A scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization is presented.
Abstract
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.
Retailers and service firms use scan-and-go systems, autonomous stores, conversational agents, voice assistants, service robots and AI-supported recommendations at different points in the customer journey. Research explains many drivers of technology acceptance, but commercial value also depends on what happens after t...
A. G., S. H, Mahendra H. R. et al.· International Journal of Cre...· 0 citations
This chapter proposes a conceptual model that integrates AI personalization technologies, behavioral finance principles, customer journey mapping, and ethical data governance frameworks to demonstrate that AI-driven personalization is not merely a technological advancement but a strategic imperative for financial insti...
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The integration of artificial intelligence (AI) in marketing has allowed for highly personalized customer experiences, transforming the traditional engagement strategies (Davenport et al., 2020). However, the implications of this for consumer behavior, ethical standards, and business performance are fragmented in the l...
Yasmeen G. Elsantil· Corporate & Business Strateg...· 0 citations
This research demonstrates how retrieval-augmented generation can be applied to transform large-scale e-commerce product data into accessible, reliable business intelligence.
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J. M. Edike· INTERNATIONAL JOURNAL OF MAR...· 0 citations
This study synthesises and critically interprets the emerging literature on retrieval-augmented generation (RAG) in marketing. It examines how RAG architectures, applications and governance conditions shape interactive marketing, customer engagement, market sensing and responsible AI deployment.
An integrati...
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