CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation
Yinzhu Quan, Xinrui Li, Ying Chen

TL;DR
CRMAgent is a multi-agent LLM system that enhances e-commerce CRM message template generation by learning from top-performing messages, retrieving similar successful templates, and providing fallback rewriting, significantly improving message effectiveness.
Contribution
This paper introduces CRMAgent, a novel multi-agent system leveraging LLMs for scalable, high-quality CRM message template generation with learning, retrieval, and rule-based fallback modes.
Findings
CRMAgent outperforms original merchant templates in effectiveness.
The system achieves significant improvements in audience match.
It demonstrates robustness across various campaign scenarios.
Abstract
In e-commerce private-domain channels such as instant messaging and e-mail, merchants engage customers directly as part of their Customer Relationship Management (CRM) programmes to drive retention and conversion. While a few top performers excel at crafting outbound messages, most merchants struggle to write persuasive copy because they lack both expertise and scalable tools. We introduce CRMAgent, a multi-agent system built on large language models (LLMs) that generates high-quality message templates and actionable writing guidance through three complementary modes. First, group-based learning enables the agent to learn from a merchant's own top-performing messages within the same audience segment and rewrite low-performing ones. Second, retrieval-and-adaptation fetches templates that share the same audience segment and exhibit high similarity in voucher type and product category,…
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