Analytic System for Dynamic Ad Insertion and ROI Optimization
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Solution Overview
Problem
Traditional methods for determining effective advertisement placements and pricing in media broadcasting are becoming obsolete due to the evolving manner of media consumption across various platforms, and existing technologies fail to accurately calculate return on investment (ROI) for advertisers, leading to inefficient ad selection and placement.
Innovation Solution
A system and method that utilizes multiple analytic sources to analyze and derive the best ROI for advertisement campaigns by selecting and inserting targeted advertisements based on viewer data, content metadata, and quality of experience (QoE), allowing for dynamic content creation and optimization across connected TV, web, mobile, and social platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertisement placement methods are used, then implementation is simple, but ROI calculation accuracy deteriorates
Solution Approach 1:
The system segments advertisement placement into multiple independent decision dimensions including content type matching, platform selection, timing optimization, and audience targeting. Each dimension is evaluated separately using specific metrics, allowing complex ROI calculation to be broken down into manageable analytical components that can be processed systematically
Solution Approach 2:
The patent introduces an intermediary analytic system that sits between traditional ad placement and the final advertisement delivery. This intermediary layer collects data from multiple sources, processes it through various algorithms, and generates optimized placement recommendations, thereby improving ROI accuracy without requiring direct modification of the core advertisement delivery infrastructure
2Productivity
If multiple analytic sources are integrated, then ROI optimization improves, but data processing complexity increases
Solution Approach 1:
The system merges multiple analytic data sources including content metadata, audience demographics, platform performance metrics, and historical advertisement effectiveness into a unified analytical framework. This consolidation allows the system to process diverse data types through standardized algorithms, improving advertising sales efficiency while managing data processing complexity through integration rather than separate handling of each source
Solution Approach 2:
The patent creates a universal analytic platform that handles multiple types of data sources and applies them across different advertisement placement scenarios. The same core algorithms and processing mechanisms are used whether analyzing TV, digital, or social media advertisements, providing multi-functional capability that improves productivity without proportionally increasing processing complexity
3Reliability
If personalized content and advertisements are created, then viewer engagement improves, but content creation complexity increases
Solution Approach 1:
The system implements self-service content creation through automated algorithms that generate personalized advertisement content based on audience profiles and performance data. The system automatically selects appropriate content elements, formats, and delivery timing without requiring manual intervention for each personalized piece, thereby improving viewer engagement while managing content creation complexity through automation
Solution Approach 2:
The patent applies preliminary action by pre-segmenting audience groups and pre-configuring content variations based on predicted performance. Audience profiles and content templates are prepared in advance, allowing the system to quickly assemble personalized advertisements by selecting from pre-prepared components rather than creating entirely new content for each viewer, thus improving engagement while reducing real-time creation complexity
Data Source
AI summary
An analytic platform, article of manufacture, system, computer-readable medium, and method for selecting and inserting advertisements for delivery to a content viewing device. A plurality of advertising metrics are generated from data originating from a plurality of content viewing devices. Then, an advertisement is selected for presentation along with content directed to one of the content viewing devices, the advertisement being selected based on the advertising metrics. Once selected, the advertisement is added to the content for delivery to said one of the content viewing devices.