AI Content Generation Using Performance Feedback for Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Marketers face challenges in creating personalized content at scale due to slow and expensive processes, limiting their ability to effectively reach the right audience with the right message across multiple channels.
Innovation Solution
A system utilizing Generative Artificial Intelligence (GAI) to proactively generate multi-modal content, integrated with Customer Data Platforms (CDPs), monitors performance, and provides real-time insights for optimization, enabling efficient and personalized content creation across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automation tools are used for marketing campaigns, then content creation can be automated to some extent, but the process remains slow and expensive, preventing effective personalization at scale
Solution Approach 1:
The system enables self-service content creation by allowing the AI model to autonomously generate personalized content messages for audience members without requiring manual intervention. The model automatically segments audiences, creates personalized messages, and determines optimal send times, transforming the content creation process from manual to autonomous operation.
Solution Approach 2:
The patent replaces manual mechanical content creation processes with an AI-based system that uses machine learning models to generate content. The AI model analyzes customer data, predicts preferences, and automatically creates personalized messages, substituting human creativity and manual drafting with automated intelligent systems.
2Reliability
If manual content creation processes are used, then quality control and creativity can be maintained, but the time consumption and cost increase significantly
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model continuously learns from campaign performance data, customer responses, and engagement metrics. This feedback loop enables the model to refine its content generation capabilities over time, improving quality while maintaining speed. The model analyzes what content performs best and automatically adjusts future content creation strategies.
Solution Approach 2:
The system performs preliminary actions by pre-segmenting audiences, pre-generating content variations, and pre-determining optimal send times before actual campaign execution. The AI model proactively creates multiple content variants and analyzes historical data to predict best performance scenarios in advance, eliminating the need for last-minute manual content creation.
3Productivity
If content is personalized for each audience member, then engagement and conversions improve, but the complexity and cost of content creation increases
Solution Approach 1:
The system divides the broad audience into segmented groups based on customer data, behavior patterns, and preferences. The AI model automatically segments audiences and creates personalized content for each segment, making the complex task of individual customization manageable through structured grouping. This segmentation approach enables personalized content at scale without overwhelming complexity.
Solution Approach 2:
The system changes parameters such as messaging tone, content format, timing, and channel selection based on analyzed customer characteristics. The AI model adjusts multiple content parameters automatically for different audience segments, enabling personalization through systematic parameter variation rather than creating entirely new content from scratch for each individual.
Data Source
AI summary
Methods, systems, and computer programs are presented for proactively generating content based on performance of previous content. One method includes an operation for transmitting a set of first items for presentation. The first items are multimodal, and each first item has values for attributes associated with the first item. The method further includes tracking performance of the transmitted set of first items, selecting values of attributes based on the tracked performance, and proactively generating, using one or more generative artificial intelligence (GAI) tools, a set of second items based on the selected values of the attributes. Further, the method includes operations for providing a user interface (UI) with an option to select from the set of second items, receiving in the UI a selection of selected second items for transmittal, and transmitting the selected second items to one or more users.