Content Creation Assistance via Attribute Contribution Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the field of WEB advertising, creating contents that increase purchase rates is challenging due to the lack of clear guidelines on which factors of the contents influence purchase rates, resulting in a high reliance on creators' senses and difficulty in producing effective advertising contents.
Innovation Solution
An apparatus, method, and program that assist in creating contents for interventions by calculating contribution degrees of attribute items that contribute to predicted intervention effects, using an estimation model based on target attributes, and generating a display screen that showcases these contribution degrees to guide content creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contents are created based on creators' senses without clear guidelines, then creation flexibility is maintained, but the effectiveness and purchase rates of advertising contents deteriorate
Solution Approach 1:
The patent segments the content creation process by identifying and analyzing specific attribute items (features) of contents separately. The contribution degree calculation unit divides the overall content effectiveness into individual attribute contributions, allowing creators to understand which specific features matter most without having to guess, thus improving reliability while maintaining manageable complexity
Solution Approach 2:
The patent implements feedback by calculating and displaying contribution degrees of each attribute item to content effectiveness. This feedback mechanism provides creators with data-driven insights about which content features actually influence purchase rates, enabling them to make informed decisions and improve advertising effectiveness systematically
2Manufacturing precision
If data-driven analysis of attribute items is implemented, then effectiveness of content creation improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent introduces an intermediary estimation model that bridges raw content attribute data and effectiveness predictions. The model includes a contribution degree calculation unit that acts as an intermediary layer, translating complex data relationships into interpretable contribution scores for each attribute item, achieving precision without overwhelming complexity
Solution Approach 2:
The patent replaces intuitive guesswork and manual trial-and-error methods with automated computational analysis. The estimation model and contribution degree calculation automatically process attribute data to determine effectiveness, substituting mechanical creative processes with data-driven algorithms that provide precise predictions without proportional increases in operational complexity
Data Source
AI summary
A processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target. The predicted intervention effect on the one target is a numerical value corresponding to an increase in gain to a beneficiary, the gain being expected to be larger in a case where an intervention is implemented to the one target than in a case where the intervention is unimplemented to the one target.