Unique Audience Estimation Under Privacy-Redacted Media Exposure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audience measurement systems overestimate unique viewer counts due to the assumption that redacted and non-redacted digital content exposures come from mutually exclusive groups, leading to overcounting when user-level information is removed for privacy protection.
Innovation Solution
A reach curve model is used to estimate the incremental audience of redacted digital content exposures, considering that a given user can generate both redacted and non-redacted exposures, by leveraging panel data and demographic information to adjust viewer counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If user-level information is removed for privacy protection, then user privacy is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the audience measurement into two distinct groups: redacted exposures (where user-level information is removed for privacy) and non-redacted exposures (where user-level information is retained). By separating these segments and applying different measurement approaches to each, the system can maintain privacy protection while still achieving accurate unique viewer count estimation through the combination of both segments.
Solution Approach 2:
The patent introduces an intermediary approach by using reach curve modeling and inversion techniques as mediators between the redacted and non-redacted exposure data. This intermediary methodology allows the system to bridge the gap between privacy-protected aggregated data and accurate audience measurement, enabling precise estimation without requiring individual user-level information.
2Device complexity
If redacted and non-redacted exposures are treated as mutually exclusive groups, then data processing is simplified, but measurement precision deteriorates
Solution Approach 1:
The patent merges the measurement of redacted and non-redacted exposures into a unified framework that accounts for both segments simultaneously. Instead of treating them as completely separate mutually exclusive groups, the system combines their contributions through reach curve inversion to calculate the total unique viewer count, thereby improving measurement precision while maintaining manageable processing complexity through structured integration.
Data Source
AI summary
In one example, a computing system is described. The computing system is configured to perform a set of acts that includes obtaining, from a DEP, a non-redacted unique viewer count for a set of digital content exposures. The set of acts also includes obtaining, from the DEP via the protected cloud environment, a redacted exposures count for the set of digital content exposures. In addition, the set of acts includes determining, using an inverted reach curve and the non-redacted unique viewer count, an initial exposures value for the set of digital content exposures. The set of acts also includes scaling the initial exposures value using the redacted exposures count to obtain a final exposures value. Further, the set of acts includes determining, using a reach curve and the final exposures value, a final unique viewer count for the set of digital content exposures, and outputting the final unique viewer count.