Grounded Persuasive Language Generation for Automated Marketing
Jibang Wu, Chenghao Yang, Simon Mahns, Chaoqi Wang, Hao Zhu, Fei Fang, Haifeng Xu
2025-02-25
Summary
This paper talks about a new AI system that can automatically write persuasive marketing content for real estate listings, using advanced language models to create descriptions that are both appealing and factually accurate
What's the problem?
Writing effective marketing content for real estate listings is time-consuming and requires skill to balance attractiveness with truthfulness. It's challenging to create descriptions that appeal to specific buyers while also highlighting important features of the property
What's the solution?
The researchers created an AI agent with three main parts: one that identifies marketable features like a human expert would, another that tailors the content to what potential buyers prefer, and a third that ensures the description is accurate and includes local details. They tested this system by having real potential home buyers compare AI-generated descriptions to ones written by human experts
Why it matters?
This matters because it could revolutionize how real estate marketing is done, making it faster and potentially more effective than human-written descriptions. It shows that AI can create persuasive marketing content that people actually prefer, while still sticking to the facts. This could lead to more efficient and personalized marketing in real estate and possibly other industries, saving time and potentially improving sales
Abstract
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.