Programmatic TV Advertising Placement Using Consumer Graph Data
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Solution Overview
Problem
Current advertising strategies for TV are inefficient due to manual processes, limited data integration, and inability to accurately associate devices with consumers, leading to suboptimal advertising placement and restricted reach across multiple devices.
Innovation Solution
A programmatic TV bidding system that integrates consumer data from various devices using a consumer graph to optimize advertising campaigns, allowing real-time bidding on TV slots based on demographic and behavioral data, enabling precise targeting and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for TV advertising strategy development, then human analysts can guide selection based on static data, but the process is inefficient and delays response to market trends
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated computational systems. Machine learning models and algorithms automatically process consumer data, identify patterns, and generate advertising strategies, eliminating the need for manual Excel-based analysis and enabling real-time responsiveness to market changes.
Solution Approach 2:
The system enables self-service advertising strategy development through automated data processing and analysis. The computational system independently performs data integration, consumer behavior analysis, and campaign optimization without requiring continuous human intervention, allowing the system to serve itself in generating and adjusting advertising strategies.
2Adaptability or versatility
If TV advertising data is treated separately from other media platforms, then TV-specific criteria can be used for planning, but advertising strategies become fragmented and limited in reach
Solution Approach 1:
The patent merges TV advertising data with mobile and desktop advertising data into a unified consumer profile. The system integrates disparate data sources from multiple platforms and devices, creating a comprehensive view of consumer behavior that enables coordinated cross-platform advertising strategies while managing data complexity through standardized processing pipelines.
Solution Approach 2:
The system creates a universal data framework that handles multiple media platforms (TV, mobile, desktop) through common processing mechanisms. The same analytical models and algorithms are applied across different platforms, enabling consistent advertising strategies that adapt to various devices and media types while maintaining data integrity and operational simplicity.
3Adaptability or versatility
If traditional TV slot purchasing is used, then advertising placement can be secured in advance, but the approach lacks flexibility and real-time optimization capability
Solution Approach 1:
The patent transforms static, pre-scheduled TV advertising into a dynamic system that adapts in real-time. The system continuously monitors consumer behavior data and automatically adjusts advertising placement decisions, enabling flexible response to changing market conditions while maintaining high campaign efficiency through automated real-time optimization.
Solution Approach 2:
The system implements feedback loops where consumer response data from TV, mobile, and desktop platforms continuously informs advertising placement decisions. Real-time performance metrics feed back into the machine learning models, which automatically adjust strategies to optimize campaign efficiency and adapt to emerging trends without manual intervention.
Data Source
AI summary
The current invention relates to a computer-generated method for optimizing placement of advertising content to consumers' TV's using a programmatic TV bidding model. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.