Data Element Optimization Using Demographic Predictions
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Solution Overview
Problem
Conventional data element optimization is complex, time-consuming, and often not quantified, particularly for producers of online videos and ads who face budget constraints and multiple objectives, making it difficult to prioritize objectives while meeting constraints in real-time.
Innovation Solution
Systems and methods for optimizing data element distribution by receiving user-defined objectives and constraints, developing predictions based on simulated performance, adjusting objectives and resources dynamically based on actual performance data, and revising resource allocation to meet performance targets efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data element optimization is used, then resource allocation can be performed, but the process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal resource allocation strategies based on historical performance data and demographic information. When optimization is needed, the system retrieves pre-computed recommendations rather than performing complex real-time calculations, significantly reducing optimization time while maintaining solution quality.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between raw performance data and optimization decisions. This intermediary layer processes and structures data into standardized formats, making the optimization process more manageable and less complex by separating data preparation from decision-making logic.
2Adaptability or versatility
If multiple objectives are considered, then comprehensive optimization can be achieved, but prioritizing objectives becomes difficult
Solution Approach 1:
The system transforms multiple qualitative objectives into quantifiable parameters with assigned weights. By converting objectives like 'engagement' and 'conversion' into measurable metrics with configurable importance weights, the system enables automated prioritization through mathematical optimization rather than manual judgment, making multi-objective optimization both comprehensive and operationally simple.
Solution Approach 2:
The system implements dynamic objective prioritization where weights and priorities are not fixed but adapt based on real-time performance data, campaign goals, and demographic insights. This dynamic adjustment allows the system to automatically re-prioritize objectives as conditions change, eliminating the need for manual re-evaluation while maintaining versatility.
3Speed
If real-time optimization is performed, then current performance can be optimized, but meeting all constraints simultaneously becomes impossible
Solution Approach 1:
The system applies partial optimization by focusing on the most critical constraints and objectives at any given moment rather than attempting to perfectly satisfy all constraints simultaneously. It identifies and addresses the most impactful constraints first, achieving satisfactory results in real-time while accepting that not all constraints can be fully satisfied at once, thus balancing speed and reliability.
Data Source
AI summary
Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving one or more user-defined objectives associated with a group of data elements, wherein at least one of the user-defined objectives includes an objective related to a selected target group; receiving one or more constraints associated with the group of data elements, wherein at least one of the constraints comprises resources apportionable to each data element in the group of data elements; developing a first prediction of a performance of the group of data elements during a time period based on the one or more user-defined objectives and the one or more constraints; and apportioning at least a portion of the resources to each data element in the group of data elements based on the first prediction once the time period has started.