Probabilistic ID Linking for Privacy-Compliant Audience Reach
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Solution Overview
Problem
Existing techniques for identifying users across multiple channels and devices are limited in effectively reaching target audiences for messaging campaigns, as they struggle to combine disparate user data without violating privacy laws or expectations.
Innovation Solution
A system that links user attribute records across various devices and channels by creating a tiered accuracy regime for unique identifiers, combining multiple user profiles into a super profile for targeted messaging campaigns, using data management platforms and graph processors to manage and link IDs, and employing asynchronous queuing techniques for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional user identification techniques are used across multiple channels and devices, then privacy compliance is maintained, but audience reach and user identification effectiveness deteriorate
Solution Approach 1:
The patent introduces probabilistic ID linking as an intermediary mechanism that connects user data across devices and channels without requiring direct identification. Instead of using deterministic identifiers that violate privacy, the system uses probabilistic matching algorithms to infer user connections based on behavioral patterns, device characteristics, and contextual data, thereby achieving both privacy compliance and improved audience reach
Solution Approach 2:
The system transforms the identification approach by changing from deterministic parameters (exact matches on device IDs, cookies) to probabilistic parameters (similarity scores, confidence levels). This allows the system to identify users across devices by evaluating multiple weak signals simultaneously, improving reach while maintaining privacy through uncertainty that prevents definitive identification
2Measurement precision
If deterministic ID matching is used to identify users across devices, then identification accuracy is improved, but privacy violations occur
Solution Approach 1:
The patent fundamentally changes the identification parameter from deterministic (exact match) to probabilistic (confidence score). Instead of requiring perfect matches on identifiers like device IDs or cookies, the system calculates probability scores based on multiple data points including browsing behavior, device characteristics, location data, and temporal patterns, achieving accurate identification without violating privacy through uncertain matching
Solution Approach 2:
The system creates a composite identification approach by combining multiple weak identification signals (device characteristics, behavioral patterns, contextual data) into a unified probabilistic profile. This composite method achieves robust user identification across devices without relying on any single deterministic identifier that could violate privacy
3Productivity
If multiple disparate user data sources are combined, then audience coverage is improved, but data integration complexity increases
Solution Approach 1:
The patent segments the complex data integration problem into modular components: data collection modules for different channels (web, mobile, offline), processing modules for different data types, and probabilistic linking modules that operate independently on each segment. This modular segmentation reduces overall integration complexity while maintaining comprehensive audience coverage
Solution Approach 2:
The system implements a universal probabilistic linking framework that can process and integrate multiple types of disparate data sources (online behavior, offline transactions, mobile app data, CRM data) through a single unified algorithmic approach, reducing integration complexity by applying the same multi-functional framework across all data types
Data Source
AI summary
A server receives incoming data records comprising an ID value and respective user attributes corresponding to a user activity that originates at a user device. Characteristics of the activity and/or characteristics of the user device are considered in order to assign a probabilistic confidence value, which is in turn used to generate links from an incoming data record to other previously-received data records, and in so doing, generates a probabilistic link between one set of user attributes from the incoming data record and another set of user attributes from previously-received data records. A messaging campaign specification that describes target user attributes is used to identify a matching set of target audience member records. The determination of a match or not is based on the probabilistic confidence value and a threshold can be varied to extend audience reach. The identified set of target audience member records are transmitted over a network.