Contextual Targeting via Consumer Content Correlation
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Solution Overview
Problem
Existing audience targeting methods face challenges in reaching consumers without available audience data, necessitating a mechanism to predict audiences based on contextual information rather than demographic data.
Innovation Solution
The method involves categorizing content into contextual categories and determining correlations between consumer categories and content categories, associating consumer categories with profiles based on these correlations to predict audience interests and habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demographic audience data is used for targeting, then audience targeting accuracy is improved, but data availability and coverage deteriorate when audience data is not available
Solution Approach 1:
The patent introduces contextual category data as an intermediary between content and audience targeting. Instead of directly using demographic audience data, the system uses contextual categories (themes, subjects, topics) of content as a mediator to infer and predict audience characteristics, thereby achieving targeting accuracy without requiring direct access to demographic data.
Solution Approach 2:
The patent replaces the mechanical system of direct demographic data collection and matching with a computational system that analyzes contextual categories and predicts audience characteristics through correlation analysis. This substitution allows the system to function without traditional audience data infrastructure.
2Adaptability or versatility
If contextual category analysis is performed to predict audiences, then audience prediction capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of audience prediction into manageable components: (1) categorizing content into contextual categories (themes, subjects, topics), (2) analyzing correlations between consumer categories and content categories, and (3) predicting audience characteristics based on these correlations. This segmentation reduces system complexity by breaking down the prediction process into discrete, manageable steps.
Solution Approach 2:
The patent performs preliminary categorization of content into contextual categories before audience prediction. By pre-establishing the contextual framework and correlation relationships between consumer and content categories, the system prepares the necessary analytical structure in advance, simplifying the actual prediction process when audience data is needed.
Data Source
AI summary
Data indicative of content associated with at least one content category may be received from a content provider. Data indicative of a plurality of consumer categories may also be received. A correlation between at least one consumer category of the plurality of consumer categories and the at least one content category may be determined. It may be determined if the correlation between the at least one consumer category and the at least one content category satisfies a threshold. If the correlation between the at least one consumer category and the at least one content category satisfies the threshold, the at least one consumer category may be associated to a profile associated with the at least one content category.