ORBIT SIGNS, INC
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Retail Analytics

Measure Engagement Metrics.

We transform passive digital displays into active, intelligence-gathering edge nodes. By integrating advanced computer vision and IoT sensors, we empower enterprise retailers to measure audience dwell times, gaze tracking, and demographic analytics, fundamentally changing how brick-and-mortar stores measure ROI.

Architectural Overview

Traditional analytics rely on post-event data crunching. Our Retail Analytics platform leverages Edge Computing—specifically utilizing NVIDIA Jetson or Google Coral TPUs mounted directly to the signage hardware. These edge processors run lightweight Machine Learning models locally, instantly converting physical human interactions into anonymized JSON payloads. This eliminates massive bandwidth costs while guaranteeing absolute GDPR and CCPA privacy compliance, as raw video feeds are never transmitted over the internet.

Core Capabilities

  • Gaze & Attention Tracking Deep-learning models that calculate precise bounding boxes around facial vectors to determine exactly when, and for how long, a customer is actively looking at a specific digital asset.
  • Anonymized Demographics Algorithmic estimation of age brackets and gender demographics, allowing marketing teams to dynamically switch A/B advertising creatives based on the real-time audience standing in front of the screen.
  • Programmatic Ad (DOOH) Integration Secure APIs that pipe real-time "Proof of Play" and audience density metrics directly into programmatic Digital Out-of-Home (DOOH) bidding networks like Broadsign or Vistar Media.
  • Heatmapping & Footfall IoT Integration with LiDAR and ceiling-mounted IR sensors to generate predictive heatmaps of customer journeys throughout the retail environment, correlating sign placement with sales conversions.

Technology Stack

The edge-inference models are written in C++ and Python (TensorFlow Lite / PyTorch) to maximize frames-per-second on low-power SOCs. The telemetry is streamed via MQTT protocols to a central ClickHouse or TimescaleDB cluster, which handles millions of time-series data points for the Grafana-powered executive dashboards.