Generative AI Feed Generation for Scalable Personalized Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Marketers face challenges in creating personalized content for marketing campaigns at scale due to slow and expensive content creation processes, limiting their ability to effectively reach the right audience with the right message at the right time across multiple channels.
Innovation Solution
The Feed Generator system uses Generative Artificial Intelligence to proactively generate multi-modal content, such as text, images, and videos, across channels, while integrating with Customer Data Platforms to monitor performance and suggest real-time optimizations for improved engagement and conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automation tools are used for marketing campaigns, then content creation is enabled, but the process is slow and expensive, limiting personalization at scale
Solution Approach 1:
The patent replaces traditional mechanical content creation processes with Generative AI technology. The system uses AI models to automatically generate personalized content for marketing campaigns, eliminating the need for manual content creation while maintaining personalization capabilities at scale. This substitution dramatically increases productivity while reducing the complexity of manual processes.
Solution Approach 2:
The system enables self-service content generation where the AI model automatically creates personalized marketing content without requiring manual intervention. The platform allows marketers to input basic parameters and audience segments, then the AI autonomously generates tailored content, reducing both time and resource requirements while maintaining high personalization quality.
2Productivity
If manual content creation is used, then quality control is maintained, but the process is time-consuming and cannot scale effectively
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes the performance of generated content and uses this information to refine future generation. The AI model learns from engagement metrics, conversion data, and user behavior patterns to improve content quality consistency. This continuous feedback loop ensures that automated content generation maintains reliable quality standards while achieving high throughput.
Solution Approach 2:
The system employs a universal AI model that can generate multiple types of content (text, images, videos) across different marketing channels simultaneously. This multi-functional capability allows the same AI infrastructure to handle diverse content creation tasks, maintaining quality consistency across all content types while dramatically increasing overall productivity and scaling capability.
3Adaptability or versatility
If personalized content is created for each audience member, then engagement is improved, but the process becomes expensive and slow
Solution Approach 1:
The patent applies segmentation by dividing the audience into distinct segments based on demographics, behavior, and preferences. The AI model then generates personalized content for each segment rather than creating individual content for every single user. This segmentation approach maintains high personalization effectiveness while significantly reducing the computational and resource costs associated with creating content for each individual audience member.
Solution Approach 2:
The system performs preliminary actions by pre-segmenting audiences and pre-generating content variations before actual marketing campaigns execute. The AI model creates personalized content templates and variations in advance, which can then be rapidly deployed to large audiences. This preliminary content generation reduces real-time resource consumption while maintaining the ability to deliver highly personalized content to each audience member when needed.
Data Source
AI summary
Methods, systems, and computer programs are presented for generating personalized content. One method includes an operation for identifying audience parameter values that indicate which users are members of an audience. The method further includes operations for determining attribute values for attributes used to generate items based on the audience parameter values, and generating items for the audience based on the attribute values. Generating each item comprises creating a prompt based on a type of the item, the attribute values, and the audience parameter values; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool. The method further includes causing presentation of the generated items in a user interface (UI), receiving on the UI a selection of one of the items; and transmitting the selected item to one or more members of the audience.