Real-Time Content Selection Engine Using Coded Rules
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Solution Overview
Problem
Current marketing systems are inefficient in providing real-time, customized content to customers due to high human and financial costs, relying on traditional targeted marketing paradigms that analyze historical data and push messages without proactive engagement.
Innovation Solution
A centralized system that evaluates viewer characteristics and situational context against coded rules to select and prioritize optimal content for display, allowing real-time content selection and management, with rules stored in an XML structure for easy administration and scalability across multiple servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional targeted marketing paradigm is used to analyze historical data and push messages offline, then customer segmentation can be achieved, but real-time personalized content delivery cannot be provided
Solution Approach 1:
The system performs preliminary actions by pre-defining content items with associated coded rules that describe ideal viewer profiles and presentation opportunities. When a content selection request is made, the system evaluates viewer characteristics against these pre-prepared rules, enabling real-time content selection without requiring complex offline analysis for each request.
Solution Approach 2:
The system creates simplified copies of customer profiles and content characteristics in the form of coded rules. Instead of analyzing complete historical data warehouses in real-time, the system uses these rule-based representations to quickly match viewers with appropriate content, achieving real-time performance while maintaining personalization.
2Adaptability or versatility
If customized content and personalization initiatives are implemented, then customer engagement improves, but human and financial costs become prohibitive
Solution Approach 1:
The system enables self-service by allowing content items to automatically evaluate themselves against viewer characteristics using coded rules. Each content item carries its own rules describing ideal presentation conditions, eliminating the need for complex centralized decision-making systems and reducing both implementation and maintenance costs.
Solution Approach 2:
The system changes parameters by representing complex customer profiles and content characteristics as simplified coded rules with defined priorities. This parameter transformation allows the system to handle personalization at scale without proportionally increasing system complexity, as the rules can be efficiently evaluated and prioritized.
3Measurement precision
If data warehouse intensive traditional model is used for content selection, then comprehensive analysis is possible, but real-time content selection cannot be achieved
Solution Approach 1:
The system extracts only the essential elements needed for content selection from comprehensive data warehouses, encapsulating them in coded rules associated with each content item. This extraction allows the system to maintain measurement precision by preserving key customer and content characteristics while achieving real-time selection speeds by avoiding processing of the complete data warehouse for each request.
Data Source
AI summary
A rules evaluation engine operable to select optimal content for presentation to the viewer at each presentation opportunity. The engine evaluates segmentation rules associated with each particular content item in parallel, and then selects the best content to be presented. Priorities determined during evaluation sort out which content items will be presented. Real time dynamic enrichment of the decision making context occurs by retrieving additional information required to evaluate the rules. Logging and administrative processes for managing the segmentation rules are also realized.