Audience Classification System Using Psychological Profiles
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Solution Overview
Problem
Current technologies lack a system or method to determine appropriate taxonomical classifications of content for a target audience before developing digital environments, especially based on psychological attributes of the audience.
Innovation Solution
A system that receives an audience definition with psychological parameters, correlates user interaction information with taxonomical classifications of content, and identifies suitable content for a prospective audience by analyzing user profiles and interaction data, presenting the appropriate content classifications to developers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If content classifications are determined based on general audience assumptions, then development process is simpler, but content relevance to target audience deteriorates
Solution Approach 1:
The system performs preliminary analysis of audience psychological profiles and content interaction data before the development process begins. By pre-determining the taxonomical classifications that align with target audience psychological attributes, the system enables developers to create content that is inherently relevant to the audience from the outset, rather than attempting to adjust content after development.
2Measurement precision
If psychological profiles and interaction data are collected and analyzed, then content relevance to audience improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that bridges raw psychological profile data and interaction information with the content development process. This intermediary component automatically performs the complex tasks of data collection, psychological attribute extraction, and taxonomical classification correlation, transforming complex data processing into a manageable system layer that developers can utilize without directly handling the complexity.
3Productivity
If content is designed for general audience, then development time is reduced, but user engagement for target audience deteriorates
Solution Approach 1:
The system changes the parameters used for content classification from generic categories to psychologically-informed taxonomical classifications. By transforming content parameters to align with specific audience psychological attributes (such as personality traits, motivations, and behavioral patterns), the system enables faster development of targeted content that naturally engages the intended audience without requiring extensive post-development optimization.
Data Source
AI summary
Systems and methods to identify taxonomical classifications of target content for prospective audience are disclosed. Exemplary implementations may: receive, via a client computing platform, an audience definition for a prospective audience; identify a set of the users based on the psychological profiles that include similar psychological parameter values as indicated by the audience definition; correlate one or more combinations of content parameter values with the prospective audience based on the interaction information characterizing interactions between the set of the users and the content parameter values that characterize the pieces of content that the set of the users interacted with; identify a set of prospective content, from the pieces of content with taxonomical classifications stored in the electronic storage, for the prospective audience based on the correlated one or more combinations of the content parameter values and the taxonomical classifications of the pieces of content stored in the electronic storage.