Decision Intelligence Framework for DSP Campaign Optimization
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Solution Overview
Problem
Existing content generation and delivery systems lack the ability to effectively plan, launch, optimize, and monitor content campaigns across various platforms, leading to inefficiencies and suboptimal user experiences.
Innovation Solution
A decision intelligence (DI)-based computerized framework that utilizes AI and machine learning models to provide DSPs with strategic and data-driven capabilities for content campaign management, including audience targeting, budgeting, and real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI and machine learning models are integrated into the framework, then campaign performance and user experience are improved, but device complexity increases
Solution Approach 1:
The system segments the AI/ML functionality into separate, modular components including a data processing module, pattern recognition module, prediction module, and optimization module. This allows the complex AI framework to be divided into manageable units that can be independently developed, deployed, and maintained, reducing the perceived complexity while maintaining high campaign performance
Solution Approach 2:
The framework introduces an intermediary layer of abstraction between the raw data and the campaign optimization functions. This intermediary includes standardized interfaces and protocols that mediate between different system components, making the complex AI/ML operations transparent to users while delivering improved campaign performance
2Productivity
If real-time optimization and automated bid adjustments are implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The system implements self-service automation where the AI/ML models continuously self-optimize campaign parameters without requiring manual intervention. The automated bid adjustments and real-time optimization run autonomously based on predefined objectives and constraints, significantly improving productivity while the modular architecture keeps the automation complexity manageable
Solution Approach 2:
The framework incorporates feedback mechanisms where campaign performance data is continuously fed back to the AI/ML models to refine future optimizations. This closed-loop feedback system enables real-time adaptation to changing conditions, improving productivity through continuous learning while the feedback architecture is designed to be modular and scalable
3Measurement precision
If comprehensive data analysis and pattern recognition are performed, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary data processing and pattern recognition during off-peak times or in advance, pre-computing insights and predictions before they are needed for campaign optimization. This preliminary action reduces the time required for real-time analysis while maintaining high measurement precision through comprehensive data examination
Solution Approach 2:
The framework selectively applies data analysis to the most critical campaign parameters and audience segments rather than uniformly processing all available data. This partial action approach maintains sufficient measurement precision for effective optimization while significantly reducing overall data processing time by focusing computational resources where they matter most
Data Source
AI summary
Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for demand-side platforms (DSPs) to effectively plan, launch, optimize and monitor the performance of content campaigns over a network on network resources. The disclosed framework operates to perform strategic and data-driven processes for DSP initiatives that can define campaign parameters, and in real-time, monitor the effectiveness of campaigns such that their modifications and/or alterations can be dynamically performed so as to adapt to the changing landscapes of how the campaign is being disseminated over a network and received by users. The framework can implement AI/ML and/or LLM models and functionality to provide DSPs with comprehensive tools for managing, curating and analyzing content related to content campaigns for optimal and accurate performance and impact.