Angel / Pre-seed stage MVP eCommerce » Apparel & Accessories
We are about to finish our prototype test with over 120 users, which allowed us to collect substantial data to validate our AI as well as consumer feedback.
Launched our prototype app with friends and family to begin validation testing.
Trained AI with 90% accuracy in determining whether a user would like a product.
AboutA fashion marketplace that allows consumers to find apparel based on style preferences. We help brands connect with their target audience.
Annual online fashion spending amounts to $90 billion in the US, $20 billion of which is from millennials with a per capita spend of $400. There was a 54% increase in mobile shopping from 2016 to 2017. Twice as much time is spent shopping on mobile rather than desktop, but only one-third as many purchases are made on mobile. We see an opportunity here to provide a true 10x improvement to the emerging mobile-only shopping paradigm by providing a hyper-personalized experience to shoppers and a strong affiliate partnership for merchants. We plan to initially launch at UCLA and USC, a market of nearly 100,000 college millennials and $40 million in online fashion spend.
Online shoppers are overwhelmed with too many, irrelevant choices- and one-third of them leave the site without making a purchase. While personalization increases conversion by up to 200%, less than 10% of tier 1 retailers say they are effective at it. For smaller brands, it's an even more daunting task and their ability to drive traffic to their sites suffers.
Our marketplace automatically connects shoppers and brands based on their mutual fashion sense. Consumers can discover products and brands they love, increasing conversion for the brands themselves. Users log in to our app with their social media accounts and our suite of proprietary deep learning algorithms interprets their style preferences from their images. Products from Shopify merchants are then polled to find relevant items that the user is likely to convert on and are displayed in a Tinder-style UX.