Activity Assignment Model for Mobile Census Data Attribution
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Solution Overview
Problem
Existing audience measurement systems face inaccuracies in determining media exposure on mobile devices due to misattribution errors, where demographic data is incorrectly attributed to users, leading to biased representation of audience demographics and measurement errors.
Innovation Solution
The development of an activity assignment model and correction factors to accurately reassign logged impressions to the correct demographic data, using historical exposure data and supplemental survey information to determine the probability of media access by household members, thereby correcting misattribution errors in aggregate census data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional panel member monitoring is used to determine media exposure, then audience measurement can be performed, but measurement precision deteriorates due to misattribution errors and biased representation
Solution Approach 1:
The patent introduces device identifiers (such as IMEI, MAC address, or other unique device identifiers) as an intermediary to link media consumption events to household members. Instead of directly attributing media exposure to panel members based on self-reporting, the system uses device identifiers to create an objective connection between the media content and the actual user, thereby reducing misattribution errors and improving measurement precision while maintaining reliable demographic representation.
2Ease of operation
If server logs are used to monitor user access to Internet resources, then monitoring capability is achieved, but measurement precision deteriorates due to inability to accurately attribute access to specific demographic groups
Solution Approach 1:
The patent merges server log data with device identifier information and household member profiles to create a comprehensive attribution system. By combining the ease of server-based logging with detailed device and demographic data, the system maintains operational simplicity while significantly improving demographic attribution precision through multi-data-source correlation.
Solution Approach 2:
The patent replaces traditional mechanical survey methods and self-reporting mechanisms with automated electronic tracking systems that use device identifiers and digital footprints. This substitution eliminates manual data collection errors and provides more precise, objective demographic attribution while maintaining ease of operation through automated processes.
Data Source
AI summary
An example apparatus includes at least one memory, instructions, and at least one processor to execute the instructions to generate electronic mobile measurement data based on network communications received from first client devices, select attributes associated with the electronic mobile measurement data to include in a model, generate the model based on the attributes and a first portion of the electronic mobile measurement data, determine a percentage of a second portion of the electronic mobile measurement data that the model correctly associates with corresponding first users of the first client devices, and when the percentage satisfies a threshold determine: (a) when a second user operating a second client device is a primary user, and (b) when the user operating the second client device is a third user, and associate demographic information of the second user with the electronic mobile measurement data to reduce a misattribution error.