Semantic Modeling for Automated Ad Creative Generation
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Solution Overview
Problem
Managing complex paid search advertising campaigns for websites with numerous products or services is challenging due to the need for optimizing advertising creatives based on relevance and performance, which is often manual and inefficient.
Innovation Solution
The development of a system and method for semantic modeling of advertising creatives using a processor-based device that analyzes existing creatives, keywords, and performance data to generate recommended strategies and updated creatives, leveraging semantic models and global performance data to improve campaign performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual optimization of advertising creatives is used, then flexibility and control are maintained, but productivity and efficiency deteriorate due to the complex and time-consuming nature of managing numerous products or services
Solution Approach 1:
The system enables self-service by automatically generating and optimizing advertising creatives through semantic modeling. The processor-based device autonomously analyzes product data, generates relevant creatives, and optimizes campaign performance without requiring manual intervention for each creative element, thus resolving the contradiction between maintaining control and improving productivity
Solution Approach 2:
The semantic modeling system serves multiple functions simultaneously: it generates creatives, optimizes targeting, analyzes performance, and manages campaign structures. This multi-functional approach consolidates numerous manual tasks into a single automated system, improving productivity while managing complexity
2Productivity
If automated systems are introduced to improve productivity, then efficiency increases, but device complexity and system sophistication increase
Solution Approach 1:
The system replaces manual mechanical processes of creative development with automated semantic modeling and machine learning algorithms. The processor-based device uses semantic models to automatically generate and optimize creatives, substituting human manual work with intelligent automation that improves productivity while managing complexity through algorithmic processes
Solution Approach 2:
The system changes parameters by dynamically adjusting semantic model configurations, performance thresholds, and optimization criteria based on campaign data. This allows the automated system to adapt to different campaign requirements without requiring complete system redesign, managing complexity through flexible parameter adjustment rather than structural complexity
3Measurement precision
If comprehensive performance tracking is implemented to improve measurement precision, then campaign optimization improves, but loss of time and data processing complexity increase
Solution Approach 1:
The system implements continuous feedback loops where performance data is automatically collected, analyzed, and used to optimize future creative generation. The processor-based device uses performance metrics to refine semantic models and improve creative performance over time, achieving precise measurement while reducing time loss through automated feedback processing rather than manual analysis
Solution Approach 2:
The system performs preliminary actions by pre-processing and structuring performance data as it is collected, preparing it for analysis in advance. This preliminary data preparation reduces the time required for subsequent analysis and optimization, achieving both measurement precision and time efficiency through proactive data management
Data Source
AI summary
Systems and methods for the semantic modeling of advertising creatives included in targeted search advertising campaigns in accordance with embodiments of the invention are disclosed. In one embodiment, an advertising creative generation device includes a processor, an advertising creative generation application, at least one semantic model and performance data, wherein the an advertising creative generation application configures the processor to obtain a set of existing advertising creatives, where at least one of the existing advertising creatives comprises an existing concept, identify at least one existing advertising strategy pattern, determine performance data for the at least one existing advertising strategy pattern based on the performance data, identify at least one performance pattern based on the performance data for the at least one existing advertising strategy pattern, and generate at least one recommended advertising strategy.