LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912410723418112 |
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| author | Yang, Haowei Lyu, Haotian Zhang, Tianle Wang, Dingzhou Zhao, Yushang |
| author_facet | Yang, Haowei Lyu, Haotian Zhang, Tianle Wang, Dingzhou Zhao, Yushang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion Yang, Haowei Lyu, Haotian Zhang, Tianle Wang, Dingzhou Zhao, Yushang Computation and Language Artificial Intelligence Information Retrieval 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. |
| title | LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2505.23809 |