IBM tests augmented-reality shopping app

The new mobile application will scan supermarket shelves to find products that meet a user's criteria.

By Trefis Jul 10, 2012 12:55PM
TrefisImage: Full Shopping Cart in Grocery Store© Fuse/Getty ImagesResearchers at IBM (IBM)'s R&D Lab in Haifa, Israel, seem to have been inspired by JARVIS, the Artificial Intelligence construct in the hugely popular Iron Man movies.

The research lab has announced an augmented-reality smartphone application that helps users identify products based on personal criteria, such as dietary requirements, pricing, or packaging information. It can also be used to identify sales and special promotions of products.

The app is still in a prototype stage and has a huge advantage over similar applications as it does not rely on Quick Response (QR) codes and has adapted image-processing technologies used in facial recognition, color and shape-matching, and comparison to products nearby. It recognizes products based on pictures taken by the smartphone and then appends product details that the user requests on top of the picture. It can be used to scan multiple products at the same time and can process more than one product in an image, which is a significant improvement over scanning individual QR codes.


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Retail analytics opportunity

The app helps create a detailed profile of the user. The user can enter details to search, such as sugar or fat restrictions, allergies, pricing, and then simply scan a shopping aisle. The app will indicate which products meets the user's criteria.


Considering the size of most super- and hyper-markets and the vast choices available to customers, the ability of the app to zero-in on a product fitting user criteria could save a customer a lot of time. Shoppers also get quick access to product information available on the Internet.


Retailers can also use the app to provide incentives to customers with loyalty programs and digital coupons. The biggest opportunity lies in retail analytics as retailers can gain an understanding of customer preferences in real-time. This allows them to suggest related products in other aisles that the customer may be looking for. In short, this is like an Amazon product recommendation algorithm, but for groceries.


IBM claims that the suggestions will be tailored mainly for in-store use, and hence won't be intrusive or annoying.


We currently have a $223 Trefis price estimate for IBM, which is ~15% above the current market estimate.


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