As e-commerce competition intensifies, balancing creative content with conversion effectiveness becomes critical. Leveraging LLMs' language generation capabilities, we propose a framework that integrates prompt engineering, multi-objective fine-tuning, and post-processing to generate marketing copy that is both engaging and conversion-driven. Our fine-tuning method combines sentiment adjustment, diversity enhancement, and CTA embedding. Through offline evaluations and online A/B tests across categories, our approach achieves a 12.5 % increase in CTR and an 8.3 % increase in CVR while maintaining content novelty. This provides a practical solution for automated copy generation and suggests paths for future multimodal, real-time personalization.
@article{arxiv.2505.23809,
title = {LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion},
author = {Haowei Yang and Haotian Lyu and Tianle Zhang and Dingzhou Wang and Yushang Zhao},
journal= {arXiv preprint arXiv:2505.23809},
year = {2025}
}