Clothes Horse Wants To Solve The Biggest Problem With Online Shopping: Finding Clothes That Fit


Clothes Horse, a fashion technology company based out of New York, is publicly launching its platform today in an attempt to address one of the biggest challenges facing online shoppers: buying clothes that fit. Through the use of a customizable widget that merchants add to their own websites, Clothes Horse can determine within just 30 seconds how the retailers’ items will fit any customer. The goal is not only to decrease shopping cart abandonment, but also the rate of returns due to ill-fitting clothes.

The problem with shopping for clothes online is that customers have gotten burned by their past experiences. “Instead of being excited about this great new thing you’re about to buy,” explains Clothes Horse co-founder Vikram Venkatraman, “you think about the last time you had to return something, or you start wondering if it’s really going to be as nice on you as it looks in the picture.”

It’s those points of hesitation that cause 70% of shopping cart abandonment, he says. Not only that, but 60% of the time customers return clothes they bought online, it was because of fit issues.

Retailers, of course, know the challenges surrounding fit – it’s why they provide measurements and size charts for all their clothes on their websites. But just because you can zip something up, that doesn’t necessarily mean it fits well. To address these issues, Clothes Horse provides a quick, user-friendly product that helps online shoppers determine, in about thirty seconds, whether something will actually fit in real life.

The product is a white-labelled Q&A system that enables shoppers to build a profile based on their answers to questions, which don’t have to involve measurements. Although retailers can customize the system to suit their needs, the idea is to go beyond things like height and weight, and find out about a customer’s body type, preferred brands, comfort in a given brand, and more.

For example, a men’s clothing site might ask “What brand’s dress shirt fits you best?” to which the shopper could choose “Ralph Lauren,” “Calvin Klein,” “DKNY,” etc. They could also describe how well that brand fits, answering “it’s perfect,” “it fits well,” or “it doesn’t fit very well.”

After a handful of questions like this are answered, the Clothes Horse widget then tells you what size to buy, and, most importantly, how it will fit (e.g. “tight around the chest,” “just right in the collar”).

“Now you know what trade-offs you have to make, if any,” explains Venkatraman. “You know what to expect given your lifetime of shopping. It lets you put this new thing that you’re shopping for in a context that you’re used to, so you know a little bit about it,” he says.

In early tests with Clothes Horse beta customer, use of the new system delivered a 13% sales boost, results which the retailer has called encouraging. Heck, it may even be able to help you find jeans that fit!

Behind the scenes, Clothes Horse has a large database of human measurements which they’ve combined with measurement data from around 50 brands, six of which are live now on the web (Bonobos, Modus Man, Duke & Winston, Five Four Jeans, Frank & Oak, and one undisclosed customer). The startup is also in talks with several large retailers, who are reportedly very interested in the platform.

Finding fit is really only phase one of Clothes Horse’s grand scheme. Further down the road, it plans to support Facebook integration to help users build a shopping profile based on brands they “like” and what their friends like. This profile will function as a portable identity that moves with you from site to site. A mobile experience that ties offline shopping to the online profile is also in the works.

Besides Venkatraman, a former Deloitte consultant, entrepreneur and author, the other two co-founders are V Bespoke co-founder Dave Whittemore and software engineer Will Charczuk.  The team has an undisclosed amount of seed funding from Contour Ventures, as well as Mark Wachen, Ben Ling, DreamIt and others.

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