UST Wi-Fi Analytics with In-Store Retail Analytics and Shopper Insights

UST Wi-Fi Analytics with Retail Analytics and Shopper Insights is a cloud-native in-store analytics platform built on AWS. It transforms your existing Wi-Fi network into a sensor for retail analytics, using passive device detection to capture privacy-conscious footfall, dwell time, and traffic flows without beacons or new hardware. By turning in-store movement into behavioral analytics, it helps retailers see how shoppers enter, navigate, and engage across every zone and store.

The platform measures new vs returning visitors, visit frequency, and journey paths, then visualizes these insights through intuitive dashboards and traffic heatmaps. Store, regional, and corporate teams can compare locations, evaluate campaigns, and understand how changes to layout, staffing, or merchandising impact shopper behavior and store performance. AI-driven segmentation and predictive analytics provide forward-looking views of footfall, peak times, and loyalty trends to support smarter planning.

Deployed in as little as a few weeks, UST Wi-Fi Analytics integrates with CRM, CDP, and BI tools so retailers can connect in-store shopper insights with broader customer and marketing data. The result is an in-store analytics and shopper insights solution that improves store layout efficiency, optimizes operations, and supports data-driven decisions which are powered by the scalability, security, and reliability of AWS.

Key Features

• Wi-Fi–based in-store analytics: Uses your existing Wi-Fi infrastructure and passive device detection to capture footfall, dwell time, and movement patterns without new sensors or beacons.
• Real-time traffic and dwell heatmaps: Visualizes zone-level traffic, congestion, and dwell time across the store, enabling teams to see which areas attract shoppers and which need attention.
• Shopper journey and path analysis: Tracks how visitors move between entrances, aisles, and departments to reveal typical journey paths, bottlenecks, and missed engagement opportunities.
• New vs returning visitor insights: Identifies unique devices to distinguish between first-time and repeat visitors, visit frequency, and loyalty trends at the store and regional level.
• Campaign and promotion impact tracking: Correlates changes in traffic and dwell time with campaigns, promotions, and merchandising changes to show which initiatives drive measurable in-store lift.
• AI-driven segmentation and forecasting: Applies predictive analytics to forecast footfall and peak periods and to segment visitors based on behavior, helping teams plan staffing and inventory.
• Multi-store comparison and benchmarking: Provides dashboards that compare stores, formats, and regions, making it easier to benchmark performance and identify best practices across the estate.
• Cloud-native, AWS-based architecture: Runs on AWS for security, scalability, and reliability, with centralized management, role-based access, and APIs for integration with existing tools.
• Integration with CRM, CDP, and BI tools: Connects in-store analytics with customer profiles and marketing systems so retailers can align shopper insights with wider customer and campaign strategies.

Key Benefits

• Unlock in-store shopper insights from existing Wi-Fi: Turn your current Wi-Fi network into an in-store analytics engine, avoiding the cost and complexity of deploying new sensors or hardware.
• Improve store layout and experience: Use traffic and dwell analytics to refine layouts, product placement, and signage, making it easier for shoppers to find what they need and stay longer.
• Optimize staffing and operations: Align staffing plans, service levels, and task scheduling with real traffic patterns and predicted peak times to improve efficiency and customer service.
• Measure the impact of campaigns and promotions: See how in-store initiatives change visitor behavior, then double down on what works and quickly adjust campaigns that underperform.
• Strengthen loyalty and retention strategies: Track new vs returning visitors and visit frequency to understand loyalty trends and identify where to invest in experience improvements.
• Benchmark and scale best-performing stores: Compare locations to identify high-performing formats, layouts, and practices, then replicate proven strategies across the network.
• Support data-driven retail decisions on AWS: Give merchandising, operations, and marketing teams consistent, trusted in-store data delivered through secure, scalable AWS retail analytics services.