Vionvision’s 4-in-1 footfall analytics go beyond basic traffic counts by combining pass-by, entry, exit, group counts, and unique visitor metrics to better reflect real shopping behavior—such as families or friends visiting together. Beyond volume, the platform enriches traffic data with core demographics (e.g., gender and age) and appearance-based style tags (e.g., color,pattern, logs), building clear shopper profiles by entrance, zone, and time period. This enables retailers to understand not only how many people visit, but who they are and how they shop—creating a more accurate foundation for conversion analysis, merchandising decisions, and targeted marketing strategies.
Vionvision visualizes how shoppers move through the store with intuitive heatmaps and path analytics. It reveals where customers go first after entering, how they navigate between zones, and which areas drive—or lose—traffic. By analyzing movement patterns and zone-to-zone correlations, retailers can pinpoint traffic drivers, dead zones, and flow bottlenecks. Shelf and zone correlation insights also highlight opportunities to improve layout logic, increase cross-category exposure, and guide shoppers more effectively through the store.
Vionvision provides zone-level visibility into customer attention across shelves and display areas. By tracking footfall intensity and dwell time by zone, teams can quickly identify high-performing areas and overlooked sections. These insights help validate the effectiveness of product placement, promotional displays, and visual merchandising—supporting smarter layout adjustments and better space utilization based on real shopper behavior.
Vionvision tracks fitting room footfall, dwell time, and peak usage periods to help retailers optimize fitting room operations. By understanding demand patterns and potential queue build-ups, teams can improve staffing allocation, reduce wait times, and enhance the customer experience—supporting stronger conversion performance for try-on-driven categories.
Vionvision supports automated SOP compliance monitoring by detecting key operational behaviors, such as whether kitchen staff are wearing required chef hats, following scheduled hand-washing routines, and disposing of waste in a timely manner. By transforming manual inspections into AI-driven oversight, the platform helps operators improve hygiene standards, reduce compliance risks, and ensure consistent execution of brand and food safety protocols across all stores.
