AI Queue Management System: How to Handle Long Lines of Customers at Supermarkets

Nobody likes waiting in lines. According to research, people spend from 3 to 5 days per year queuing up. That might add up to 6 months of waiting for things during a person’s lifetime!

Long queues at the supermarket cause shopper fatigue, decrease customer experience and result in shopping cart abandonment.

Although retailers turned so many business processes into exact science, queue management remains a mystery. Just ask a store manager what is the average number of items scanned by cashier per minute and you will get a precise number. Ask the manager how much time customers wait in line during peak hours on weekdays or what is the average size of the queue – and you will get a puzzled look in return.

Introducing Computer Vision Powered Queue Management Platform

Time to checkout is fundamental in optimizing retail store performance, as it affects so many performance indicators. Token based queue management platforms can help quantify the wait times and customer numbers. However, they are ineffective in retail environments such as line management in grocery stores and supermarkets. These crowded retail environments require a different approach.

Enter the world of computer vision powered queue management software. Computer vision turns video footage into actionable business analytics. The platform uses video surveillance cameras already installed at the checkout location and requires no extra hardware investment, as usually the checkout area is well surveyed.

The platform uses AI and machine learning algorithms to track individual customers until they checkout, identifying their wait time as well as the size of the queue at any given time. It can also identify cart abandonment actions, giving an exact estimate of revenue lost due to long lines.

The smart queue management platform brings numerous benefits for retailers.

1. Improve retail performance analytics

Video analytics driven queue management platform will help to estimate time-to-checkout at any given time. The platform will automatically calculate the wait during peak hours, identify service bottlenecks when customer wait is the longest. It will help to establish a baseline for normal checkout operations. Store manager can filter these results by time of day or day of the week, holiday periods, or filter events by the wait time (e.g. show all events when customer wait was longer than 5 minutes). It helps to identify outlier events and analyze them with corresponding video footage. By combining queue size with sales data, conclusions can be made on revenue lost due to long wait times.


2.Accurately identify shopping cart abandonment

Using video analytics, the queue management platform can accurately identify shopping cart abandonment actions. The data can be correlated with average wait time and queue size (e.g. if wait time is longer than 5 minutes, 10 carts will be abandoned per hour). By adding sales data to the mix, the value of revenue lost can be estimated.

3.Improve employee shift scheduling

Cart abandonment numbers and longest wait to check-out analytics will provide insights on employee shift planning. Maybe your retail store does not follow a simple pattern of peak hours. Perhaps a sell-out campaign resulted in increase of customer influx. Or maybe too many checkout counters are open when few customers are present. AI queue management software will help to align your employee resources with the customer flow.

4.Improve customer experience

Customer service standards look great on paper. But to ensure they are followed to the letter in the real world is a different matter. By improving employee resource planning and ensuring a quick checkout will result in better customer experience. Less time waiting for things – more time to enjoy them!

Want to better manage queues and improve in-store experience? Get in touch with us.

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