Bob Dylan once said that you have to take inspiration where you find it, and Fitting Room Social founder Brad Liff found his on the morning after Christmas a couple of years ago. While Liff’s wife was returning clothes that did not fit at a store, he thought there had to be a better way to help women find fashion with the right fit while shopping online.
After finding that there were no solutions that addressed his wife’s frustrations, Liff said that he “wanted to find a way to encourage women to shop online, and help them overcome the single greatest hurdle they have to making purchases of clothing, which is feeling comfortable that what they are looking at is going to fit them well.” After developing a prototype and securing some seed funding, Liff departed his career in finance to devote himself to entrepreneurship.
Now open for business, Fitting Room Social is a collaborative shopping platform for women designed to allow its members to share and receive recommendations tailored to their peers’ style and fit preferences. Women can show off their style by sharing pictures of themselves wearing clothes they love, while helping other women with similar sense of style and size to make their buying decisions rather than relying on generic size charts.
Fitting Room Social also packs tools to help members streamline their decision-making and shopping experience. The platform’s recommendation engine uses members’ profile information to show them trending styles, flag items that are a good fit, and the ability to easily add items to a virtual fitting room. From there members can click to be directed to the retailer’s site to complete their purchase.
In addition to serving women, Liff wants to add value to online retailers. He believes that by increasing the accuracy of the fit of each purchase, retailers will see increased sales and a decrease in returns.
Liff’s main goal for 2014 is to increase Fitting Room Social’s membership. He says that “this platform gets exponentially better as more people sign up and add to it.” He adds that “the more members, the more data points, the more fit recommendations we can make, and the experience and quality of the recommendations just get better with more people.”
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