Grounded Persuasive Language Generation for Automated Marketing
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.
67bdfc14c45e6063fed00c43
Jibang Wu
67bdfc14c45e6063fed00c44
Chenghao Yang
claimed_verified
2025-02-26T08:28:53.841Z
62fb49bafcce44435d7e079a
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Chenghao Yang
user
chromeNLP
67bdfc14c45e6063fed00c45
Simon Mahns
claimed_verified
2025-02-28T12:15:59.842Z
65c5898e469efddc1c54b873
/avatars/25e19bb239eee9fbc0ca48119891c5a8.svg
simon
user
smahns
67bdfc14c45e6063fed00c46
Chaoqi Wang
67bdfc14c45e6063fed00c47
Hao Zhu
claimed_verified
2025-02-25T17:22:57.723Z
61aa376688c20eebf1e8deb3
/avatars/7c11dcb232c73547d7d87834be287822.svg
Hao Zhu
user
ProKil
67bdfc14c45e6063fed00c48
Fei Fang
67bdfc14c45e6063fed00c49
Haifeng Xu
Grounded Persuasive Language Generation for Automated Marketing
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.
An LLM-based agentic framework generates preferred, factual, and personalized real estate marketing content using three modules: Grounding, Personalization, and Marketing.
large language models (LLMs) Grounding Module Personalization Module Marketing Module systematic human-subject experiments