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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67058
Volume:
10 |
9 |
Sep 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Management & Business Studies
Sree Narayana Guru College of Arts and Science, Coimbatore
This study develops and empirically examines an AI-driven framework for predictive customer experience (PCX) and personalized digital marketing (PDM), with special reference to consumers in Coimbatore District, Tamil Nadu. The framework integrates AI-enabled predictive analytics, recommendation systems, conversational AI, customer-data integration, personalization quality, perceived usefulness, trust, privacy concern, customer engagement, satisfaction, and purchase intention. A quantitative, cross-sectional research design is proposed using a structured questionnaire administered to digitally active consumers in Coimbatore District. The study recommends a sample of 400 respondents and the use of descriptive statistics, reliability analysis, exploratory factor analysis, correlation, multiple regression, and structural equation modelling (SEM) to test the proposed relationships. The framework assumes that AI capability improves personalization quality and predictive relevance, which in turn enhance customer experience and engagement; however, privacy concerns are expected to weaken the positive effect when customers perceive excessive surveillance or insufficient control over personal data. The article contributes a locally contextualized model for businesses, marketers, and researchers seeking to implement responsible AI-driven marketing. Because this manuscript is designed as a research-ready article, numerical findings are not fabricated; the Results section provides a statistical reporting template to be completed after primary-data collection.
KEYWORDS
AI-driven digital marketing offers organizations an opportunity to move from broad targeting toward predictive, contextual, and individualized customer experiences. This article proposes an integrated empirical framework in which AI capability improves predictive relevance and personalization quality, which then influence customer experience, engagement, satisfaction, and purchase intention. Trust is positioned as an enabling mechanism, while privacy concern is treated as a potential constraint and moderator. For businesses in Coimbatore District, the strategic priority should therefore be responsible personalization: the goal is not simply to make marketing more targeted, but to make customer interactions more relevant, useful, transparent, and trustworthy. The proposed research design provides a practical basis for collecting and analysing primary data without overstating findings that have not yet been empirically observed.
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Publication Date
9/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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