Retail

Customer Tracking

Strategically placed counting devices throughout a retail store can gather data through machine learning processes about where customers spend their time and for how long. As a result, customer analytics can improve retail stores’ understanding of consumer interaction and improve store layout optimization.

People Counting

Computer Vision algorithms are trained with data examples to detect humans and count them as they are detected. Such people counting technology is useful for stores to collect data about their stores’ success and can also be applied in situations regarding COVID-19 where a limited number of people are allowed in a store at once.

Theft Detection

Retailers can detect suspicious behavior such as loitering or accessing areas that are off-limits using computer vision algorithms that are autonomously analyzing the scene.

Waiting Time Analytics

To prevent impatient customers and endless waiting lines, retailers are implementing queue detection technology. Queue detection uses cameras to track and count the number of shoppers in a line. Once a threshold of customers has been reached, the system sounds an alert for clerks to open new checkouts.

Social Distance

To ensure safety precautions are being followed, companies are using distance detectors. A camera tracks employee or customer movement and uses depth sensors to assess the distance between them. Then, depending on their position, the system draws a red or green circle around the person.

Productivity Analytics

Productivity analytics track the impact of workplace change, how employees spend their time and resources, and implement various tools. Such data can provide valuable insight into time management, workplace collaboration, and employee productivity.

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