Machine Learning and Image Recognition in Retail

References to machine learning seem to be everywhere these days. From new university-level courses to magazine articles and social media posts, daily we encounter new information on artificial intelligence and machine learning. But how can those new technologies be applied to improve retail operations?

One of the main ways that machine learning can help retail is when it comes to quantifying data that previously came as individual pieces of information while surveying stores, such as images.
Image recognition in retail is one of the best examples of the use of machine learning in merchandising applications. 

Machine learning allows images to be fed by programmers into a mobile merchandising system in order to train a neural network to identify product faces on outlet shelves.

Then, field staff can simply take pictures of the store shelves on their mobile merchandising apps and the system will be able to identify - with accuracy that reaches 95% - the number of faces of the target product, as well as compare it to competitor brands and automatically calculate share of shelf.

Merchandising managers and other stakeholders can, in real-time, access the results of store audits, filtering the quantified data and generating reports and visualizations according to their needs on KPIs such as out-of-stocks, share of shelf, product availability, competitor availability, and promotions, among others, and make faster mission-critical decisions.

BrandML is the image recognition technology developed by VisitBasis, creator of the namesake mobile merchandising software chosen by many market leaders in the CPG and product distribution industries.

Want to know more about machine learning and image recognition in retail? Contact www.visitbasis.com and ask about the new BrandML technology.