Digital Promotion Generation Using Historical Embeddings and AI
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Solution Overview
Problem
Existing systems for generating digital promotions lack efficiency and accuracy in automating the process of creating personalized and effective promotional content for products, particularly in digital form, and do not leverage historical data effectively for predictive modeling.
Innovation Solution
A digital promotion generating system utilizing multi-modal AI models to analyze historical promotion data, generate promotion parameters, and create suggested descriptions and imagery based on product content, leveraging embedding models and predictive analytics to optimize promotional strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for creating digital promotions, then customization and creativity can be achieved, but the process is time-consuming and lacks efficiency
Solution Approach 1:
The system enables automatic self-generation of digital promotions by utilizing historical promotion data and multi-modal AI models. The server autonomously generates promotion parameters, descriptions, and imagery without requiring manual human intervention for each promotion creation, thereby significantly improving productivity and reducing time loss.
2Measurement precision
If historical promotion data is not leveraged, then the system remains simple, but accuracy and effectiveness of promotional content are reduced
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical promotion data in advance, and using embedding models to create searchable representations of this data. This pre-processing enables the multi-modal AI models to accurately generate new promotions by referencing proven historical patterns, thereby improving accuracy while managing complexity through structured data preparation.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing historical promotion data and redemption information to continuously improve future promotion generations. The embedding models and AI models learn from past performance data, adjusting generation parameters to enhance accuracy based on what has proven effective historically.
3Manufacturing precision
If multi-modal AI models are used to generate promotion content, then accuracy and personalization are improved, but computational resources and processing time increase
Solution Approach 1:
The system segments the promotion generation process into distinct functional modules: embedding model for historical data processing, first multi-modal AI model for parameter generation, and second multi-modal AI model for description and imagery generation. This segmentation allows each component to specialize in specific tasks, improving overall content quality while enabling efficient resource management and parallel processing.
4Productivity
If the system automates the entire promotion generation process, then efficiency is improved, but flexibility and human control are reduced
Solution Approach 1:
The system implements dynamic control by allowing users to input specific product information, branding requirements, and promotional objectives, which then guide the AI models in generating customized promotions. The system adapts its generation process based on user inputs while maintaining automated efficiency, providing flexibility without sacrificing productivity.
Data Source
AI summary
A digital promotion generating system may include a user promotion generator device and a digital promotion generating server. The server may cooperate with the user promotion generator device to obtain content relating to a proposed digital promotion for a product for purchase and store historical promotion data associated with historical digital promotions. The server may generate a historical promotion embedding database based upon the stored historical promotion data and operate a first large language model (LLM) to generate an embedding and identify similar historical promotions to the proposed promotion based upon the content and the embedding database. The server may operate a first multi-modal AI model to generate promotion parameters and operate a second multi-modal AI model to generate a suggested description based upon the historical promotion data and the promotion parameters. The server may communicate the suggested description and the parameters to the user promotion generator device.