As many brick and mortar stores have already built and integrated online platforms, other digital-native brands who gained popularity online are opening physical shops, not with the purpose of directly selling their products, but simply providing retailtainment in the form of refreshments and pick-up services.
The physical encounters with customers can reveal great opportunities in terms of better understanding of customer’s behavior and preferences, opening to a more accurate services customization -: in a market where 62% of customers expect discounts based on purchase history, and 82% of millennials still prefer in-store shopping as a retail channel, enhancing in-store experience seems the way to go.
Real-time emotional and behavioral data have the power of providing important insights on how the brand impacts visitors, leading a company to make the most efficient decisions about its marketing tactics.
We created a mobile application capable to identify products in a store and automatically add them to cart based on computer vision and machine learning capabilities.
We used dedicated machine learning devices Neural Compute Stick with Intel Movidius VPUto track and identify products in a store.
The mobile app will use the camera to take pictures and identify products, using CNN pre-trained models with store products.
Once identified, products can be added to cart or bookmarked for later buying or sharing with friends. Bookmarked products will be visible in augmented reality inside the store, making customers’ life easier when it comes to immediately spot what they are looking for.