“By tracking those cultural trends and how different people’s styles are similar or different, we can use that data to inform other people’s recommendations,” said Bunbury. Rather than let User A’s recommendations stagnate, Blend will use User B’s data to inform recommendations to User A. User A pauses engagement with the app, while User B continues to engage regularly, and sees her feed adjusted according to new trends. So, for example, let’s say there are two users who were actively using the app three months ago. On the back end, Blend is comparing products and users to get a statistical picture of which products will be right for which users. The more a user interacts with the app, the more personalized their recommendations will become. Blend uses all of that data to form a picture of the user, who has already pre-set preferences to size and budget. Their feed will also feature short-form videos and product curations from influencers who can earn an affiliate commission on any sales they generate.Īs the user scrolls, Blend collects data on how they interact with the app, whether they’re liking products, saving them, sharing with a friend, “or simply how long you’re looking at one product,” according to Bunbury. When the user opens the app, they’ll scroll through a feed that is a mix of product imagery and descriptions that have been pulled from different retail and e-commerce sites. “The main thing that’s always important when it comes to AI is what data you are actually putting into ,” said Bunbury, noting that the founding team decided on an app rather than a web page in part because it’s easier to track a user’s data that way. In the world of fashion, this means it can better understand user preferences and make tailored clothing recommendations. Transformer technology, which makes up the tech stack of popular generative AI models like ChatGPT, is a model for teaching computers how to understand and generate human language. There are also applications in production improvement, trend forecasting, inventory management and virtual try-ons.īlend’s approach centers around transformer technology and recommendation algorithms, powered in large part by user interaction data. Others are using image generation to create new designs. Some companies are using natural language processing algorithms to improve the customer service experience. The fashion industry has tapped the generative AI frenzy in a range of ways. “Ultimately, the vision is really to be the front door for every online shopping experience, and therefore, to be the largest-scale retailer because of that ability to personalize and only present people with the 1% of the internet that is most relevant to them.” Generative AI we can get behind “We hope that by attracting first the very fashion-forward, trendsetting crowd, we can then move more mainstream from there, but it’s much more difficult to go the other way round,” said Bunbury. first and then hopes to move into the U.S. The startup’s go-to-market strategy targets users aged 18 to 34, “very digital, native mobile-first shoppers” who are starting to define their personal style as they accrue disposable income. The startup will use those funds to build out additional features on the app and push for a full-scale launch.īlend has already signed on over 250 retailers, including Net-a-Porter, a luxury retailer. After raising angel investment in April, Blend is now on the hunt to secure investors for its seed round. At the event, the startup launched its MVP - an app that will slowly open to the 2,000 users on Blend’s waiting list. “When trends are changing relatively quickly, and people’s style does change over the course of their lives, it doesn’t stay relevant for a user to have such historic recommendations.”īlend participated in TechCrunch Disrupt 2023 as one of the Startup Battlefield 200 companies. “The vast majority of retailers do absolutely no personalization, and in the instances when they do, they only personalize according to historic purchase data,” Blend co-founder Jemima Bunbury told TechCrunch. But anyone who has found themselves two hours deep into a fashion rabbit hole, with nothing to show for it but 15 open tabs, four full shopping carts, an earful of YouTube clothing haul reviews and the gnawing anxiety of the overwhelmed, shopping online can feel like a chore.Įnter Blend, a U.K.-based startup that is using AI to cut through the noise and help shoppers find personalized product recommendations to suit their style, budget and size. Shopping for clothing online has liberated us from the need to brave the endless aisles, fluorescent lights and sale-hungry crowds of the brick-and-mortar retail inferno.
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