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This paper presents a scalable framework that integrates AI-powered Product Research Agents into e-commerce platforms, effectively bridging search and CRM systems to enhance customer re-engagement. By identifying users with exploratory purchase intent and low engagement, the system conducts grounded multi-agent product research and delivers personalized recommendations via WhatsApp. The 23-day production deployment demonstrated significant improvements in click-through rates (CTR) and downstream purchases, highlighting the framework's effectiveness in optimizing the customer journey.
AI Product Research Agents can significantly boost customer engagement and sales by delivering personalized recommendations directly through WhatsApp, transforming how e-commerce platforms interact with users.
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.
