Visitor Identification via Feature Selection and Relevance Scoring
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Solution Overview
Problem
Traditional tracking cookie-based techniques fail to effectively link clickstream data across devices and time, leading to incomplete tracking data and impaired digital marketing decisions, such as reduced revenue and offer acceptance.
Innovation Solution
A digital marketing environment that uses feature selection analysis to match clickstream data from unknown visitors with known user profiles, employing an information retrieval model to compute relevance scores and merge data into existing profiles, ensuring accurate visitor identification and enhanced marketing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tracking cookies are used to identify visitors, then visitor identification is achieved, but clickstream data cannot be linked across devices and time
Solution Approach 1:
The patent introduces an intermediary matching mechanism that uses multiple features (device identifiers, IP addresses, timestamps, behavioral patterns) as mediators to link clickstream data across different devices and time periods. This intermediary layer enables correlation between separate cookie-based sessions without relying solely on persistent cookies, thereby resolving the data linkage problem while maintaining identification accuracy.
Solution Approach 2:
The system implements a universal visitor identification approach that works across multiple devices and contexts. By establishing a unified profile that can be accessed through various identifiers (cookies, device IDs, IP addresses), the system achieves multi-functional identification capability that maintains consistency regardless of which device or method is used, thereby linking clickstream data comprehensively.
2Reliability
If tracking cookies are used to distinguish visitors, then visitor differentiation is achieved, but data completeness deteriorates
Solution Approach 1:
The patent merges multiple data sources and identification methods into a unified visitor profile. By combining information from cookies, device identifiers, IP addresses, and behavioral patterns into a single comprehensive profile, the system achieves both reliable visitor differentiation and complete tracking data. This merging process ensures that all available information about a visitor is aggregated regardless of which identifier is used.
Solution Approach 2:
The system performs preliminary profile creation and feature extraction when a visitor first interacts with the system. By pre-establishing a comprehensive profile with multiple identifiers and behavioral patterns before the visitor leaves, the system ensures that data completeness is maintained from the outset. This preliminary action prevents information loss by capturing all relevant data points early in the interaction sequence.
3Ease of manufacture
If traditional cookie-based tracking is used, then implementation simplicity is maintained, but marketing decision quality deteriorates
Solution Approach 1:
The patent segments the tracking system into modular components: a profile management module, a feature extraction module, a matching module, and a data linkage module. This segmentation allows the system to maintain implementation simplicity through standardized interfaces while dramatically improving marketing data quality through sophisticated analysis. Each module handles specific tasks independently, making the complex overall system easier to implement and maintain.
Solution Approach 2:
The system replaces the mechanical cookie-based tracking mechanism with a more sophisticated software-based profile management and matching system. By substituting the simple cookie storage mechanism with intelligent feature extraction, pattern recognition, and multi-source data correlation, the system achieves superior marketing decision quality while maintaining ease of implementation through automated processing.
Data Source
AI summary
Techniques are described in which a service operates to identify consumers corresponding to visitor interactions with resources available from a service provider. Features are selected to use for matching of clickstream data collected for unknown visitors to profiles established for known visitor IDs. The features are selected based on analysis that accounts for consistency, completeness, and uniqueness of features among a corpus of profiles. Then, relevance scores are computed over the selected features using an information retrieval model in which clickstreams are treated as queries and profiles are treated as documents. Unknown visitors are matched to corresponding profiles using the relevance scores. Access to the digital media content is then controlled in accordance with the matching based on relevance scores, such as by serving individualized marketing offers and content to consumers that is targeted to characteristics of the consumers indicated by respective profiles.