LLM Campaign Generation With Deep Customer Learning Feedback
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Solution Overview
Problem
Traditional campaign management systems require manual feature engineering and are inefficient in capturing nuanced user behavior, leading to suboptimal campaign personalization.
Innovation Solution
A campaign management system utilizing deep customer learning, including a clustering algorithm, CTR prediction model, and reinforcement learning to generate personalized campaigns by identifying target groups, predicting engagement likelihood, and iteratively refining the campaign generator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature engineering is used to identify target groups, then campaign personalization can be achieved, but the process becomes time-consuming and may not fully capture nuanced user behavior
Solution Approach 1:
The patent replaces manual feature engineering with automated deep learning models (including clustering algorithms, CTR prediction models, and reinforcement learning agents) that automatically learn and extract meaningful features and user behavior patterns from data, eliminating the time-consuming manual process while improving capture precision of nuanced user behavior
Solution Approach 2:
The system performs self-learning and self-optimization through reinforcement learning, where the campaign generator automatically adjusts its strategies based on performance feedback without requiring manual feature engineering or human intervention in the learning process
2Adaptability or versatility
If traditional clustering algorithms are used to identify target groups, then basic segmentation is achieved, but nuanced user behavior patterns are not fully captured
Solution Approach 1:
The patent transitions from traditional clustering to deep learning-based models that dynamically adjust parameters and feature representations based on learned patterns, enabling the system to adapt to nuanced user behavior patterns and achieve more accurate target group identification through multi-dimensional feature learning
Solution Approach 2:
The system incorporates reinforcement learning feedback loops where campaign performance data is used to continuously refine and update the deep learning models, enabling iterative improvement in capturing nuanced user behavior patterns and improving target group identification accuracy over time
Data Source
AI summary
A system and method for generating and optimizing marketing campaigns. More specifically, a campaign management system leverages a Large Language Model (LLM) to create multiple variations of an existing campaign tailored to specific target groups. The system employs a cluster-based approach and a click-through rate (CTR) prediction model to generate revised campaigns for targeted readers, thereby creating a feedback loop for further fine-tuning of the LLM for future campaigns.