Cross-Device User Interaction Correlation via Link Removal
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Solution Overview
Problem
Existing technologies face challenges in correlating comprehensive user interaction data across multiple sources, including client-side and server-side information, especially when users interact with web applications from different devices or without logging in, due to limitations in tracking mechanisms and the use of distinct identifiers by third-party services.
Innovation Solution
A system that collects user interaction records from multiple sources, stores them in a correlation data store, and retroactively associates them with conclusive user identifiers using established links and confidence metrics, allowing for comprehensive analysis of user experiences without requiring constant login.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If third-party analysis services are used to track client-side interactions, then user interaction information can be collected, but the tracking identifiers do not necessarily relate to user identifiers for the web application itself, making it difficult to correlate data across sources
Solution Approach 1:
The patent introduces a correlation service as an intermediary that receives interaction records from multiple sources (including third-party analysis services) and correlates them using a correlation context. This mediator translates between different identifier systems (third-party tracking IDs and application user IDs) by finding matching records that represent the same user interaction, thereby resolving the identification accuracy problem while maintaining data completeness from all sources
Solution Approach 2:
The correlation context serves as a universal data structure that can represent interaction records from multiple different sources and identifier systems. It provides a common framework that handles both third-party tracking data and application-specific user data uniformly, enabling correlation across diverse data sources without requiring each source to use the same identification methodology
2Loss of information
If multiple tracking mechanisms are used to obtain comprehensive user interaction information, then data completeness improves, but the complexity of compiling and correlating information from multiple sources increases
Solution Approach 1:
The patent segments the correlation process into distinct modular components: interaction record receivers for different data sources, a correlation context manager, and identifier resolution services. Each component handles specific tasks independently, making the overall complex system manageable through functional segmentation. This modular architecture allows the system to incorporate multiple tracking mechanisms while maintaining organizational complexity at acceptable levels
Solution Approach 2:
The correlation service acts as a central intermediary that standardizes the integration of multiple tracking mechanisms. Rather than requiring direct integration between each tracking source and the application, the correlation service provides a unified interface that accepts records from various sources and produces correlated results, thereby reducing the overall system complexity despite handling multiple data sources
3Ease of operation
If users interact with web application without logging in, then user experience flexibility improves, but the ability to attribute interactions to specific users decreases
Solution Approach 1:
The system performs preliminary correlation of interaction records with user identifiers even before login occurs. By analyzing patterns in anonymous interaction data (device identifiers, interaction behavior, timing) and pre-establishing correlations when users eventually log in, the system maintains user attribution capability throughout the entire user journey, not just after authentication. This allows flexible anonymous interaction while preserving measurement precision
Solution Approach 2:
The correlation system uses feedback from logged-in user data to improve attribution of earlier anonymous interactions. When users log in and provide identifying information, the system retroactively correlates this information with their previous anonymous interaction records, refining the attribution accuracy. This feedback loop allows the system to maintain high measurement precision while permitting flexible anonymous access during the initial interaction phase
Data Source
AI summary
Embodiments collect, from an event stream having both client-side and server-side information from multiple tracking sources, user interaction records for a web application and supporting systems. The records are stored in a data store, and are retroactively correlated with conclusive user identifiers and, if applicable, with areas of interest. Attribution of interaction records to conclusive identifiers and areas of interest is based on links that have been established between identifiers. Established identifier links may be subject to one or more link removal criteria. If link removal criteria is satisfied for an established link, the link is broken. Once a link is broken, interaction records, found in the data store, that are associated with the inconclusive identifier of the broken link are no longer associated with the conclusive identifier of the broken link. Each interaction record that is associated with a conclusive identifier is assigned a confidence metric for the association.