Automated Attribution Modeling Using Real-Time Location Data
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Solution Overview
Problem
Conventional attribution techniques in digital content ecosystems are ineffective in providing near-real-time feedback for optimizing advertisement campaign performance, relying on delayed credit card data synchronization and manual comparisons that are resource-intensive and inaccurate.
Innovation Solution
An automated attribution modeling system using real-world visit data, which associates unique device identifiers with user features to create exposed and control groups, allowing for rapid optimization of targeted content campaigns through algorithmic analysis and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional attribution techniques using credit card data synchronization are used, then measurement accuracy is improved, but response time deteriorates (weeks or months delay)
Solution Approach 1:
The patent introduces a third-party data provider as an intermediary that supplies real-time location and behavior data about consumers. This mediator enables the system to access accurate attribution data without requiring direct synchronization of credit card information, thus maintaining measurement precision while eliminating the weeks-long delay associated with traditional credit card data matching methods.
Solution Approach 2:
The patent replaces the mechanical/manual process of synchronizing credit card data with automated algorithmic processing. Instead of manually matching transaction records with campaign data over extended periods, the system uses automated algorithms to process real-time location data, device identifiers, and consumer behavior patterns, achieving both speed and accuracy in attribution measurement.
2Measurement precision
If manual comparison methods are used to assess campaign performance, then measurement accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent implements self-service through automated algorithms that independently perform the comparison and analysis functions. The system automatically processes consumer data, matches it with campaign exposure information, and generates performance metrics without requiring manual intervention. This automation maintains measurement accuracy while dramatically reducing the human resources and time consumption associated with manual comparison methods.
Solution Approach 2:
The patent substitutes manual comparison processes with automated computational algorithms. Instead of human analysts manually reviewing and comparing data points, the system uses algorithmic processing to automatically analyze consumer behavior patterns, match them with campaign data, and generate performance measurements, thereby reducing resource consumption while maintaining or improving measurement precision.
3Speed
If real-time data processing is implemented, then response speed is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: data collection components that gather real-time location and behavior data, data processing components that algorithmically analyze the data, and output components that generate performance metrics. This segmentation allows each module to handle specific tasks independently, improving real-time processing speed while managing complexity through modular architecture rather than monolithic processing.
Solution Approach 2:
The patent uses a third-party data provider as an intermediary that handles the complex data collection and initial processing tasks. This mediator supplies pre-processed real-time data in a standardized format, reducing the complexity burden on the main analysis system while enabling real-time processing capabilities. The intermediary absorbs much of the infrastructure complexity, allowing the core attribution system to focus on analysis rather than data infrastructure management.
Data Source
AI summary
The present disclosure relates to systems and methods for automatic attribution modeling and measurement. In aspects, a system may receive identification information associated with profiles and electronic devices that were exposed to a certain piece of targeted content. The demographic and device data associated with the individuals who were exposed to the targeted content are used to create a control group of individuals who were not exposed to that targeted content. The real-world visit rates of the exposed group and the control group to one or more locations may be monitored over a period of time (or campaign) and evaluated to assess the effectiveness of the targeted content.