用AI代理打通搜索与客服，主动召回潜在买家。
Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement
- 构建AI产品研究代理，融合行为数据与外部知识
- 23天内推送1.5万条消息，点击率显著提升
- 适合电商做用户召回与全链路体验优化
现代电商平台常将搜索、推荐、个性化和客户关系管理（CRM）系统独立运行，限制了主动用户召回的机会。尤其在用户探索性意图（如寻找最佳智能手机或最新5G手机）时，可能离开平台去外部调研后再购买。本文提出一个可扩展的生产级框架，通过AI驱动的产品研究代理连接搜索与CRM流程。该系统识别具有探索性购买意图但低活跃度的用户，利用行为信号、外部知识和企业商品目录数据，开展基于事实的多智能体产品调研，并通过WhatsApp推送个性化推荐。在为期23天的生产部署中，共发送约1.5万条微信消息用于手机产品发现。相比传统微信推荐活动，本方案显著提升了点击率，且出现二次传播与分享行为。部署还带动了后续购买和营收增长，验证了AI产品研究代理在主动用户召回与端到端客户旅程优化中的实际效果。
原文摘要 · Abstract (English)
Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement. This is particularly challenging for exploratory intents such as best smartphones or latest 5G phones, where users may leave the platform for external research before purchasing. We present a scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents. The system identifies users with exploratory purchase intent and low engagement, conducts grounded multi-agent product research using behavioral signals, external knowledge, and enterprise catalog data, and delivers personalized recommendations through WhatsApp. We evaluate the framework in a 23-day production deployment involving approximately 15K WhatsApp notifications for mobile product discovery. The campaign achieved substantial CTR improvements over traditional WhatsApp recommendation campaigns, with evidence of secondary engagement through message forwarding and sharing. The deployment also generated downstream purchases and GMV impact, demonstrating the practical effectiveness of AI Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。