Experts cover key points of computer vision in retail

Computer vision and artificial intelligence are innovations that businesses are often skeptical about.

Thanks to solutions like Goods Checker, businesses can obtain real-time overview of store shelves and make decisions based on accurate, complete and latest data,”

— experts from IBA Group conclude.

Computer vision and artificial intelligence are innovations that businesses are often skeptical about. Experts from IBA Group spoke about computer vision benefits for the companies through the example of merchandising processes automation: creating and checking planograms, generating analytics.

Planogram is a significant contributor to merchandising. It determines whether the buyer will notice a particular product among all the goods on the shelf. Indeed, according to statistics, 80% of decisions to purchase a product are made by people standing in front of a shelf in a store.

Amongst top grocery retailers, planogram compliance typically falls below 50%. This means that companies do not know how their product is actually displayed. At the same time, full planogram compliance resulted in almost 8% sales lift. This percentage is lost profit for others.

Image recognition through computer vision-powered IT solutions helps improve planogram compliance. The Goods Checker service, which is rapidly developing in the European market, allows streamlining the planogram compliance process. This is an innovative ecosystem based on computer vision to automate merchandising processes: create planograms, compare layouts against planograms and generate analytics.

In a store, product recognition process is as follows: merchandisers take photos of products on the shelf. Next, the photos are uploaded to the server, where artificial intelligence analyzes them. Neural networks recognize images, that is, products, and determine whether they are displayed correctly on the shelves. Processing of one photo takes up to 30 seconds, and recognition accuracy is over 95%. If the shelf does not fit into the frame, Goods Checker is able to merge multiple photos to get one image of the entire shelf.

After recognition, the tagged photo is immediately displayed in the merchandiser’s app, and its data is added to analytics available via a web browser. Goods Checker gives you a complete picture of the store performance by calculating various KPIs that can be customized according to business requirements: planogram compliance percentage, product availability, shelf share, etc.

Deploying computer vision is easy. IT companies often offer their SaaS solutions. This ensures that companies can test the product and see if it is appropriate for their purposes.

Dzmitry
IBA
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