Queuence
Predict queues, alert lane opens, and cut walk-offs with staffing hooks.
Queuence is queue and staffing analytics that counts the people in every line through the cameras you already have, predicts the wait a few minutes ahead, and tells a manager to open a lane before customers walk off.
Who is this for?
Store operations leads in grocery and general retail, branch managers in banking, and operators of quick service restaurants with drive-thru and counter queues. It fits any site with security cameras covering the lines and a workforce management system to feed. It does not need facial recognition, and it stores no personal data.
What problem does it solve?
A walk-off is invisible in the sales report. The customer who left a full cart at 5:40 shows up as a slightly lower hour, not as a lost sale with a cause. Managers open lanes by looking up from the office when someone complains, which is ten minutes after the queue formed.
Staffing is planned from last year's roster rather than this week's pattern, so the busiest hour of the busiest day is the one with the fewest cashiers on shift. The data to fix this exists in the camera feed; nobody has been counting.
How does it work?
Computer vision counts customers entering and leaving each queue and measures dwell, then a model trained on your store's own patterns (rush hours, cashier speed, seasonal peaks) predicts wait time with 85 to 90 percent accuracy inside a two-minute window. Thresholds fire alerts to managers' phones, to overhead announcements or to digital signage that points customers to a shorter line.
The same counts feed your workforce management system. Queuence recommends lane openings from predicted demand, and the history flows into scheduling so the roster follows the pattern instead of the calendar. Abandonment is tracked with dwell time and trend analysis by day of week and time of day, which is the number that finally puts a cost on the queue.
False alerts are the failure mode we watch. Multi-frame validation and historical patterns filter them, the threshold is yours to set, and the false-alert rate is a core KPI on the dashboard, because an alert managers learn to ignore is worse than no alert.
Capabilities
- Real-time queue length & wait prediction
- Alerts to managers & signage
- Staffing planner integration
- Anonymized video analytics
- Abandonment tracking and trend analysis
Outcomes
- Wait time ↓ 20–40%
- Walk-offs ↓ 15–30%
- Prediction accuracy ↑ 85–90%
What does it connect to?
- Workforce management: Kronos, ADP, Legion and other WFM platforms, two-way
- Digital signage systems through REST APIs; SMS and app notifications to staff
- Existing security cameras covering the lines
- KPI dashboards with abandonment, dwell and wait trends
How is it deployed, and how long does it take?
Analytics are anonymised by design: counting and flow without identification, no PII stored, GDPR and CCPA compliant. Edge inference handles the real-time alerts; the cloud keeps the trends and the reporting.
Multi-zone tracking covers fitting rooms, checkouts and drive-thru lanes, and seasonal pattern learning means the first holiday season is the one where the forecasts are still settling in.
Proof of concept to pilot in 8 to 12 weeks with KPI gates. Wait times typically fall 20 to 40 percent and walk-offs 15 to 30 percent once lane-open alerts are trusted.
Industry context
Explore the related workflows and other solutions for your industry.