Buying clothes online is convenient and sometimes even fun. What’s fun for customers translates to some serious cash for retailers. The 8-12 percent projected growth in the eCommerce industry this year could result in as much as $443 billion in sales. The only problem, though, is that almost a third of all products bought online are returned. In women’s retail alone, returns are a $30 billion cash sink. Savitude is a service that uses a machine learning-based SaaS software to help eCommerce users find clothes that are a perfect fit, so you don’t miss the trial room when ordering an outfit online.
To make integrating its features simple, Savitude works in conjunction with existing eCommerce websites. The service launched in San Francisco partnering with clothing company JAKE. The proprietary software works on two different levels–one on the retailer side, and the other on the customers’ side.
With retailers, the primary objective is to create a knowledge base around available inventory. This is where the fashion designers and software engineers on the team combine forces to find efficient ways to classify each piece of clothing. The team currently factors in as many as 9,000 rules as part of the process to classify any user into one of nine body types. The software uses machine learning to scale gathering and classifying all that information, which is currently collated in a patent-pending Knowledge Base.
Savitude next comes into play when a potential customer accesses a retailer’s website. At this stage, the customer is asked a series of question to get an understanding of their body shape. The questions relate to specific parts of the body and include images from which customers can pick the ones most relevant to them. Once it susses out a user’s physical features, Savitude matches her with clothes it thinks would fit her best. So instead of being overloaded with choices–some of which aren’t even relevant–customers see a personalized, curated feed of clothing and apparel.
The data that Savitude gathers isn’t just of use when customers are browsing clothing collections. Foreseeably, data relating to body types and purchasing behaviors could be leveraged by clothing companies to tailors their inventories based on the preferences of their customers. The startup also plans on offering their software to brick-and-mortar stores so finding clothes offline becomes easier as well.
Savitude was founded by Camilla Olson, an inventor who has experience both in the fashion industry as well as creating technology companies. The startup has raised $135 million in a single round of Angel funding so far.
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I think the app would need a ton of data, as you mention, for it to work properly. How many people are willing to spend that much time with an app just to find a pair of jeans or two that might fit when they arrive?
Ecommerce is huge online, more than ever. But buying clothes that MUST fit my body the right way online, that is not happening. I am not convinced from the few times I’ve tried that it will work to my benefit.
Yeah, it is, but just like the article stated, 1/3 of all products are returned. If companies cannot figure out how to keep that from happening, there is no point to sell online.
LOL, I do not care what anyone says, there is no way that an application or search engine is going to find the perfect fit of clothes for me. Everyone lies about measurements anyways 🙂
Clothes shopping online has always been a big no-no for me.
I guess it would be nice to narrow down your online search to just the stores that have clothes that will fit you.
I think that is the main benefit. I am guessing that once a person goes through the entire process of filling in any needed info, the search is going to bring back few to no options.
For those that have always had to try real hard to find clothes that fit, this is not really going to work for them because they are likely to be of abnormal shape. For the rest, that can fit into anything on any rack, this is a nice addition to your smartphone app inventory.