Retailers Can Now Access Valuable Insights On Customer Apparel Preferences and Cross Sell Data In a Timely Manner

Trendage, a new data-driven style platform, officially announced the launch of its company, team, and product, Automated Product Recommendations for retailers. Using artificial intelligence, visual search, and a community of millions of trendsetters, Trendage automatically generates 10 million+ style recommendations per month for apparel, accessories and footwear retailers that 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.”

Trendage also announced its team which is comprised of three co-founders: Vineet Chaudhary, co-founder and technical CEO; Roya Ansari, co-founder and business development; and Mohammad Ahmad, co-founder and operations, all of whom have worked together for over 12 years. The company also announced that it has received $1.5M angel funding from notable investors in retail, technology and fashion that includes 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.

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Trendage’s insights are powered by its viral consumer product, Style Challenge, a styling game which solves the difficult problem of gathering consumer style preference data. The game, which is currently available on mobile and desktop platforms, enlists millions of community members to determine what clothes, accessories and shoes from leading brands match, building various outfit combinations on a virtual model that are shared and rated by the Trendage community. The game is immensely addicting and a fun way for shoppers to discover new products online in an engaging manner. In January 2018 alone, Trendage’s community created more than three million customized outfits.

Trendage then uses machine learning to automatically generate data that helps customers “complete the look” based on the choices of its community. The end result are recommendations for popular clothes, accessories and shoes matches which retailers can use to personalize product pages and email marketing campaigns with frequently paired items within a shopper’s age and region. This data for apparel and accessories is unique and provides a powerful competitive advantage. The company can also provide a report to help retailers better predict style trends in the fashion industry and avert costly mistakes.

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“Retailers are struggling to find ways to compete with online giants and fast-growing mail-based startups that have massive data. 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. 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. No other platform makes cross-sell data as readily available, which is why you don’t see it online. Cross-sell product recommendations have been mostly done manually so far. Trendage’s automation fixes this problem,” said Vineet Chaudhary.

Roya Ansari, added, “Brands often think they know who their core consumer base is, so they tend to tightly control how their products are styled and marketed. On the flip side, consumers like to stick with brands they are familiar with, and might not consider a brand that’s outside of their comfort zone. Trendage has come up with an ingenious way for brands to put their apparel in front of a broader audience, one they may have never thought of reaching, to learn how consumers might mix and match their items with other brands. It’s also a great way for consumers to discover new brands that they would have never found otherwise. It’s really a win-win for both sides.”

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