Work

LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion

Year: 2025

Type: conference-paper

Abstract: When the competition in e-commerce rises, it is essential to ensure content is creative as well as effective in driving conversions. Using language generation from LLMs, we suggest a process that includes prompt engineering, multi-objective training and post-processing to make marketing texts engaging and effective for sales. We use a two-step process that applies sentiment modification, boosts diversity and adds calls to action. The way we test offline and online helps achieve a 12.5% increase in click through rate (CTR) and 8.3% increase in conversion rate (CVR). With this solution, creating texts can be automated and it will guide future improvements in live, customized user experiences.

Source: International Conference on Computer and Multimedia Technology (ICCMT)

DOI: 10.1145/3757749.3757850

Authors: Haowei Yang, Haotian Lyu, Tianle Zhang, Dingzhou Wang, Yushang Zhao

Institutions: University of Houston, University of Southern California, Duke University, Washington University in St. Louis

Language: English

Topic: Digital Marketing and Social Media

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4414182452

FWCI: 30.89

Cites: 12

Cited by: 9

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