Device Detection System for Venue Location Tracking
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Solution Overview
Problem
Current systems face challenges in tracking a customer's journey between online activity and physical venue visits, as they often require customer action, struggle with GPS accuracy, and are hindered by third-party tracking cookie restrictions, making it difficult to understand digital content's impact on foot traffic.
Innovation Solution
A system that integrates location data from device detection with online tracking data using a device detection system and identity matching server to generate a customer journey timeline, allowing for anonymous user identification and content modification based on in-store and online behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS technology or social media check-ins are used to track foot traffic, then location tracking capability is improved, but customer convenience deteriorates because customers must enable GPS tracking or interact with applications
Solution Approach 1:
The system uses passive Wi-Fi scanning capability that is already present in customer devices without requiring any customer action. The device detection system automatically detects devices in the venue using their inherent Wi-Fi radio, eliminating the need for customers to enable GPS or interact with applications, thus resolving the contradiction between tracking precision and customer convenience
Solution Approach 2:
The system introduces an intermediary device detection system that acts as a bridge between the venue and customer devices. This intermediary passively collects device information through Wi-Fi scanning without requiring direct customer participation, thereby maintaining both accurate location tracking and customer convenience
2Loss of information
If GPS data is used to determine customer location, then location information is obtained, but accuracy deteriorates because it is difficult to determine whether a customer is located within a venue or nearby but outside the venue
Solution Approach 1:
The system employs multiple Wi-Fi access points strategically positioned throughout the venue, each creating a localized detection zone. By using signal strength measurements from multiple access points and triangulating device position, the system achieves precise determination of whether a device is inside or outside the venue boundaries, resolving the accuracy problem with GPS
Solution Approach 2:
The system transitions from GPS's global coordinate system to a local venue-based reference frame using Wi-Fi signal strength measurements from multiple access points. This dimensional change from global to local positioning enables precise determination of device location relative to venue boundaries, solving the accuracy issue
3Measurement precision
If third-party tracking cookies are used to track online activity, then online tracking capability is improved, but system compatibility deteriorates because many systems and customers do not permit third-party tracking cookies
Solution Approach 1:
The system introduces an identity matching server as an intermediary that receives both online tracking data and offline device detection data. This intermediary performs probabilistic matching to connect online and offline customer identities without requiring third-party cookies, thereby maintaining online tracking capability while improving compatibility with systems that block such cookies
Solution Approach 2:
The system replaces the mechanical cookie-based tracking mechanism with a probabilistic identity matching approach. Instead of relying on third-party cookies that are blocked by many systems, the system uses the identity matching server to correlate online behavior with offline device detection through statistical methods, resolving the compatibility issue
4Loss of information
If comprehensive customer journey tracking is implemented, then understanding of digital content impact on foot traffic is improved, but system complexity increases due to integration of multiple data sources
Solution Approach 1:
The identity matching server performs multiple functions: it receives online tracking data, receives offline device detection data, performs probabilistic matching between the two data sources, and generates unified customer journey information. This multi-functional approach consolidates what would otherwise be separate complex systems into a single unified platform, resolving the complexity issue while maintaining comprehensive tracking capability
Data Source
AI summary
A device detection system integrates location data with online tracking data describing content viewed by a user. The device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers to an identity matching server. An entity associated with the venue may provide online tracking data obtained via a tracking pixel to the identity matching server. The identity matching server provides the tracking data and anonymous identifiers to the device detection system which correspond to the device user identifiers. The device detection system generates a customer journey timeline that describes the location data and online tracking data for a user. The device detection system or the entity may use the information in the customer journey timeline to modify or select digital content to provide to users.