Crowd-Sourced Metrics for Broadcast Content via ML Segmentation
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Solution Overview
Problem
Current systems for analyzing broadcast content consumption are limited, relying on small sample groups for extrapolation to the broader audience, lacking comprehensive and real-time metrics for content providers, especially for broadcast media that is 'pushed' to users, and failing to account for time-shifting and user engagement dynamics.
Innovation Solution
A system that provides crowd-sourced metrics for content providers by monitoring individual user consumption across multiple streams, determining available users, content consumption patterns, and event engagement, using modules for content consumption, user management, available user determination, content selection, audience, and event metrics, employing statistical methods and machine learning to predict user behavior and content preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If small sample groups are used for analysis, then data collection cost and complexity are reduced, but measurement precision and reliability of audience metrics deteriorate
Solution Approach 1:
The system segments the audience measurement problem into two parts: (1) a small monitored sample group that provides detailed behavioral data, and (2) a large unmonitored population whose preferences are inferred through machine learning models trained on the sample data. This segmentation allows precise measurement on a small scale while extrapolating to a large population without proportionally increasing complexity.
Solution Approach 2:
Machine learning models serve as an intermediary between the monitored sample group and the unmonitored population. The models learn patterns from the sample data and use these patterns to predict preferences of the broader audience, effectively translating limited direct observations into comprehensive audience metrics without requiring direct monitoring of everyone.
2Measurement precision
If real-time monitoring of individual user consumption is implemented, then audience metrics precision and user behavior insights are improved, but data collection complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and storing detailed user consumption data in advance. User behavior patterns, preferences, and consumption habits are captured and stored as users interact with content, so that when analysis is needed, the data is already organized and ready for processing without requiring complex real-time computation during analysis events.
Solution Approach 2:
The monitoring system is integrated into the user's existing device and interface, making the data collection transparent and self-service. Users interact with content through their normal devices, and the system automatically captures consumption data without requiring additional user actions or complex external monitoring infrastructure.
3Loss of information
If comprehensive user consumption data is collected across multiple streams, then audience metrics completeness and content selection accuracy are improved, but information processing load and storage requirements increase
Solution Approach 1:
The system extracts only the most relevant features and patterns from the comprehensive user consumption data, rather than storing and processing every raw data point. Machine learning models identify and extract key behavioral patterns, preferences, and consumption habits, discarding redundant information while retaining the essential insights needed for accurate audience metrics and content selection.
4Productivity
If machine learning models are used to predict user behavior, then content selection accuracy and audience reach are improved, but computational requirements and processing time increase
Solution Approach 1:
Machine learning models are trained in advance on historical user consumption data from the monitored sample group. This preliminary training phase allows the models to learn user behavior patterns and preferences beforehand, so that during actual content selection and recommendation, the models can make rapid predictions without requiring intensive real-time computation, thus reducing energy consumption during operational use.
Data Source
AI summary
A system and method for providing crowd sourced metrics for broadcast content providers. For determinations of such metrics, user consumption of broadcast content in multiple streams by multiple content providers may be monitored and user consumption information regarding the broadcast content may be obtained. One or more content consumption metrics may be determined to quantify individual user consumption of the broadcast content. Audience metrics may be determined, for a content provider, to inform about users that are available to consume broadcast content provided by the content provider. Events within the broadcast content may be determined and event information regarding individual user consumption of the broadcast content at the event may be obtained. Event metrics may be determined based on the obtained event information to inform about consumption of the broadcast content at the event by users.