LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion

TL;DR

This paper presents a framework using large language models to generate e-commerce marketing content that balances creativity with conversion, achieving significant improvements in click-through and conversion rates.

Contribution

It introduces a novel fine-tuning approach combining sentiment, diversity, and CTA embedding to optimize marketing copy for engagement and effectiveness.

Findings

12.5% increase in CTR

8.3% increase in CVR

maintains content novelty

Abstract

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.