Watch all the Transform 2020 sessions on-demand here.
When you have millions of choices, shopping isn’t easy. That’s why Trendage is using artificial intelligence, visual search, and crowdfunded fashion expertise to help retailers make the right recommendations for shoppers.
Trendage is a data-driven style platform that is coming out of stealth today with Automated Product Recommendations for retailers. The company has raised $1.5 million in angel funding.
It draws on a combination of AI, visual search, and a community of trendsetters to generate more than 10 million style recommendations a month for apparel, accessories, and footwear retailers. Trendage can highlight what items pair well together based on a shopper’s age and regional trends to increase average order values and conversions.
Trendage has also identified 216 core body types for shoppers and will enable anyone to create their personal avatars with a selfie and matching body type on its outfit game Style Challenge. The game is a consumer product that also solves the problem of gathering consumer style preference data and powers Trendage’s insights.
June 5th: The AI Audit in NYC
Join us next week in NYC to engage with top executive leaders, delving into strategies for auditing AI models to ensure fairness, optimal performance, and ethical compliance across diverse organizations. Secure your attendance for this exclusive invite-only event.
The company was cofounded by Vineet Chaudhary, CEO; Roya Ansari, head of business development; and Mohammad Ahmad, head of operations. The three have worked together for over 12 years.
Funding comes from investors in retail, technology, and fashion and includes Bhupen Shah, cofounder of Sling Media; Ilaria Galimberti, cofounder of Impressa Hong Kong and O’ahu Sport; and Nooshin Esmaili, founder of Sutro Footwear and ShoeBiz SF.
Above: Trendage crowdsources results from a fashion game to make retailer recommendations.
Style Challenge is available on mobile and desktop platforms and enlists millions of community members to determine what clothes, accessories and shoes from leading brands work together. It then builds various outfit combinations on virtual models that are shared and rated by the Trendage community.
In January 2018 alone, Trendage’s community created more than three million customized outfits. Trendage uses machine learning to automatically generate data that helps customers “complete the look” based on the choices of the individual’s community.
This process yields recommendations for popular clothes, accessories, and shoes that retailers can use to personalize product pages and email marketing campaigns and lets them suggest frequently paired items within a shopper’s age and region.
The company can also provide a report to help retailers better predict style trends in the fashion industry and avert costly inventory mistakes.
“Retailers are struggling to find ways to compete with online giants and fast-growing mail-based startups that have massive data,” said Chaudhary, in a statement. “The challenge of making sense of all the various data points gathered from website views, email campaigns, sale and return data, however, is that the data is often not available until it’s too late to impact a shopper’s decision.”
He added, “By the time the data is ready, the season and trends have changed. 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.”
The Santa Clara, California-based company was founded in 2015 and has eight employees.
