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 the first trial. This conceptual paper integrates seminal work on technology acceptance, innovation diffusion, expectation-confirmation, perceived value, habit, customer satisfaction and willingness to pay with recent retail and service research published from 2023 to 2025. The synthesis develops a five-stage customer-value architecture covering adoption readiness, assisted trial, value confirmation, routine formation and commercial conversion. Recent evidence strengthens four parts of the architecture. Familiarity can shape acceptance and disclosure in AI-mediated product search; automation, personalisation, efficiency and precision can improve engagement with service robots; empathy can support social presence and satisfaction but may weaken the experience under time pressure; and customer responses differ according to the level and location of in-store automation. The framework also identifies feedback through service recovery, social proof and cumulative satisfaction. Its main contribution is the separation of technology acceptance, usage value and payment value. A customer may accept and repeatedly use an AI-enabled service without choosing a paid option when familiar alternatives remain available or the value difference is unclear. The paper provides a source-based structure for commerce and service managers to assess AI-enabled customer journeys without treating intention as a commercial outcome.
A. G., S. H, Mahendra H. R. et al.· International Journal of Cre...· 0 citations
This chapter examines the transformative role of artificial intelligence (AI)-driven personalization in reshaping financial product marketing, with a specific focus on enhancing customer engagement and improving conversion efficiency. The financial services industry is undergoing a profound digital evolution, where data-driven strategies, algorithmic decision-making, and real-time behavioral analytics are redefining how financial institutions interact with their customers. Traditional marketing approaches in banking, insurance, and investment services often rely on broad demographic segmentation, which fails to capture the nuanced financial needs, risk profiles, and behavioral patterns of individual customers. AI-driven personalization presents a powerful solution by enabling financial marketers to deliver hyper-relevant product recommendations, dynamically tailored communications, and predictive engagement strategies at scale.
This chapter proposes a conceptual model that integrates AI personalization technologies, behavioral finance principles, customer journey mapping, and ethical data governance frameworks. The model explains how machine learning algorithms, natural language processing (NLP), and predictive analytics collaborate to enable financial institutions to identify customer intent, predict product preferences, and optimize marketing touchpoints across digital channels. Case studies drawn from retail banking, insurance, investment platforms, and fintech applications illustrate the practical deployment of AI personalization tools. The chapter also examines significant challenges including regulatory compliance, algorithmic bias, data privacy, customer trust, and the tension between personalization depth and ethical boundaries. Future directions address the convergence of generative AI, open banking ecosystems, explainable AI frameworks, and responsible personalization as the next frontier in financial marketing. The overarching contribution of this chapter is to demonstrate that AI-driven personalization is not merely a technological advancement but a strategic imperative for financial institutions seeking competitive differentiation, deeper customer relationships, and sustained conversion growth.
Keywords: AI-Driven Personalization, Financial Product Marketing, Customer Engagement, Conversion Efficiency, Machine Learning, Behavioral Finance, Predictive Analytics, Fintech, Digital Marketing, Algorithmic Recommendation
K. G. Hemalatha Dr. K. G. Hemalatha, A. R· International Scientific Jou...· 0 citations
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 literature. This systematic review synthesizes current research on AI-driven marketing personalization, followed by examining the underlying technologies, their impact on consumer and business outcomes, associated ethical concerns, and existing research gaps. In line with the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 (Page et al., 2021), we conducted a systematic search that included studies that were published between 2015 and 2025. The most popular AI tools used for personalization were recommendation systems, machine learning (ML) and natural language processing (NLP). It has been found that AI-powered personalization helps to boost customer satisfaction, engagement, and brand loyalty. The proposed AI-personalization-ethics model (AIPEM) is an abstraction of these results and a comprehensive model for understanding AI personalization in marketing. Future studies are needed in the direction of responsible AI design, validation of the AIPEM model in practice, and long-term evaluations of its impact on consumers.
Yasmeen G. Elsantil· Corporate & Business Strateg...· 0 citations
The rapid growth of online retail has created enormous volumes of unstructured product data that most businesses struggle to convert into actionable intelligence. Customer reviews, product descriptions, pricing information, and rating histories collectively represent a rich source of market knowledge, yet extracting meaningful insights from this data at scale remains a persistent challenge for organizations operating in competitive e-commerce environments. This thesis presents ProductIQ, an intelligent analytics platform that addresses this gap by enabling users to submit natural language business queries and receive structured, evidencegrounded analytical reports derived from a corpus of 200,000 Amazon Electronics product records. The system retrieves semantically relevant products using BGE-M3 embeddings and ChromaDB vector search, then conditions Claude Opus 4.6 to synthesize the retrieved evidence into coherent analytical narratives covering key market findings, product comparisons, and strategic recommendations. By grounding every response in retrieved product data, the system reduces the risk of hallucination while producing outputs that reflect the actual state of the product catalog. Beyond the core retrieval and generation components, the platform incorporates a production-grade MLOps pipeline comprising MLflow experiment tracking, Docker containerization, and GitHub Actions for continuous integration and delivery. Evaluation across eight quantitative metrics confirms the quality of system outputs, with a BERTScore F1 of 0.9131 indicating strong semantic alignment with human-authored reference insights and a faithfulness score of 0.5567 reflecting meaningful grounding in retrieved evidence. All sixteen automated unit tests pass, confirming system reliability. This research demonstrates how retrieval-augmented generation can be applied to transform large-scale e-commerce product data into accessible, reliable business intelligence.
The emergence of artificial intelligence (AI) has become a vocal strategy in digital marketing and
has no doubt been receiving huge attention and adoption. Firms now channel individualized
services and marketing communication to its customers using machine learning algorithms,
predictive analytics, and large-scale consumer data from purchase or engagement history to
provide highly personalized offerings. Despite this, the relationship between AI personalization
and customer engagement is inconsistent across different digital environment. Thus, the need to
evaluate existing literatures. This study addresses this gap by conducting 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. It found out the
dominant research themes, patterns, methodological approaches, and key determinants
influencing engagement outcomes. The result established that engagement comes from perceived
usefulness and relevance of a firms’ offering, however, concerns regarding privacy, algorithmic
transparency and intrusiveness remains key challenges affecting consumers which may undermine
trust and limits the effectiveness of personalization strategies. The use of AI personalization
reduces consumers’ information overload and improves its overall digital experiences. From the
managerial perspective, the adoption of ethical frameworks and customer-centric personalization
will enable firms to enjoy high engagement leading to positive responses and the policy aspect
employs government to step in and tackle the growing concern of privacy and intrusiveness from
the use of personalization by establishing clear regulatory frameworks and data protection laws
for its citizens because the use of AI personalization is shaping the business environment.
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 integrative literature review guided by the SPIDER framework was conducted across Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar and Emerald Insight for publications from 2020 to 2025. A final corpus of 25 marketing-relevant RAG studies was analysed through qualitative coding and thematic synthesis. Adjacent peer-reviewed literature on AI in marketing, recommender systems, customer analytics, data-driven marketing capability, AI-augmented co-creation and immersive digital environments was used to contextualise, rather than expand, the primary corpus.
Five themes emerge: adaptive and knowledge-graph-enhanced RAG architectures; multilingual, real-time and hyper-personalised customer interaction; intelligent and context-aware retrieval; marketing intelligence and decision support; and robustness, hallucination mitigation and responsible deployment. The review shows that RAG can support retrieval-grounded interaction and market sensing, but evidence is stronger for technical and prototype demonstrations than for longitudinal customer, conversion or strategic outcomes. Responsible deployment requires transparency, privacy safeguards, fairness controls, human oversight and auditable knowledge sources.
The study advances a marketing-specific interpretation of RAG as a socio-technical marketing capability. It contributes by shifting personalisation from predictive targeting to retrieval-grounded interaction, linking market sensing to dynamic knowledge grounding and positioning governance as intrinsic to the capability rather than a downstream safeguard.
Mayemba K. Nzita, Marius Wait· Journal of Research in Inter...· 0 citations
