Retail intelligence uses in-store cameras to understand how customers move, where queues form, what is on the shelf and where loss occurs, so store teams and merchandisers act on evidence.
Online retailers know every click, path and abandoned basket. Physical stores know what was sold and little about what happened before the sale: how many people entered, where they went, what they considered, how long they waited, and which shelves were empty when they looked.
Store cameras already observe all of this. Without analytics, however, that observation is used only for security review. Decisions about layout, staffing, replenishment and loss prevention rest on intuition, sampled audits and lagging sales data.
Our retail intelligence solution extracts anonymized behavioral and shelf insight from existing cameras: footfall and paths, queue dynamics, shelf availability and loss-related events, aggregated across the estate and delivered to the teams who can act on them.
Each capability is marked so you can distinguish what we deliver today from capabilities engineered to your specification.
Anonymized footfall, paths, zone dwell and conversion between areas, derived from tracked trajectories without identifying individuals.
Queue length and estimated wait at checkouts and service points, with alerts when thresholds are exceeded so staff can open lanes.
Detection of gaps, low stock and misplaced products on monitored fixtures, prompting timely replenishment.
Recognition of products and planogram compliance on shelf, supporting merchandising audits from fixed cameras.
Detection of patterns associated with loss, such as unusual dwell in high-risk zones and self-checkout anomalies, surfaced for human review.
Cross-store comparison of footfall, conversion and queue performance to identify outliers and best practice.
Correlating visual metrics with transactions and staffing schedules for conversion and labor analysis.
The pipeline behind the solution, stage by stage.
Existing in-store cameras are connected to an edge device in the back office; coverage gaps are identified and addressed where needed.
People are detected and tracked as anonymous trajectories; shelves are analyzed for product presence. No facial or identity data is retained.
Trajectories become footfall, paths, dwell and queue metrics; shelf analysis becomes availability and compliance states.
Real-time alerts reach store teams on queue and shelf conditions, while aggregated reports reach merchandising, operations and loss prevention.
Layout, staffing and replenishment changes are evaluated against the same metrics, closing the loop between decision and result.
Store teams respond to queues and empty shelves as they occur.
Layout and merchandising decisions are informed by observed customer behavior.
Loss-prevention effort is focused on evidence-backed patterns.
Performance is comparable across stores on a consistent basis.
Customer privacy is protected by design through anonymized processing.
Real-time video understanding, event detection, behavioral analysis and multi-camera analytics.
Custom detection, classification, segmentation, recognition and tracking systems.
Develop custom machine learning pipelines, predictive systems and intelligent data solutions.
Tell us about your estate and the questions your store teams ask. We will show which can be answered from the cameras already installed.