Digital Ad Tracking via Location and Time Correlation
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Solution Overview
Problem
Conventional digital advertising tracking systems are ineffective for non-personal devices, as they rely on device identification methods that fail to link impressions and events occurring on different devices used by the same consumer, such as outdoor displays where multiple individuals view advertisements without a unique association.
Innovation Solution
A system and method that dynamically track digital advertisement delivery and performance on non-personal devices by using a distribution platform to receive ad play data, estimate impressions, generate virtual sessions, and associate events with impressions through unique identifiers, enabling real-time reporting and integration with conventional digital advertising systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional device identification systems (cookies, device IDs) are used to track impressions and events, then tracking accuracy on personal devices is improved, but tracking effectiveness on non-personal devices deteriorates
Solution Approach 1:
The patent introduces location data and time-stamps as intermediary elements that mediate between impressions on non-personal devices and events on personal devices. Instead of relying on device identifiers, the system uses geographic location and temporal information to create a bridge that links ad exposures to consumer actions across different devices, resolving the contradiction between tracking precision on personal devices and adaptability to non-personal devices.
Solution Approach 2:
The patent segments the tracking process into distinct components: location-based impression attribution, time-stamp validation, and event correlation. By dividing the tracking mechanism into these separate functional segments, the system can accurately track impressions on non-personal devices using location data while maintaining the ability to link events on personal devices through time-based correlation, thus improving both measurement precision and cross-device adaptability.
2Reliability
If device-specific identifiers are used to link impressions and events, then event attribution on personal devices is improved, but applicability to non-personal devices deteriorates
Solution Approach 1:
The patent creates a universal tracking framework that functions across both personal and non-personal devices. By using location data and time-stamps as universal identifiers that work independently of device type, the system achieves reliable event attribution on personal devices while simultaneously enabling campaign tracking across non-personal devices, thus improving both reliability and adaptability.
Solution Approach 2:
The patent changes the fundamental parameters used for tracking from device-specific identifiers to location and time-based parameters. This parameter transformation allows the system to maintain reliable event attribution by using time-stamps for validation while extending adaptability to non-personal devices through location-based impression capture, resolving the contradiction between reliability and multi-device applicability.
3Measurement precision
If conventional tracking systems are used for personal devices, then individual consumer behavior tracking is improved, but aggregate campaign performance measurement on non-personal devices deteriorates
Solution Approach 1:
The patent applies preliminary action by capturing and storing location data and time-stamps at the moment of ad impression on non-personal devices, before the consumer action occurs. This preliminary capture of contextual data enables accurate aggregate campaign performance measurement by pre-establishing the framework for linking multiple impressions to subsequent events, thus improving both consumer action tracking precision and total impression volume measurement.
Solution Approach 2:
The patent implements feedback mechanisms where event data from personal devices is fed back into the system and correlated with previously captured location and time-stamp data from non-personal devices. This feedback loop enables the system to accurately attribute individual consumer actions to specific ad impressions while simultaneously aggregating campaign performance metrics across all devices, resolving the contradiction between precise consumer tracking and aggregate measurement.
Data Source
AI summary
Systems and methods for dynamically tracking delivery and performance of digital advertising placed on non-personal devices in physical locations and integrating, displaying, and reporting impressions and events in digital advertising systems.