Queue management system and checkout performance analytics
A queue management system continuously measures how many people are waiting at the checkout and how long they wait, then alerts staff when a set threshold is crossed. EscherSense does this with your existing cameras, producing queue length, average wait time and service time per till in real time, so the decision to open another till is based on data.
A shopper who sees the queue and abandons their basket never appears in a sales report. The loss is silent: the product goes back on the shelf, no receipt is printed, and the store manager can only guess what happened.
Once waiting passes a certain point, abandonment climbs quickly, which is why most stores aim to keep checkout wait time under three minutes. When the checkout area is measured continuously, opening another till stops being a judgement call and rosters follow the real demand curve. For the wider staffing picture, see staff productivity.
How it works
Queue length
The number of people waiting is measured separately for each till and queue zone.
Wait time
The moment a person joins the queue is matched with the moment they leave the till, giving average and longest wait times.
Thresholds and alerts
When a headcount or time limit is exceeded, an alert goes to the store dashboard, a radio or a phone. If nobody responds within the set time, it escalates to the next level.
Checkout and shift reports
Hourly wait times, service time per till and estimated abandonment are reported and feed directly into shift planning.
What it measures
Queue length
How many people are waiting at the checkout at any moment.
Average wait time
Time from joining the queue to leaving the till; the clearest measure of checkout experience.
Threshold breaches
How often, and at what times of day, the wait-time threshold is exceeded.
Service time per till
How long a customer takes to be served at the till; points to processing speed and training needs.
Abandonment estimate
A lost-sales indicator based on people leaving the queue during long waits.
Checkout efficiency
The hour-by-hour balance between open tills and queue load.
In-store use cases
Evening rush at a supermarket
After work, the queue can grow within minutes. As soon as the threshold is crossed, floor staff get an open-a-till alert and a second checkout opens before baskets are abandoned.
A single-till fashion store
If checkout waits regularly exceed the threshold at certain times, that points to an extra till or a shifted roster. The report shows exactly which days and hours need cover.
Spotting cashier training needs
When some tills show markedly longer service times under the same load, it can signal a process or training gap. The assessment is made by checkout point and process, not by individual.
Food and beverage order points
When the order queue grows, extra staff are directed there immediately. End-of-day reports show which hours need permanent reinforcement.
Privacy and GDPR/KVKK
Queue measurement produces only headcounts and durations; who is waiting is never known and never needed. No facial recognition is used, no identity data is stored, and cashier performance is reported by checkout point rather than by person. By default, video is processed on a server inside the store and raw footage never leaves the premises; if your IT policy prefers, processing can run in the cloud instead. Retention periods are defined, and the setup is designed around KVKK and GDPR requirements.
Setup and integration
All you need is a camera angle that covers the checkout and queue area, which most stores' existing security cameras already provide. ONVIF-compatible IP cameras with an RTSP stream are supported. Alerts can go to the store dashboard, radios or phones and warning lights, while data is published via REST API, webhooks and MQTT to your POS, workforce management software and BI tools.
Frequently asked questions
How does a queue management system work?
It measures the number of people waiting and each person's wait time continuously from the checkout camera. When the set threshold is exceeded, the store manager and staff get an instant alert. The same data also feeds hourly reports and shift planning.
What should our checkout wait time target be?
For most stores, the target is to keep checkout wait time under three minutes. Beyond that, the share of shoppers leaving the queue rises sharply. Thresholds are tuned to your own store's data during the pilot.
Who receives the open-a-till alert, and how?
Alerts can be sent to the store dashboard, staff phones or radios, or a warning light. An unanswered alert escalates to the next level after a set time. Which threshold notifies whom is configured per store.
How much can queue management reduce wait times?
Pilot stores recorded a 34% drop in average wait time. Results depend on the number of tills, staffing and traffic in each store. You see the effect in your own store with real data during the 30-day pilot.
Are individual cashiers monitored?
No, measurement is done at checkout-point and process level. Service time and processing speed are reported per till, with no facial recognition or identity matching. The aim is to understand the checkout process and training needs, not to watch individuals.
Which systems does queue data integrate with?
Queue data works with POS, workforce management software and BI tools. Events are published via REST API, webhooks and MQTT, and reports can be exported as JSON or CSV. Contact us for integration details.
Related modules
People counting and store conversion rate, from your existing cameras
People counting is the automatic counting of visitors entering and leaving a store from camera video. EscherSense does it with AI on the security cameras you already have: it separates entries from exits, removes staff from the count and matches net footfall against POS transactions to give you the store's true conversion rate.
Explore module →Retail staff productivity and workforce planning analytics
Retail staff productivity analytics measures how employees' time on the floor splits between serving customers and idle time, so rosters can be built around real footfall. EscherSense distinguishes staff by their uniform without identifying anyone; what is assessed is the shift pattern and store process, never the individual employee.
Explore module →Retail heatmaps and customer path analysis
A retail heatmap is a colour-coded view of how much time shoppers spend in each zone of the sales floor. EscherSense builds it anonymously from your existing ceiling cameras, and its path analysis reveals the most common routes from entrance to checkout, movement between aisles and the dead zones that almost nobody visits.
Explore module →This module is also covered in the solution family of CX Teknoloji, the team that builds EscherSense: cxteknoloji.com/en/cozumler/kuyruk/