Problem
Traffic counts from door sensors are often incomplete, easy to game, and disconnected from what happens inside the store.
Business impact
Labor plans miss reality. Marketing cannot attribute campaigns. Operations cannot compare stores fairly.
Current approaches
- Beam counters at entrances
- Wi-Fi probe estimates
- Manual clicker counts
- POS transaction proxies
Why existing methods fail
- Entrance counts ignore dwell and zone occupancy
- Sensor drift and occlusion create silent errors
- Counts rarely link to queues or staffing actions
How AI solves it
- Vision-based counting at entrances and zones
- Occupancy context for aisles and departments
- Trends that feed staffing and merchandising
- Optional linkage to queue and shelf events
With AnomaAI, detections become business events with evidence—so teams review what matters and leaders see outcomes, not camera walls.
Business outcomes
- More trustworthy traffic baselines
- Better labor alignment
- Campaign measurement with less guesswork
Frequently Asked Questions
How accurate is vision counting?
Accuracy depends on camera angle, crowding, and calibration. Treat counts as operational estimates with confidence and review.
Related use cases
Next step
Ready to see this on your cameras? Book a demo or explore Explore Retail AI.
