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

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.

Figures (9)

• Figure 1: System architecture
• Figure 2: The Profile Builder System architecture
• Figure 3: Prompt for the Profile Builder (PB)
• Figure 4: System architecture of the Style Rephraser (SR)
• Figure 5: Prompt for Style Rephraser (SR)