Data-Driven Style Recommendation Platform Launches

In the US, a data-driven platform called Trendage has been launched with $1.5m funding, to automatically generate style recommendations based on a shopper's age and regional trends.

The platform uses artificial intelligence, visual search and a community of millions of trendsetters to provide insights on customer apparel preferences, and highlight which items look best together. Trendage has also identified 216 core body types and enables anyone to create their own personal avatars with a selfie and matching body type on its outfit game 'Style Challenge'. This game enlists millions of community members to determine which clothes, accessories and shoes match, building various outfit combinations on a virtual model that are shared and rated by the Trendage community. The platform then uses machine learning to automatically generate data that helps customers 'complete the look', based on the choices of its community.

Angel funding comes from investors in retail, technology and fashion that include Bhupen Shah, co-founder of Sling Media; Ilaria Galimberti, co-founder of IMPRESSA Hong Kong and O'ahu Sport Ltd; and Nooshin Esmaili, founder of Sutro Footwear and ShoeBiz SF. Commenting on the launch, Trendage co-founder and CEO Vineet Chaudhary explains: 'The challenge of making sense of all the various data points gathered from web site views, e-mail campaigns, sale and return data, is that the data is often not available until it's too late to impact a shopper's decision. Trendage gathers all the same data without ever having to touch a single item of clothing, or receive a return, giving retailers an important time advantage of leveraging current trends just when they need it most: at the point of sale while customers are making critical purchasing decisions. No other platform makes cross-sell data as readily available'.

Web site: www.trendage.com .