Privacy Compliant Insights Platform Using Device Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data analytics solutions for consumer behavior analysis are inefficient and costly, and traditional cookie-based digital data methods are ineffective and inaccurate due to the rise of mobile applications and changing privacy regulations, necessitating a platform that provides actionable insights without personally identifiable information.
Innovation Solution
The Intuizi Consumer Behavioral Insights Platform aggregates and anonymizes data from multiple sources, using encrypted anonymous data to provide actionable insights while ensuring compliance with GDPR and CCPA/CPRA regulations, enabling real-time analysis and visualization of billions of data points for marketing and business decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cookie-based digital data methods are used, then marketing data collection is established, but the methods become ineffective, inaccurate, and fraudulent with the rise of mobile applications
Solution Approach 1:
The patent changes the fundamental parameter of data identification from cookie-based identifiers to device graph identifiers and probabilistic matching parameters. This allows the system to adapt to mobile environments where cookies are ineffective, maintaining data accuracy through alternative identification methods that work across apps and devices.
Solution Approach 2:
Instead of trying to make cookies work in mobile environments, the patent inverts the approach by abandoning cookie dependency entirely and building a new identification system based on device graphs and probabilistic matching. This reversal solves the effectiveness problem by designing a system native to mobile rather than adapting web-based solutions.
2Loss of information
If data is aggregated from multiple sources to provide comprehensive insights, then actionable information is improved, but privacy compliance becomes more difficult to maintain
Solution Approach 1:
The patent introduces an intermediary layer of probabilistic matching and device graph technology that sits between data collection and insight generation. This intermediary enables comprehensive data aggregation while maintaining privacy compliance by using statistical methods rather than direct personal identification, thus reconciling insight completeness with privacy reliability.
Solution Approach 2:
The system creates probabilistic copies and representations of consumer behavior patterns rather than using direct personal data. These synthetic representations maintain the informational value needed for insights while eliminating privacy risks associated with personally identifiable information.
3Adaptability or versatility
If cookie-based methods are abandoned for mobile-app based methods, then data effectiveness is improved, but data accuracy and reliability decrease
Solution Approach 1:
The patent creates a composite identification system that combines multiple data sources and verification methods (device graphs, probabilistic matching, multiple signal sources) to achieve reliability equivalent to or exceeding traditional cookie-based methods. This composite approach compensates for the limitations of individual mobile-based identification methods.
Data Source
AI summary
The present disclosure relates to techniques for determining insights from disparate data sets provided from multiple different data sources in a manner that complies with applicable privacy and data protection regulations. More particularly, the present disclosure relates to a computer-implemented privacy compliant data insights and audience activation platform incorporating data signals from various sources.