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Smart Retail Video Analytics Platform for Real-Time Customer Behavior Insights
  1. case
  2. Smart Retail Video Analytics Platform for Real-Time Customer Behavior Insights

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Smart Retail Video Analytics Platform for Real-Time Customer Behavior Insights

netguru.com
Information technology
Retail
Security

Challenges in Retail Video Data Utilization

Retailers struggle to extract actionable insights from unstructured video data due to high processing costs, manual analysis limitations, privacy compliance requirements, and integration challenges with existing infrastructure. Current systems fail to provide real-time operational intelligence for queue management, occupancy control, and resource allocation.

About the Client

Technology consulting firm specializing in AI/ML solutions for enterprise digital transformation

Key Project Goals

  • Develop scalable ML-based video analytics for real-time customer behavior tracking
  • Create cost-effective automation for retail operations optimization
  • Ensure GDPR-compliant anonymized data processing
  • Enable integration with existing CCTV hardware and retail management systems

Core System Capabilities

  • Customer count tracking with 98%+ accuracy
  • Queue length measurement and cashier demand prediction
  • Real-time occupancy monitoring with threshold alerts
  • Activity recognition using CNN-based models
  • Edge computing deployment for low-latency processing

Technology Stack

Convolutional Neural Networks (CNN)
NVIDIA Jetson edge computing devices
TensorFlow/PyTorch frameworks
Kubernetes container orchestration
Apache Kafka streaming platform

System Integrations

  • Existing CCTV camera systems
  • Retail point-of-sale (POS) systems
  • Cloud storage for historical data analysis
  • Store management alert systems

Operational Requirements

  • Process 1080p video at 5-10 FPS per stream
  • Support 100+ concurrent camera streams per deployment
  • Maintain <500ms processing latency
  • 99.9% system uptime SLA
  • HIPAA/GDPR data compliance framework

Business Value Proposition

Enables 30% reduction in operational costs through automated resource allocation, improves customer satisfaction via optimized queue management, ensures compliance with occupancy regulations, and provides actionable business intelligence for revenue optimization through customer behavior analytics.

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