Minimum Cross Entropy for Media Audience Granular Data Estimation
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Solution Overview
Problem
Audience measurement entities face challenges in collecting granular data for media audience measurement, particularly in regions with low population density, as enlisting and monitoring panelists is costly and intrusive, and non-panelists may not consent to exposure data collection, limiting the value of tuning data.
Innovation Solution
Utilizing minimum cross entropy to calculate granular data of a target region based on aggregate behavioral and demographics data from a non-panelist region, leveraging granular data from a panelist region, allowing for estimation without direct collection from the target region, thereby reducing costs and increasing data value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If panelists are enlisted and monitored to collect granular exposure data, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses minimum cross entropy as a mathematical intermediary to bridge aggregate tuning data from non-panelists and granular exposure data from panelists, enabling estimation of granular metrics without directly monitoring non-panelists
Solution Approach 2:
The patent creates a virtual representation of panelist behavior patterns and applies these patterns to non-panelist aggregate data through minimum cross entropy calculation, effectively copying the structure of granular data from panelists to estimate non-panelist behavior
2Measurement precision
If panelists are enlisted to collect granular exposure data, then measurement precision is improved, but ease of operation deteriorates due to intrusiveness
Solution Approach 1:
Minimum cross entropy serves as a mediator that translates non-intrusive aggregate tuning data into granular exposure estimates by leveraging patterns from panelist data, eliminating the need for intrusive monitoring of non-panelists
Solution Approach 2:
The patent applies partial action by using only aggregate tuning data from non-panelists (without full exposure monitoring) combined with panelist patterns to achieve granular measurement, avoiding excessive intrusion while maintaining precision
3Productivity
If aggregate tuning data from non-panelists is used, then productivity is improved by reducing collection burden, but measurement precision deteriorates due to lack of granular exposure information
Solution Approach 1:
The patent merges aggregate tuning data from non-panelists with granular exposure patterns from panelists through minimum cross entropy calculation, combining the productivity benefits of aggregate data with the precision of granular patterns
Solution Approach 2:
The patent transforms aggregate tuning data parameters into granular exposure estimates by applying minimum cross entropy optimization, changing the parameter representation from aggregate to granular while maintaining data collection efficiency
Data Source
AI summary
Methods and apparatus to utilize a minimum cross entropy to calculate granular data of a region based on another region for media audience measurement. An example method for calculating granular data of a region for media audience measurement includes determining, by executing first instructions via a processor, aggregate behavioral data associated with a measurement of a media audience of a target region; determining, by executing second instructions via the processor, aggregate demographics data of the target region; and determining, by executing third instructions via the processor, granular data of a source region. The example method includes calculating, by executing fourth instructions via the processor, granular data of the media audience of the target region by utilizing minimum cross entropy to apportion the aggregate demographics data and the aggregate behavioral data of the target region based on the granular data of the source region to determine.