Grounded Persuasive Language Generation for Automated Marketing
Authors: name: Jibang Wu, name: Chenghao Yang, name: Simon Mahns, name: Chaoqi Wang, name: Hao Zhu, name: Fei Fang, name: Haifeng Xu

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.