Fashion AI is changing the industry forever
Jess Erdman, January 2021
If you’ve read any retail news in the past few years, you’ve likely heard lofty statements about how fashion artificial intelligence is taking over the fashion industry--and the consequences (either good or bad, depending on your optimism) for the entire fashion ecosystem, from supply chain to stores. In our latest article, we break down exactly what is AI in fashion--and why you should care, in 5 simple questions.
Everyone from large fast-fashion retailers like H&M to smaller brands looking to heighten the customer experience. In 2021, we expect to see an uptick in the number of retailers using fashion artificial intelligence. We also expect that fashion eCommerce will begin to integrate AI more deeply into their product strategies, as customers continue to shop online.
By using predictive analytics, businesses can understand their internal processes better, whether that’s tracking the supply chain or forecasting demand on product sales. Artificial intelligence can use past data, combined with machine learning, to try to predict future actions.
While predictive analytics can be implemented across industries, there are fashion specific uses for artificial intelligence as well. For example, brands are not only using fashion AI to predict trends but to re-think an experience that many of us dread: the fitting room experience. Companies such as Drapr are using fashion AI to create 3D models of brand’s catalogs, so the customer can visualize the clothing on her body type. Forma can create a virtual try-on experience from just one picture, using fashion artificial intelligence.
Fashion artificial intelligence is also changing the way brands create customer experiences. Through product recommendations and product search/discovery powered by AI, brands are completely rethinking the product search process from the point of view of the customer. Product recommendations can be personalized to a customer’s unique buying attributes
At YesPlz, our Style Filter pulls products for customers based on attributes that matter to them, such as clothing fit, neck style, and shirt style. Our AI is trained specifically to understand fashion-based attributes, so product search results are both accurate and highly relevant. By creating a universal fashion language that any customer can understand, we’ve enabled customers to visually demonstrate their style preferences, which in turn, guide our proprietary AI algorithm to deliver even better search results that are carefully curated for each customer. Try our free demo version of the Style Filter to see how it works.
One of the debates among designers and brands is whether AI can replace the design-work or trend-forecasting abilities of a human with real-life industry knowledge. Because AI is trained by people (for example, at YesPlz, our algorithm is trained by real customer interview data points), it is unlikely that human touch will be totally eliminated in the world of fashion and retail.
However, AI is amplifying the capabilities that humans have by allowing us to perform analyses at scale. For example, if you were to try to pull products on an eCommerce site that match a customer’s requirements based on size, fit, color, and so on, it would be tedious and possibly inaccurate, even with the help of software.
Through AI, we can go above and beyond, and pull relevant products for customers in seconds--products that are far more relevant to a customer’s unique search journey.
Over the past few years, retailers have increased their use of fashion artificial intelligence to make better business decisions and benefit customers. However, in light of the events of 2020, customers became increasingly more sophisticated online shoppers. Retailers met their needs by elevating the customer experience, from using AI behind the scenes to analyze inventory to making AI front-and-center in selecting relevant products.
The benefits of fashion artificial intelligence are abundant, from increased efficiency in inventory management to better management of the customer experience. While humans may struggle to provide around-the-clock customer support, many of these inquiries can easily be answered with an AI chatbot. And lastly, AI-powered product recommendations can keep customers on-site for longer, resulting in higher basket sizes, and increase overall conversions.
All of these elements point to one clear benefit: an uplift in customer satisfaction, which in turn, leads to increased repeat purchases.
Too many retailers are stuck with static, expensive product recommendation systems that aren’t delivering accurate, fashion-forward recommendations to customers. That’s why we decided to create the YesPlz Product Recommendation Engine.
By utilizing the Style Filter and YesPlz Product Recommendations, Lately and Kolon Mall were able to create the best-possible online shopping experience for customers.
It's hard to know what customers are really looking for, and the fashion taxonomy can complicate the search process even further. In this piece, we demystify the fashion taxonomy and explain how understanding customer search intentions can shape your eCommerce strategy.