Audience Measurement Demographic Data Correction
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Solution Overview
Problem
Audience measurement entities face inaccuracies in self-reported demographic information from online database proprietors, which can lead to unreliable audience measurement results due to users providing false data, and privacy concerns limit the use of external database information without consent.
Innovation Solution
An audience measurement entity collects highly reliable demographic information from panel members through traditional methods and uses decision tree-based approaches to analyze and adjust self-reported demographic data from online database proprietors, linking it to reference data to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If demographic information is obtained from database proprietors (social network sites, online retailer sites, etc.), then the quantity of monitored individuals expands to a larger sample population, but the reliability of demographic information decreases due to self-reported data being potentially false or inaccurate
Solution Approach 1:
The patent uses panel members as an intermediary reference group. These panel members provide both self-reported demographic information from database proprietors and highly accurate reference demographic information collected through traditional methods. By comparing the two data sources for panel members, the system establishes correction relationships that can then be applied to the larger non-panel population, thus mediating between the quantity expansion and reliability maintenance
Solution Approach 2:
The system changes the parameter of demographic information accuracy by applying correction factors derived from panel member comparisons. The correction model adjusts the self-reported demographic parameters (age, gender, etc.) using the reference data from panel members, transforming the unreliable parameters into corrected parameters that maintain both the expanded population coverage and improved accuracy
2Reliability
If traditional panel member monitoring methods are used to collect demographic information, then the reliability of demographic data is high, but the quantity of monitored individuals is limited to a small panel population
Solution Approach 1:
The patent segments the population into two groups: panel members who provide reference data and the larger non-panel population whose data needs correction. By segmenting the data collection approach - using traditional methods for the small panel group and corrected database methods for the larger population - the system achieves both high reliability through the panel reference and large quantity through the expanded database coverage
Solution Approach 2:
The system performs preliminary action by first collecting and comparing demographic information from panel members using both traditional methods and database proprietor sources. This preliminary comparison establishes correction relationships and models that are then applied to the larger population, preparing the correction framework in advance before processing the expanded dataset
3Ease of operation
If self-reported demographic information from database proprietors is used, then data collection becomes easier and more scalable, but the accuracy decreases due to users providing false information
Solution Approach 1:
The patent implements feedback by using the highly accurate reference demographic information from panel members to evaluate and correct the self-reported data. The system continuously compares self-reported data against reference data for panel members, uses this feedback to build correction models, and applies these corrections to improve the precision of the easily collected database information
Data Source
AI summary
Example methods, apparatus, systems, and articles of manufacture to facilitate analysis and adjustment of demographic information for monitored audience members are disclosed. Disclosed example methods include receiving a data set including media exposure data and associated data from at least one of a panelist database and a user account database. Disclosed example methods include measuring the data set to determine a probability distribution of user age in the data set according to a first model. Disclosed example methods include comparing the probability distribution of user age to a threshold. Disclosed example methods include adjusting, based on the comparison of the probability distribution of user age to the threshold, the probability distribution to an adjusted probability distribution by replacing the probability distribution with a degenerate distribution. Disclosed example methods include generating audience measurement information based on the data set and the probability distribution and/or the adjusted probability distribution.