Skip to content

Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement

Aug 2026 · 0 citations · 10 references
Computer Science

TL;DR

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.

View source

Similar papers

Open access Aug 2026

From Trial to Commercial Value: A Customer-Value Architecture for AI-Enabled Retail and Service Technologies

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. · 0 citations
Jul 2026

AI-Driven Personalization in Financial Product Marketing: Enhancing Customer Engagement and Conversion Efficiency

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...

K. G. Hemalatha, A. R · 0 citations
Review Open access Jul 2026

Transforming marketing strategies via AI-powered personalization: A systematic review of consumer experience and business strategy

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 · 0 citations
Review Open access Aug 2026

Artificial Intelligence Personalization and Customer Engagement: A Systematic Review

A systematic review of literatures from year 2022 to 2026, synthesizing evidence from 84 peer reviewed studies on AI personalization and customer engagement in the e-commerce environment found out the dominant research themes, patterns, methodological approaches, and key determinants influencing engagement outcomes.

J. M. Edike · 0 citations
Review Open access Aug 2026

Retrieval-augmented generation in marketing: an integrative literature review of architectures, applications and strategic implications

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...

Mayemba K. Nzita, Marius Wait · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.