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

2025-05-27

AI Summary

To address the challenge of simultaneously achieving creativity and conversion efficacy in e-commerce copywriting, this paper proposes a marketing-optimized large language model (LLM) content generation framework. Methodologically, we introduce a novel multi-objective supervised fine-tuning strategy integrating emotional regulation, diversity enhancement, and call-to-action (CTA) embedding, complemented by a prompt-engineering–driven and rule-guided post-processing mechanism to jointly optimize semantic novelty and behavioral guidance during generation. Experimental results from large-scale online A/B tests across multiple product categories demonstrate statistically significant improvements: +12.5% in click-through rate (CTR) and +8.3% in conversion rate (CVR), while maintaining target-level textual novelty. This work establishes a reusable technical pipeline and empirically validated benchmark for LLM-driven, precision marketing content generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Balancing creative content and conversion effectiveness in e-commerce
• Optimizing marketing copy using LLMs for engagement and conversions
• Improving CTR and CVR while maintaining content novelty

Innovation

• LLM-driven prompt engineering for content generation
• Multi-objective fine-tuning with sentiment adjustment
• Post-processing for diversity and CTA embedding