How Stitch Fix used AI to personalize its online shopping experience

“A key tenet of our personalization model is that the more information clients share, the better we are able to personalize their recommendations. We are usually able to adapt the model based on feedback from our clients; however, rules-based systems aren’t generally adaptive. We need the system to learn from client feedback on the outfits it recommends. We’re receiving immensely helpful feedback, from how clients engage with the outfit recommendations and also from a custom-built internal QA system.

Style Profile: When a client signs up for Stitch Fix, we receive 90 different data points — from style to price point to size.

Feedback at checkout: 85% of our clients tell us why they are keeping or returning an item. This is incredibly rich data, including details on fit and style — no other retailer gets this level of feedback.

Style Shuffle: an interactive feature within our app and on our website where clients can “thumbs up” or “thumbs down” an image of an item or an outfit. They can do this at any time — so not just when they receive a Fix. So far, we’ve received an incredible 4 billion item ratings from clients.
Personalized request notes to Stylists: Clients give their Stylists specific requests, such as if they are looking for an outfit for an event, or if they’ve seen an item that they really like.”

https://www.google.com/amp/s/venturebeat.com/2020/07/05/how-stitch-fix-used-ai-to-personalize-its-online-shopping-experience/amp/

Leave a Reply