Audience Measurement System for Contextual Viewer Behavior Analysis
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Solution Overview
Problem
Existing audience measurement systems lack the granularity and context needed to understand viewer behavior effectively, failing to differentiate between intentional and accidental viewers, and do not adequately account for non-content related factors, leading to skewed data that hampers content creators' ability to improve programming and advertising strategies.
Innovation Solution
A system and method that obtain primary and secondary content information, along with viewer activity data, to determine activity component information and display it in context, allowing for the normalization of audience data to isolate content-driven and non-content driven components, providing actionable insights for content creators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing audience measurement systems collect and analyze viewing data, then basic ratings information is obtained, but the data lacks granularity and context to understand true viewer behavior
Solution Approach 1:
The patent segments audience data into multiple components including intentional viewers, accidental viewers, lead-in viewers, and seasonal viewers. This segmentation allows the system to analyze each component separately, providing granular insights into different viewer behaviors while maintaining overall measurement precision.
Solution Approach 2:
The patent adds contextual dimensions to traditional audience measurement by incorporating content information, viewer intent data, and behavioral patterns. This transforms the measurement from a single-dimensional count to a multi-dimensional analysis that preserves rich contextual information about viewer behavior.
2Productivity
If traditional systems analyze all viewers uniformly, then overall ratings are calculated, but intentional and accidental viewers are not differentiated
Solution Approach 1:
The system segments the audience into distinct categories based on viewer intent and behavior patterns. By classifying viewers as intentional, accidental, lead-in, or seasonal, the system maintains efficient processing while preserving critical information about why viewers are watching content.
Solution Approach 2:
The patent applies different analysis methods to different viewer segments. Each segment receives tailored analysis appropriate to its characteristics, such as measuring retention differently for intentional viewers versus accidental viewers, thereby preserving specific information about each group's behavior.
3Ease of operation
If audience data is presented in aggregate form, then overall performance metrics are provided, but actionable insights for content optimization are lost
Solution Approach 1:
The patent presents audience data in segmented components that can be independently analyzed. By breaking down aggregate metrics into intentional viewers, accidental viewers, and other segments, the system maintains simplicity while providing actionable insights about what drives performance in each segment.
Solution Approach 2:
The system adds dimensional breakdowns to performance metrics, presenting data across multiple dimensions such as viewer intent, time of day, and content type. This transforms simple aggregate numbers into multi-dimensional performance information that guides content optimization decisions.
4Reliability
If existing systems do not account for non-content related factors, then content performance is measured in isolation, but external influences on viewership are ignored
Solution Approach 1:
The patent incorporates external context dimensions into content performance measurement by analyzing factors such as lead-in programs, seasonal patterns, and external events. This transforms isolated content metrics into contextually-enriched measurements that account for external influences on viewership.
Data Source
AI summary
Systems and methods for presenting actionable program performance information correlated with content are disclosed. A method includes obtaining primary content information related to first content distributed to a plurality of viewers during a particular time duration; obtaining secondary content information related to the first content, wherein the secondary content information includes information identified based on the first content; obtaining activity information of the plurality of viewers of the first content during the particular time duration; determining a plurality of activity component information corresponding to a plurality of activity categories; storing the plurality of activity component information to be associated with the first content; and displaying data of at least one of the plurality of activity component information at a first time point along with the first content.