“Queuing units” are among the most challenging elements to identify and track accurately, as customers continuously join and leave and queue lines constantly change shape. To address this, Vionvision has developed cutting-edge AI algorithms that measure real-time queue length and waiting time across a wide range of real-world scenarios. By tracking wait duration by each line or counter across different time periods, the system helps managers identify peak-time bottlenecks and optimize staff scheduling and counter operations—reducing congestion and improving the overall customer experience.
This metric measures how long staff spend serving each customer in average and how many customers are successfully severed within a given time period. By analyzing service duration across different hours, days, and service counters, retailers can clearly understand staff productivity, counter efficiency, and peak service demand. These insights help optimize staffing levels, improve workflow design, reduce waiting times, and ensure customers receive timely assistance during busy periods.
In the self-checkout areas, where the flexible and varied shape of the queue places requires more intelligent queue analysis system, queue management system is able to automatically identify free-form queues and accurately measure waiting and service times.
This metric measures the number and percentage of customers who leave a queue or service area before completing their purchase or receiving assistance. By tracking the abandonment rate over time, retailers can evaluate service quality, identify long wait-time risks, and pinpoint peak-period pressure points. These insights help optimize staffing, improve queue management, and reduce lost sales caused by frustration or excessive waiting.
