Cross-Platform User Tracking via Unified Data Normalization
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Solution Overview
Problem
Current analytics tools for advertising campaigns are limited in their ability to provide unified insights across multiple platforms, leading to inefficiencies in campaign analysis and revenue losses due to inconsistent and misleading data from different ad-serving companies and social media websites.
Innovation Solution
A system and method for generating unified user-level data across various media platforms, involving a data sanitizing module, transformation and storage engine, media-link module, and metadata database to normalize and analyze data from multiple sources, enabling cross-platform analytics and tracking user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each ad-serving company and social media website independently gathers its own statistics using unique user identifiers, then each platform can track user behavior accurately on its own platform, but the same user is mapped to multiple different user IDs across different platforms, creating misleading data and loss of information
Solution Approach 1:
The patent merges multiple platform-specific user identifiers into a single unified user ID that persists across different ad-serving companies and social media websites. This is achieved by creating a cross-platform user profile that consolidates identification data from multiple sources, allowing the system to recognize the same user regardless of which platform they are on, thereby eliminating the information loss caused by multiple mapping of user identities.
Solution Approach 2:
The unified user ID system serves multiple functions simultaneously: it identifies users on individual platforms, tracks users across platforms, and provides a consistent identifier for cross-platform analytics. This universal identifier replaces the need for separate platform-specific identifiers while maintaining compatibility with existing platform tracking mechanisms.
2Quantity of substance
If data from multiple ad-serving companies and social media websites is collected for campaign analysis, then comprehensive campaign performance data is available, but the volume of data becomes trillions of records and the complexity of analysis becomes almost impossible using existing tools
Solution Approach 1:
The patent segments the massive dataset by organizing it into structured formats with standardized schemas that separate different types of information (user data, ad data, platform data, etc.). This segmentation allows the system to process and analyze specific portions of the data independently rather than attempting to analyze all trillions of records simultaneously, significantly reducing computational complexity.
Solution Approach 2:
The patent introduces intermediary processing layers including data normalization modules and unified data models that act as mediators between the raw multi-platform data and the analysis tools. These intermediaries standardize the data format, reduce redundancy, and prepare the data for efficient analysis, making the complex multi-platform data manageable for existing analytical tools.
3Adaptability or versatility
If each ad-serving company presents gathered statistics using different formats, then each company can optimize its data presentation for its specific needs, but the inconsistency in data formats further increases the complexity of campaign analysis
Solution Approach 1:
The patent implements data normalization that converts diverse data formats from different ad-serving companies and social media websites into a homogeneous standardized format. This normalization process maintains the adaptability to receive various input formats while transforming them into a consistent structure that simplifies integration and analysis, reducing the complexity caused by format inconsistencies.
Data Source
AI summary
A system and method for tracking users across a plurality of media platforms are provided. The method includes generating unified user-level data of each user across the media of advertising platforms; storing the unified user-level data generated for each user in a storage; taping into the plurality of advertising platforms to render pixel trafficking data; and analyzing raw data based on which the unified user-level data is generated.