Household Demographic Assignment Using Provider Overlap Targets
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Solution Overview
Problem
Existing household demographic assignment models inaccurately predict demographics for households subscribing to multiple digital media providers due to ignoring overlaps between providers, leading to biased and inaccurate demographic assignments.
Innovation Solution
A methodology that determines target distributions of demographics by accounting for overlaps between digital media providers using reach values, mixed provider fractions, and constrained optimization routines, ensuring accurate demographic assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing household demographic assignment models are used that do not account for provider overlap, then the modeling process is simpler, but the demographic assignment accuracy deteriorates
Solution Approach 1:
The patent segments the subscriber base by creating distinct groups based on provider overlap patterns (single-provider subscribers, dual-provider subscribers, etc.). This segmentation allows the model to apply different demographic distributions to different segments, improving accuracy while managing complexity through structured categorization.
Solution Approach 2:
The patent introduces a new dimension of analysis by considering provider combination types as an additional categorical variable. Instead of treating all subscribers uniformly, the model now operates in a multi-dimensional space that includes provider overlap status, enabling more precise demographic assignments without exponentially increasing overall model complexity.
2Measurement precision
If separate demographic distribution targets are established for each digital media provider, then the calculation process is simpler, but the demographic assignments become biased and inaccurate
Solution Approach 1:
The patent introduces mixed provider fractions as an intermediary variable that mediates between individual provider demographics and combined provider demographics. This intermediary allows the model to account for overlap effects without requiring complex direct calculations of all possible provider combinations, simplifying the overall calculation process while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary calculations to determine mixed provider fractions and provider overlap distributions before finalizing demographic assignments. By pre-computing these overlap characteristics, the model avoids complex real-time calculations during the demographic assignment phase, reducing computational complexity while preserving accuracy.
3Reliability
If provider overlaps are not accounted for in demographic distribution targets, then the data processing is simpler, but the audience metrics become biased
Solution Approach 1:
The patent segments the audience measurement process by creating separate calculation pathways for different provider overlap scenarios. This segmentation ensures that each segment's demographics are calculated with appropriate accuracy while maintaining overall processing efficiency through modular computation.
Solution Approach 2:
The patent changes the demographic distribution parameters to account for provider overlap by introducing mixed provider fractions as adjustment factors. This parameter modification allows the model to correct for overlap bias without requiring a complete restructuring of the data processing framework, balancing reliability improvement with processing complexity management.
Data Source
AI summary
An example method includes determining, for each of multiple digital media providers, respective estimated distributions of a characteristic for a television viewing area. The method also includes determining respective reach values based on a universe estimate for the television viewing area. In addition, the method includes determining a distribution of subscribers across overlapping combinations of the digital media providers. The method also includes determining, for each of the multiple digital media providers based on the distribution of subscribers, respective mixed provider fractions relative to a total provider fraction for the digital media provider. The method further includes determining, using a constrained optimization routine, target distributions of the characteristic for combinations of the digital media providers. And the method includes using the target distributions as a basis for assigning values of the characteristic to households that are subscribes of the digital media providers and located in the television viewing area.