Better Insights: How Retail can Benefit from Computer Vision

Image of a store.
Computer vision can enhance physical retail stores. From better in-store experiences to reducing shrinkage, computer vision offers plethora of improvements.

What is computer vision?

Computer vision analyzes images and video to identify and classify objects. First, computer vision algorithms train on an annotated dataset to learn objects. This dataset can either be manually collected or synthetically generated. Then, the algorithms analyze real imagery to generate insights.

Gather critical in-store KPIs with computer vision

Occupancy management is one of the holy grails of retail computer vision. Computer vision can derive three major insights into store occupancy:
  • How many people are in the store?
  • How do customers move through the store?
  • Which types of customers are shopping in the store?
The first metric, how many people are in the store, is straightforward to put in place. Apply a person-detection algorithm to a camera focused on the front door. You can use this to count how many people enter and leave the store, and from this you can derive peak usage times.
Picture of a busy store.

Computer vision can also help retailers understand how customers move through a store. Configure a rig of cameras (or use existing security cameras) to watch the entire store. Then, apply a machine learning algorithm which can track people across camera frames. As a customer moves around the store, the AI/ML algorithms will track them as an single individual.

In-store tracking is very valuable. Retailers can see which products are examined the most. This can be used to optimize the retail experience.

Individual tracking is the underlying data behind a retail heatmap analysis. Heatmaps lets you visualize where customers are spending time in a store. Individual tracking also enables flow analysis. Retailers can use algorithms to track a person’s entire path throughout the store. Flow analysis measures how optimal a customer’s path was throughout the store. Retailers can use this insight to reconfigure aisles as necessary.
Computer vision algorithms can also anonymously classify customers based on age and gender. This also useful for classifying families versus individuals. Retailers can use this information to understand their customer base. Ultimately, this will drive better outcomes for retailers.
The end result of combining customer profiles with flow analysis is quantifiable insight. With proper econometric analysis, retailers can use this information to improve their stores.
Moreover, these insights can lead to better customer experiences. For an optimal customer experience, the store arrangement also needs to be optimal. As retailers expand their in-store offerings, data can help drive their effectiveness. And the best way to capture this data is with computer vision.

Reduce shrinkage and improve public safety with computer vision

Shrinkage is a loss of retail merchandise attributed to theft, damage, or frauds. It is a significant problem for retailers. Fortunately, computer vision can address this problem.

Computer vision can help reduce theft by monitoring both employees and customers. Products taken off the shelves should match transactions at checkout. Retailers can ensure that customers are paying for the products they took. Stores can also verify that employees are conducing cashier transactions. Computer vision can help stem the over $100 billion lost each year due to shrinkage.

Customers also want to feel safe when shopping. That is why public safety is a major concern for retailers. Computer vision enables automatic weapons detection, and can alert employees as necessary. Automated doors can also deny people carrying dangerous weapons from entering the store.

Improving retail operational efficiency in real-time with computer vision

Retailers can better serve customers they understand their store in real-time. For example, are certain products out of stock on the shelves? How long is the current wait time at the checkout counter? Is there a customer who seems lost or unable to find the item they are looking for? AI can generate these insights in real-time for retailers.
Illustration of technology.

Lay a foundation for new technologies

No-checkout shopping is another trend that is gaining momentum is the retail sector. In 2020, Amazon opened its first grocery store that is completely cashier-less. It relies on computer vision technology to recognize a customer’s purchases. Walmart is also expanding its computer vision efforts. The company aims to track inventory and prevent theft with artificial intelligence. Computer vision is the critical technology that enables this.

What is great about technology is that it is always improving. As new opportunities emerge in retail, computer vision can update. That is why it is important for retailers to consider and get to know computer vision now. By the time they developed their system, another critical use case will appear. This way, retailers can position themselves for the next wave of transformative retail.


Computer vision is improving continuously, and the technology is becoming more compelling. Whether its tracking individuals or reducing theft, computer vision can be valuable. Improvements in operational efficiency will also help justify the expense. The future of retail stores is at stake. We can help retailers adopt computer vision. We’re also listed as a top computer vision provider. If you’re interested in working with us, please get in touch here.