Audience Metric Clustering for Device-Level Viewing Precision
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Solution Overview
Problem
Existing methods fail to accurately reflect the relationship between device categories and content viewing time, particularly in determining audience metrics for content delivery, as they do not adequately consider individual device viewing levels.
Innovation Solution
Devices are grouped into subsets using a clustering algorithm based on content viewership data, with an index parameter to rank content delivery spots by the likelihood of including a significant audience segment with a particular extent of viewership, and weights are assigned to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If devices are grouped into subsets based on content viewership data using clustering algorithms, then audience metric determination precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments devices into multiple subsets based on content viewership data using clustering algorithms. Each subset represents devices with similar viewing patterns, allowing for more precise audience metric determination. This segmentation enables the system to analyze and target specific audience segments rather than treating all devices uniformly, thereby improving measurement precision while managing complexity through structured data organization.
2Measurement precision
If individual device viewing levels are considered in parameter determination, then audience metric accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by considering individual device viewing levels within each clustered subset. Rather than applying a uniform analysis to all devices, the system evaluates viewing patterns at the individual device level within their respective subsets, allowing for more accurate audience metric determination that reflects actual viewing behavior variations across different devices and audience segments.
Data Source
AI summary
Methods, systems, and apparatuses for audience metric determination are described herein. An audience segment may be targeted for delivery of content. A clustering algorithm may be used to categorize a quantity of users or devices into subsets based on a propensity to consume, present or output a particular type of content, and a quantity of time to output the particular type of content. A weight may be assigned to each subset based on its relevance to other subsets, such as based on data variance, e.g., on a distance to a midpoint of a specific subset of the subsets. An index parameter may be determined for the datasets, e.g., based on each weight for each subset, and data may be generated that reflects a ranking of content delivery spots for delivery of content to the audience segment.