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.