Problem
Buyers comparing retail video analytics software see feature lists that look identical: people counting, heatmaps, and “AI alerts.” The real differences show up in false-positive rates, evidence quality, and whether analytics improve store operations—not just security.
Business impact
Choosing a tool that only counts traffic wastes camera investment. Stores still miss queues, shelf gaps, and shrink events that affect conversion and margin.
Current approaches
- Legacy VMS analytics plugins
- Point people-counting sensors
- Security-only AI appliances
- Separate tools for LP, ops, and facilities
Why existing methods fail
- Security-only tools ignore queues and cleaning
- Counting without store context creates vanity metrics
- Siloed tools block executive visibility
- Hard to prove ROI without operational outcomes
How AI solves it
- One platform maps cameras to retail workflows: checkout, floor, stockroom, entrance
- Events become Operations Scores and recommendations leaders can act on
- Evidence clips close the loop for LP and store managers
- Existing CCTV keeps deployment cost predictable
With AnomaAI, detections become business events with evidence—so teams review what matters and leaders see outcomes, not camera walls.
Business outcomes
- Clear evaluation scorecard
- Faster shortlist decisions
- ROI tied to shrink, queues, and labor
- One vendor path for security + operations
Frequently Asked Questions
What should be on an RFP?
Camera compatibility, zone mapping, event evidence, multi-store dashboards, false-positive handling, and data retention/governance.
Is heatmapping enough?
Heatmaps are directional. Buyers need event timelines and outcomes—queue time, empty shelves, exception reviews.
How does AnomaAI compare?
AnomaAI focuses on operational intelligence from existing cameras: detections → business events → decisions—not vanity analytics alone.
Related use cases
Next step
Ready to see this on your cameras? Book a demo or explore Book a Retail AI demo.
