Audience Server Targeting Engine for Ad Delivery Optimization
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Solution Overview
Problem
Existing content delivery systems are inefficient in targeting specific audience members, as they often rely on stale information and fail to differentiate between valuable and low-value consumers, leading to wasted resources and lost revenue. Additionally, they are contextually reactive and do not adequately consider individual user characteristics, resulting in inappropriate ad placement and inadequate revenue allocation.
Innovation Solution
A system that collects and aggregates profile data from various sources to optimize ad delivery based on established user profiles, using a targeting engine to select appropriate ads outside of request context and considering performance criteria for publishers, while also allocating revenue based on data provider participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content delivery systems deliver targeted content to maximum quantity of consumers, then reach is improved, but revenue is lost due to inability to differentiate valuable consumers
Solution Approach 1:
The patent segments the audience into different value tiers by analyzing consumer behavior data, purchase history, and engagement metrics. This allows the system to differentiate between high-value and low-value consumers, allocating advertising resources more effectively to maximize revenue while maintaining broad reach.
Solution Approach 2:
The system changes the parameters of content delivery by dynamically adjusting targeting criteria based on consumer value assessment. High-value consumers receive more personalized and frequent targeted content, while lower-value consumers receive less resource-intensive delivery, optimizing the balance between reach and revenue.
2Ease of operation
If systems use reactive ad placement based on request context, then implementation is simple, but individual user characteristics are ignored leading to inappropriate placement
Solution Approach 1:
The system performs preliminary analysis of user characteristics, preferences, and behavior patterns before ad placement decisions are made. This pre-processing of user data enables more accurate and personalized ad matching while maintaining operational efficiency through cached user profiles and pre-computed relevance scores.
3Device complexity
If systems rely on stale information for targeting, then data collection overhead is reduced, but targeting accuracy deteriorates
Solution Approach 1:
The system implements periodic updates of user profile data at strategically determined intervals, balancing the need for current information with the overhead of data collection. This periodic refresh approach ensures targeting accuracy is maintained without requiring continuous real-time data gathering, thus managing system complexity effectively.
4Productivity
If systems deliver content to large volumes of low-value consumers, then market penetration is improved, but system resources are wasted
Solution Approach 1:
The system applies local quality by delivering differentiated content quality and frequency based on consumer value segments. High-value consumers receive premium, highly personalized content with greater resource investment, while lower-value consumers receive standardized content with reduced resource allocation, optimizing the balance between market penetration and resource efficiency.
Data Source
AI summary
Delivery of content such as advertisements to audience members. Profile data is collected regarding audience members to whom advertisements may be delivered, such that a given audience member has an established profile data. Upon receiving a request to serve an advertisement to an audience member, a recognition that the target of the request is the given audience member is made. Then it is determined that a particular advertisement should be served to the given audience member. The determination includes recognition of the given audience member and corresponding selection advertisements optimized for the recognized audience member. A configurable delivery decision making mode allows pre-optimized as well as delivery time factoring for determining advertisements. Performance criteria and revenue allocation based upon data provider participation are also provided.