Beacon-Based Usage Measurement with Panel-Centric Adjustments
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Solution Overview
Problem
Existing internet audience measurement technologies face challenges in accurately translating machine-based resource access data to person-based metrics due to issues like cookie deletion and dynamically assigned IP addresses, particularly when multiple devices are used by the same individual.
Innovation Solution
A combination of beacon-based data collection and panel-centric data analysis is employed to determine households with specified client devices, using techniques such as cookie-per-person and machine overlap adjustments to refine measurements, ensuring accurate counting of unique visitors across different device types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine-based resource access data is used for audience measurement, then data collection is simplified, but measurement precision deteriorates due to cookie deletion and dynamically assigned IP addresses
Solution Approach 1:
The patent introduces panel-centric data as an intermediary layer between machine-based resource access data and person-based audience metrics. This panel data acts as a mediator that connects device identifiers to individual users, enabling accurate audience measurement without requiring direct reliance on unreliable cookies or IP addresses. The panel data includes device identifiers, user identifiers, and usage information that bridges the gap between machine and person-based measurement.
Solution Approach 2:
The patent replaces the mechanical cookie-based tracking system with a panel-centric data system. Instead of relying on cookies that are deleted or IP addresses that change dynamically, the system uses a structured panel database that stores persistent device identifiers and their associations with users. This substitution fundamentally changes the measurement mechanism from fragile mechanical tracking to a more robust database-driven approach.
2Adaptability or versatility
If multiple devices are used by the same individual, then device diversity increases, but measurement precision deteriorates due to difficulty in translating machine-based data to person-based metrics
Solution Approach 1:
The patent segments the audience measurement problem into distinct components: device identifiers, user identifiers, and usage information. By segmenting the data structure this way, the system can handle multiple devices per user independently while maintaining the ability to aggregate at the user level. The panel-centric data is segmented by device type (mobile, tablet, desktop) and user characteristics, allowing precise measurement across device diversity.
Solution Approach 2:
The panel-centric data structure serves multiple functions simultaneously: it tracks device identifiers, identifies users, records usage patterns, and enables cross-device attribution. This universal data structure handles various device types (mobile phones, tablets, desktop computers) uniformly, allowing the same measurement system to accurately count unique visitors regardless of which device they use.
3Ease of operation
If cookie-based tracking is used, then device identification is simplified, but reliability deteriorates due to cookie deletion and dynamic IP addresses
Solution Approach 1:
The patent creates a persistent copy of device identification information within the panel-centric data structure. Instead of relying on the original cookie that may be deleted or the dynamic IP address that changes, the system creates and stores a stable copy of the device identifier and its association with the user in the panel database. This copied information maintains the simplicity of device identification while ensuring reliability through persistent storage.
Data Source
AI summary
Methods and systems for determining usage are described. Initially, site-centric data and panel-centric data are accessed and pre-processed. Initial usage measurement data is determined based on the pre-processed site-centric data. One or more adjustment factors are determined based on the pre-processed panel-centric data. The one or more adjustment factors are applied to the initial usage measurement data to generate an adjusted usage measurement data. Reports based on the adjusted usage measurement data are generated.