Modern retail environments are dynamic and unpredictable. Customer footfall fluctuates throughout the day, across locations, and due to external factors such as promotions, weather, or local events. Without accurate visibility into how many customers are currently in store, retailers face several challenges:

Operational inefficiency

Stores may be understaffed during busy periods or overstaffed during quiet hours, directly affecting labor costs and service quality.

Customer experience risks

High in‑store density often leads to longer queues, crowded aisles, and reduced shopping comfort. Without timely awareness, teams are unable to intervene proactively.

Safety and security considerations

Customer volume is a key input for situational awareness. Understanding occupancy levels helps stores better manage congestion and respond to incidents in a controlled manner, aligning with SAI Group’s broader focus on store safety and operational oversight.

Limited real‑time visibility

Traditional footfall reports are often retrospective. By the time data is reviewed, the opportunity to act has already passed.

The Customers in Store Count feature addresses these gaps by providing a continuously updated view of customer presence, enabling retailers to respond in real time rather than react after the fact.

The Customers in Store Count feature is delivered as part of SAI Group’s visual AI platform, which overlays machine‑vision intelligence on top of existing CCTV infrastructure in retail stores.

At a high level, the feature operates as follows:

Camera‑based visual analysis

The platform analyzes video feeds from in‑store cameras that are already installed for security and operations. No separate people‑counting hardware is required, aligning with SAI Group’s emphasis on cost‑effective deployment using existing systems.

AI‑driven recognition of customer presence

Computer vision models interpret visual patterns within the store environment to estimate the number of customers currently present. This analysis runs continuously, allowing the system to adapt as customers enter, move through, and exit the store.

Real‑time aggregation and visibility

Customer counts are aggregated at the store level and made available through SAI’s operational interfaces, alongside other store insights such as queue management and safety alerts. This ensures that store teams and operations managers can view occupancy contextually rather than in isolation.

Privacy‑aware design

SAI Group positions its platform as operating within strict security and privacy standards, using visual AI to extract operational signals rather than personal identity information. This approach supports responsible use of computer vision in public retail environments.