Smart retail assistant finds the right fit without store visits

A new app is a database of high street fashion brands that suggests accurate sizing for items based on a users’ actual size and preferences.

Retail is a constantly changing industry as tech companies gather more data on how consumers browse, both online and in brick-and-mortar stores. At Springwise, we have seen first hand the wave of disruption in the retail fashion industry with an AI search tool and peer-to-peer rental service. When it comes to fashion, however, both real world and online shopping have a drawback. Not all sizing charts are created equal. What one outlet says is a large might be a medium to some individuals.

EyeFitU is an app designed to solve this problem. It works like a smart assistant for fashion. Users create a profile on the app. This includes their measurements and preferences, if they like a slightly loose fit for example they can include this as an option. It then works two ways. First, users can browse a database of online high street retailers, such as H&M and Bonobos. They then select an item they like and EyeFitU quickly recommends the size that will actually fit the user’s body. Alternatively, EyeFitU can recommend items to users, based on the latest trends. Overall, EyeFitU aims to prevent the amount of time users spend returning items that don’t fit . EyeFitU uses Machine Learning to improve the information provided by the mass of data from consumers.

The app can also be used in-store. When a user finds an item they like, they look it up on the app and find the size they need. There’s therefore no need to visit the fitting room or make a return trip to exchange the item. In addition, users can set multiple profiles on their app. This enables users to buy clothes for friends and family, without having to include the receipt. EyeFitU is free for users and is available on both the app store and Google Play. The company has recently announced the launch of its B2B platform that is now available for integration with online stores through a SaaS model. Through user-generated content, the platform can cross-reference consumer sizing preferences helping retailers to reduce return rates and dead inventories.