Customer Analysis and Text Generation for Small Retail Stores Using LLM-Generated Marketing Presence

TL;DR

This paper presents a human-AI collaborative system that enhances point-of-purchase marketing materials for small retail stores using large language models, improving text quality and customer targeting.

Contribution

It introduces a prototype system that combines LLMs with human insight to improve POP text creation and evaluation for small retail stores.

Findings

Average evaluation score increased by 2.37 points with system support.

System aids in understanding target customers and refining marketing texts.

Collaborative approach improves the quality and persuasiveness of POP materials.

Abstract

Point of purchase (POP) materials can be created to assist non-experts by combining large language models (LLMs) with human insight. Persuasive POP texts require both customer understanding and expressive writing skills. However, LLM-generated texts often lack creative diversity, while human users may have limited experience in marketing and content creation. To address these complementary limitations, we propose a prototype system for small retail stores that enhances POP creation through human-AI collaboration. The system supports users in understanding target customers, generating draft POP texts, refining expressions, and evaluating candidates through simulated personas. Our experimental results show that this process significantly improves text quality: the average evaluation score increased by 2.37 points on a -3 to +3 scale compared to that created without system support.