include pants on the platform. We’re also very excited about licensing our technology to interested brand partners, particularly bespoke clothiers. The enormous strength of our algorithm is that it outputs a detailed set of customer measurements, much like what a tailor would measure. This data can be directly used to create made-to-measure clothing and we’ve already done the groundwork in evaluating its accuracy. We have a great deal of confidence in using the algorithm output to create impeccably fitted clothing and we’re excited to open up our platform this year so that other companies around the world can power their stores with Fashion Metric technology.
About the blogger: Jasmine France is a travel-addicted, food-obsessed Bay Area writer with a decade of experience covering consumer electronics, digital music, mobile apps and cloud computing. Follow her on Twitter @WeirdEaredJas.