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Paper ID:IJSREM67058

An AI-Driven Framework For Predictive Customer Experience And Personalized Digital Marketing: An Empirical Study with Special Reference to Coimbatore District, Tamil Nadu

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67058

Volume:

10 |

9 |

Sep 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67058

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.

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Authors

D

Dr.P.Radhakrishnan

Management & Business Studies

Sree Narayana Guru College of Arts and Science, Coimbatore

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Abstract

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

Artificial Intelligence Predictive Customer Experience Personalized Digital Marketing Customer Engagement Trust Privacy Concern Predictive Analytics Coimbatore District.

Cite This Article

Dr.P.Radhakrishnan, "An AI-Driven Framework For Predictive Customer Experience And Personalized Digital Marketing: An Empirical Study with Special Reference to Coimbatore District, Tamil Nadu," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-6, Sep 2026. https://doi.org/10.55041/IJSREM67058

Full Article/Conclusion

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.

References

·                 Hardcastle, K., Vorster, L., & Brown, D. (2025). Understanding customer responses to AI-driven personalized journeys: Impacts on the customer experience. Journal of Advertising Research, 65(2), 176–195. https://doi.org/10.1080/00913367.2025.2460985 ·                 Teepapal, T. (2025). AI-driven personalization: Unraveling consumer perceptions in social media   engagement.   Computers   in   Human   Behavior,   165,   108549. ·                 https://doi.org/10.1016/j.chb.2024.108549 ·                 Owusu-Mensah, D., Adu Sarfo, P., & Appiah Kusi, G. (2025). Exploring the impact of artificial intelligence on customer experience personalization and marketing strategy optimization in digital marketing: An empirical analysis. Journal of Management and Informatics, 4(2). https://doi.org/10.51903/jmi.v4i2.242 ·                 Wang, A., et al. (2026). Balancing personalization, privacy, and value: A systematic literature review  of  AI-enabled  customer  experience  management.  Information,  17(2),  115. ·                 https://doi.org/10.3390/info17020115 ·                 Abhinav V. Hari, & Suresh, V. (2026). The impact of digital marketing on consumer buying behavior: An empirical study in Coimbatore. International Journal of Science, Engineering and Technology. ·                 Suganya, K. (2025). A study on the influence of digital marketing on consumer behaviour in rural and urban area (with special reference to Coimbatore district). International Journal of Commerce and Management Research, 11(4), 48–50. ·                 Sabarinathan, T., & Sivagnana Selvakumar, T. (2025). Shaping consumer behaviour and preference: A study of digital advertising among consumers in Coimbatore, Tamil Nadu. EPRA International Journal of Economics, Business and Management Studies, 12(5). ·                 Javi Prabha, R., Srikesh, V., Mohamed Asik, M., Sukilesh, N. M., & Praveen, G. (2026). AI in digital marketing: Enhancing customer engagement and brand experience, a descriptive empirical study among digital marketing professionals. International Journal of Economic Practices and Theories.

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Publication Date

9/2/2026

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