Web Audience Measurement Using Persistent Cookie Weighting
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Solution Overview
Problem
Existing internet audience measurement techniques face challenges in accurately estimating the number of unique devices accessing web resources due to issues like cookie rejection and deletion, particularly on mobile devices, which affects the accuracy of traffic flow analysis and advertising planning.
Innovation Solution
A system analyzes beacon data over a reporting period to identify persistent cookies, weights their hits as if they persisted throughout the period, and subdivides data into device categories to estimate the number of unique devices, providing an average number of hits per device and market share.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cookie-based tracking is used to measure audience size, then implementation is simple, but accuracy deteriorates due to cookie rejection and deletion on mobile devices
Solution Approach 1:
The patent segments the measurement approach by dividing the reporting period into multiple time windows and separating persistent cookies from transient cookies. This segmentation allows the system to handle different cookie behaviors independently, improving accuracy by accounting for mobile device cookie deletion patterns while maintaining manageable system complexity through structured data organization.
Solution Approach 2:
The patent applies preliminary action by establishing cookie persistence thresholds and weight calculation rules before the reporting period begins. By pre-defining what constitutes a persistent cookie (e.g., present across multiple time windows) and pre-calculating weight factors, the system prepares the measurement framework in advance, reducing complexity during actual execution while ensuring accurate unique device counting.
2Measurement precision
If persistent cookies are identified and weighted to account for mobile device variability, then measurement accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent introduces dynamics by implementing weight calculations that adapt to each cookie's persistence pattern. Instead of uniform counting, the system dynamically assigns weights based on how long cookies persist across time windows, allowing the measurement system to flexibly accommodate varying mobile device behaviors while maintaining processing efficiency through algorithmic optimization.
Solution Approach 2:
The patent applies parameter changes by transforming the measurement approach from simple cookie counting to weighted counting based on persistence parameters. By changing the parameter from binary presence/absence to continuous weight values reflecting cookie longevity, the system achieves higher measurement accuracy while managing complexity through standardized weight calculation formulas applied consistently across all cookies.
3Reliability
If the system accounts for transient cookies and mobile device variability, then measurement reliability improves, but computational requirements increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on identifying and weighting only the persistent cookies that contribute most reliably to unique device counts. Rather than exhaustively analyzing every single cookie event, the system selectively processes cookies meeting persistence thresholds, achieving sufficient measurement reliability while conserving computational resources by ignoring transient noise.
Data Source
AI summary
Evaluating web activity is disclosed. Initially, activity data for one or more resources on a network is received for a predetermined time period. The resources have been accessed by a plurality of client systems, and the activity data includes a unique identifier and a category of an accessing client system for each access to the one or more resources. Next, at least one persistent identifier of a client system within the activity data is identified. A subset of the activity data associated with the at least one persistent identifier is also identified. Based on the subset of the activity data, a total number of accesses to the one or more resources from the client system having the persistent identifier is determined. Finally, an estimated number of accesses to the one or more resources from the client system is determined if the persistent identifier persisted on the client system during the entire predetermined time period.