2018 — 2019
StyleSnap is a new visual search experience launched in the Amazon App to help customers easily discover inspiring fashion products on Amazon based on pictures they see posted by brands and influencers on social media feeds or from any other website. Customers can upload photos or screenshots of inspirational looks, or explore the looks of Amazon Fashion Influencers, to find visually or aesthetically similar styles on Amazon.
Goal: Design an intuitive experience that takes the guesswork out of shopping from inspiration using AI, Android and IOS.
Users: All Amazon app users.
Why: Customers struggle to find products from their visual inspirations that inspire purchases (i.e. pictures on social media, fashion bloggers) and some pictures lack sufficient information making them have to go through a major filtering, sorting, typing and re-typing exersise, to figure out how to describe what they are looking for. Leveraging machine learning, we can identify the visual characteristics of an image such as pattern, color, length etc, to provide customers similar looking products based on their inspirations.
Role: Lead designer, UX/UI design, prototyping, user research, product vision and strategy.
Process: My role as Design Lead was to work collaboratively with design, science, computer vision, product management and engineering to deliver an intutive end-end flow. I worked with partner teams in Amazon Fashion and Amazon Search, to surface best practices around the display, filtering and saving of products, as well as led research for identifying motivations for using photos in the shopping journey.
Outcome: StyleSnap is live today in the Amazon mobile app. It is also a 4 year effort for me to push visual search for fashion into the Amazon App since my work as an intern on Amazon Visual Fashion Finder.
We wanted to encourage customers to upload their saved photos from their camera roll and found screenshots to be a common behavior. These starting points take priority in the interface. However, users may not always have a photo or screenshot ready, so we worked with the Amazon Influencer Program to surface influencer images that have a variety of genders and body types as well as exact products recommended by the influencer as another starting point.
Using deep learning we are able to recognize and visualize the different aspects of the photo that have products in them. Customers are able to navigate the large preview of the image they uploaded to see what the most similar styles available on Amazon. It was important to balance the ability to see the original inspiration clearly against showing products, so the customer can visually compare, pivot to other aspects of the image or swipe up if they want to dive deeper.
PRODUCT THUMBNAILS AND FILTERS
Once customers scroll to full screen, they can toggle thumbnails between the different aspects of the photo to see all the products available within a few seconds. The filters surfaced are the most important for the category, letting customer know which gender was recognized automatically along with Prime, Price and Size. The product grid is also optimized for the category, ensuring large images to compare to the original inspiration thumbnails.
IMPROVED INFLUENCER IMAGE DISCOVERY
My last work on the project was improving the discoverability of influencer looks. With the improved design, customers can navigate from the camera as they would through a fashion experience. Instead of presenting an organic grid of photos, we present 4 gender and category based options to remove cognitive dissonance. Once more personalized, they can continue to narrow down with category filters or start discovering products from the influencers.
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