Computer Vision in the Grocery Industry: Use Cases and Solution Considerations

Updated: Apr 16, 2021

Digital transformation in the grocery industry has accelerated due to a combination of factors including shifting customer preferences, fierce competition, and the current pandemic. Grocery retailers are increasingly testing and deploying cutting-edge technologies including robotics and artificial intelligence to improve the customer experience, manage inventory and reduce theft. Computer vision – a type of artificial intelligence – is one of the innovative technologies being used to reinvent the shopping experience across retail, including the grocery segment.

The Multiple Use Cases for Computer Vision in Grocery

Amazon Go is a prominent example of the use of computer vision in grocery, where customers can shop and leave the store without the need to check out with a cashier or self-checkout station. Computer vision is used to detect the items taken from or returned to shelves, and items are tracked in a virtual cart.

Computer vision extends further into additional use cases that help grocery retailers gain deep customer insights, improve the shopping experience, and create safer, healthier spaces. These use cases include:

End cap measurement: Using sensors to detect customer engagement with end caps in real-time, grocery retailers can gain up-to-the minute insights about customer engagement with this high-value real estate. Using a variety of metrics, grocery retailers can provide brands with robust and accurate data about shopper engagement with end caps and increase the revenue generated from these areas of their stores. Example metrics include:

  • Dwell time: how long a customer spends at the end cap

  • Engagement: the percentage of shoppers who engage with the end cap for a set amount of time or more

  • Shopper type: determining the percentage of shoppers who engage with end caps are stocking up vs. purchasing a few items can help retailers decide on the types of items that will perform best on end caps

Queue time management: Computer vision can also be used to monitor queue times in real time, helping grocery retailers create more effective staffing plans and improve overall customer satisfaction based on weekly and daily trends. Even further, the technology can be used to alert store management when store traffic suddenly increases, allowing managers to adjust staffing more quickly and reduce customer wait times.

Health and safety: With the current Covid-19 pandemic, maintaining a safe and healthy retail environment is critical to providing peace of mind for customers and employees. Computer Vision can also be used to take the burden of compliance away from store employees. Ways that grocery retailers can leverage this technology for health and safety include:

  • Mask detection: kiosks enabled with computer vision can be placed at store entrances to detect which shoppers aren’t wearing masks and alert them to use one

  • Occupancy: sensors can detect the number of customers in a store and alert them if the store is at capacity through digital signage

  • Physical Distancing: sensors can also detect if customers are complying with physical distancing guidelines and alert them to adjust their behavior if needed

Key considerations when evaluating computer vision solutions

Computer vision is a powerful technology that can provide deep insights to help transform your business. When evaluating computer vision offerings there are several important questions that you and your innovation team should consider, including:

· How flexible is the platform and how many metrics can it track?

· What hardware is required and how easy is it to install?

· How easy is it to update the platform to accommodate additional metrics?

· How does the solution address privacy and security?

· How quickly can I deploy the solution in all my locations?

· How can I consume the data that the solution provides?

Answering these questions can help you understand how the solution will integrate into your store environment and its potential ROI.

End-to-end Computer Vision from Nomad Go

Nomad Go is the first end-to-end computer vision solution that is designed to accelerate time to value, deliver real-time, actionable insights and provide a faster ROI compared to traditional computer vision solutions. Built for rapid deployment and out of the box insights, Nomad Go’s platform is extensible to accommodate a variety of metrics, including the ones described above. Using smart devices that deploy in minutes, Nomad Go is scalable and cost-effective, with security and privacy built in. Real-time data is made available through a dashboard, allowing operations and marketing teams to gather insights for improvements in marketing campaigns, customer experience and more. Alerts also be pushed to mobile phones and digital signage for both customers and employees.

Ready to see how Nomad Go can help accelerate your digital transformation initiatives? Contact us at to speak with one of our computer vision experts.

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