用大模型生成既吸引人又促转化的电商文案
LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion
- 结合提示工程与多目标微调，优化文案创意与转化率
- 线上测试显示点击率提升12.5%，转化率提升8.3%
- 适合需要自动化营销内容生成的电商团队
随着电商竞争加剧，平衡内容创意与转化效果日益关键。我们利用大语言模型的语言生成能力，提出一个整合提示工程、多目标微调和后处理的框架，生成兼具吸引力与转化力的营销文案。微调方法融合情感调节、多样性增强和行动号召嵌入。通过跨品类的离线评估与线上A/B测试，该方法使点击率提升12.5%，转化率提升8.3%，同时保持内容新颖性。为自动化文案生成提供了实用方案，并指明了未来多模态、实时个性化的发展方向。
原文摘要 · Abstract (English)
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.
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