THE PROBLEM
The Gap Between Assumed Reality and Operational Truth
Most retailers possess the “eyes” (cameras) but lack the ‘brain’ to process what they see. This creates a dangerous void in operational data.
Invisible Demand
No automated data feed for actual footfall or abandonment; operations rely entirely on manual sampling.
Lagging Decisions
Store performance views and insights are ‘a day behind’ at best, preventing real-time response to pressure.
The Excel Trap
Heavy reliance on manual collection and arbitrary assumptions leads to inconsistent, error-prone forecasting.
A Dedicated Data Acquisition Layer for WFM
Definition: An AI-driven system that consumes standard retail security video and converts it into structured operational signals.
Forecasting
Real-world demand sensing
Scheduling
Alignment with actual service dynamics
Intraday Management
Real-time response to queue pressure
Real-Time Visibility via V-WFM Connect
From Gut Feel to Live Telemetry
Current Traffic
142
Avg Queue Time
4.2m
Staff Coverage
94%
Abandonment Risk
Low
WFM Connect
Demand vs. Forecast
Real-time computer vision tracking (Actual) compared to WFM projections.
Live AI Feed
Specific alerts like "Queue length exceeds threshold" or "Staff redeployed".
Operations Overview
Current Traffic, Avg. Queue Time, and Staff Coverage percentages.