Television Engagement Analyzer Correlation
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Solution Overview
Problem
The advertising industry struggles to effectively utilize user-generated content from social media to evaluate and analyze audience engagement levels for televised content, leading to suboptimal decisions regarding program maintenance, scheduling, and commercial pricing.
Innovation Solution
A television engagement analyzer that calculates viewing audience engagement measurements by correlating social media data with television viewing data, using filtering criteria such as keywords, locations, and time stamps to identify relevant social media messages and determine audience engagement levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional television rating points are used to evaluate audience engagement, then the measurement system remains simple and easy to implement, but the accuracy and precision of engagement assessment deteriorates because it fails to account for differences in viewer engagement levels
Solution Approach 1:
The patent combines traditional television rating data with social media data into a unified engagement measurement system. The television engagement analyzer integrates viewership information from traditional metrics with user-generated content from social media platforms, creating a composite engagement score that captures both broad audience reach and deep engagement levels.
Solution Approach 2:
The patent introduces a television engagement analyzer as an intermediary system that processes and correlates data from multiple sources. This analyzer acts as a mediator between traditional rating systems and social media platforms, transforming raw data from both sources into meaningful engagement metrics without requiring direct integration between the original systems.
2Measurement precision
If social media data is collected and analyzed to measure audience engagement, then the precision of engagement measurement improves, but the complexity of the analysis system increases due to the need to process and correlate multiple data sources
Solution Approach 1:
The patent segments the engagement measurement process into distinct functional modules: a data collection component that gathers television and social media data, a filtering component that identifies relevant social media messages using criteria such as keywords and time stamps, and an analysis component that correlates the filtered data with viewership information to generate engagement metrics.
Solution Approach 2:
The patent extracts only the relevant portion of social media data by applying filtering criteria to identify messages specifically related to the televised content being analyzed. This extraction process removes irrelevant noise from the massive volume of social media data, focusing analysis only on meaningful engagement signals.
3Measurement precision
If comprehensive filtering criteria are applied to identify relevant social media messages, then the precision of engagement measurement improves, but the time and computational resources required for data processing increase
Solution Approach 1:
The patent applies filtering criteria such as keywords, time stamps, and location data in advance to pre-filter social media messages before they enter the main analysis pipeline. This preliminary filtering reduces the volume of data requiring detailed processing, enabling the system to handle comprehensive filtering requirements without excessive time costs.
Data Source
AI summary
Audience engagement is analyzed by receiving at least one analysis parameter comprising a content selection parameter, identifying one or more content based on the content selection parameter, determining viewership for the one or more content, determining one or more keywords for the one or more content based on the content, filtering social media messages based on the determined one or more keywords, and calculating an audience engagement measurement corresponding to the one or more content based on the viewership and the social media messages.