title: Grounded Persuasive Language Generation for Automated Marketing
summary: This paper develops an agentic framework that employs large language models
  (LLMs) to automate the generation of persuasive and grounded marketing content,
  using real estate listing descriptions as our focal application domain. Our
  method is designed to align the generated content with user preferences while
  highlighting useful factual attributes. This agent consists of three key
  modules: (1) Grounding Module, mimicking expert human behavior to predict
  marketable features; (2) Personalization Module, aligning content with user
  preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion
  of localized features. We conduct systematic human-subject experiments in the
  domain of real estate marketing, with a focus group of potential house buyers.
  The results demonstrate that marketing descriptions generated by our approach
  are preferred over those written by human experts by a clear margin. Our
  findings suggest a promising LLM-based agentic framework to automate
  large-scale targeted marketing while ensuring responsible generation using only
  facts.
paper_authors._id: 67bdfc14c45e6063fed00c43
paper_authors.name: Jibang Wu
paper_authors._id: 67bdfc14c45e6063fed00c44
paper_authors.name: Chenghao Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T08:28:53.841Z
paper_authors._id: 62fb49bafcce44435d7e079a
paper_authors.avatarUrl: /avatars/116cb4371206ee7010e161c986b09e85.svg
paper_authors.fullname: Chenghao Yang
paper_authors.type: user
paper_authors.user: chromeNLP
paper_authors._id: 67bdfc14c45e6063fed00c45
paper_authors.name: Simon Mahns
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-28T12:15:59.842Z
paper_authors._id: 65c5898e469efddc1c54b873
paper_authors.avatarUrl: /avatars/25e19bb239eee9fbc0ca48119891c5a8.svg
paper_authors.fullname: simon
paper_authors.type: user
paper_authors.user: smahns
paper_authors._id: 67bdfc14c45e6063fed00c46
paper_authors.name: Chaoqi Wang
paper_authors._id: 67bdfc14c45e6063fed00c47
paper_authors.name: Hao Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T17:22:57.723Z
paper_authors._id: 61aa376688c20eebf1e8deb3
paper_authors.avatarUrl: /avatars/7c11dcb232c73547d7d87834be287822.svg
paper_authors.fullname: Hao Zhu
paper_authors.type: user
paper_authors.user: ProKil
paper_authors._id: 67bdfc14c45e6063fed00c48
paper_authors.name: Fei Fang
paper_authors._id: 67bdfc14c45e6063fed00c49
paper_authors.name: Haifeng Xu
paper_title: Grounded Persuasive Language Generation for Automated Marketing
paper_summary: This paper develops an agentic framework that employs large language models
  (LLMs) to automate the generation of persuasive and grounded marketing content,
  using real estate listing descriptions as our focal application domain. Our
  method is designed to align the generated content with user preferences while
  highlighting useful factual attributes. This agent consists of three key
  modules: (1) Grounding Module, mimicking expert human behavior to predict
  marketable features; (2) Personalization Module, aligning content with user
  preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion
  of localized features. We conduct systematic human-subject experiments in the
  domain of real estate marketing, with a focus group of potential house buyers.
  The results demonstrate that marketing descriptions generated by our approach
  are preferred over those written by human experts by a clear margin. Our
  findings suggest a promising LLM-based agentic framework to automate
  large-scale targeted marketing while ensuring responsible generation using only
  facts.
paper_ai_keywords: large language models (LLMs) Grounding Module Personalization Module Marketing Module