发表机构
Flipkart Internet Pvt Ltd（Flipkart互联网私人有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出打通搜索与CRM的AI产品研究智能体框架，在23天生产部署中验证其可提升WhatsApp推荐的CTR、带来GMV增长，实现客户再互动与旅程优化。
AI 中文摘要
现代电商平台通常独立运营搜索、推荐、个性化及CRM系统，限制了主动客户再互动的机会。对于“最佳智能手机”或“最新5G手机”等探索性需求，用户可能在购买前离开平台进行外部调研，这一问题尤为突出。我们提出了一种可扩展的生产部署框架，通过AI驱动的产品研究智能体打通搜索与CRM工作流。该系统识别具有探索性购买意图且互动度低的用户，利用行为信号、外部知识及企业目录数据开展基于事实的多智能体产品研究，并通过WhatsApp推送个性化推荐。我们在为期23天的生产部署中对该框架进行评估，共生成约1.5万条用于移动产品发现的WhatsApp通知。该活动相较于传统WhatsApp推荐活动实现了显著的点击率（CTR）提升，且有证据表明存在通过消息转发和分享产生的二次互动。此次部署还带来了下游购买行为及商品交易总额（GMV）的增长，证明了AI产品研究智能体在主动客户再互动及端到端客户旅程优化方面的实际有效性。
英文摘要
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.
