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A Study On the Impact of Artificial Intelligence On Personalized Marketing Strategies and Consumer Trust
Subject area: Management and Commerce · Area of research: Marketing
Abstract
Artificial Intelligence has transformed modern marketing through hyper-personalized communication, real-time customer profiling, and predictive consumer insights. In this study, the focus is on the effects of AI-driven personalized marketing strategies on consumer trust, with particular emphasis on understanding how recommendation systems enabled by AI, behavioural analytics, and automated customer engagement influence perception, trust formation, and purchase intention among consumers. A quantitative descriptive research design was adopted and data were collected from 385 respondents using a structured questionnaire. Reliability, correlation, and regression analyses were therefore carried out to test four hypotheses. Indeed, the results indicate that AI-powered personalization enhances the trust of consumers, given that transparency, perceived usefulness, and assurance of data privacy are sustained. In addition, AI personalization was strongly positively related to perceived relevance, satisfaction, and finally, to trust. Trust turned out to be a strong predictor of purchase intention. Demographic evidence showed age and digital literacy impact consumer trust levels, though there was no significant difference based on gender. The study concludes that while AI-driven personalization increases marketing effectiveness, trust remains a critical mediator and may be strengthened by ethical AI use, clear communication, and robust privacy frameworks. This research contributes to the following practical implications for marketers to balance personalization with transparency and responsible data handling.
Keywords
Artificial Intelligence AI, Consumer Behaviour, Consumer Trust, Data Privacy, Digital Personalization, Machine Learning, Personalized Marketing, Predictive Analytics, Purchase Intention, Recommendation Systems
References
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How to cite this paper
@article{1712499,
author = {Kondru Praneeth Joel, Jakkireddy Ravtiteja Kiran Reddy, Dr. Archana Nag},
title = {A Study On the Impact of Artificial Intelligence On Personalized Marketing Strategies and Consumer Trust},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {135-139},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712499.pdf},
abstract = {Artificial Intelligence has transformed modern marketing through hyper-personalized communication, real-time customer profiling, and predictive consumer insights. In this study, the focus is on the effects of AI-driven personalized marketing strategies on consumer trust, with particular emphasis on understanding how recommendation systems enabled by AI, behavioural analytics, and automated customer engagement influence perception, trust formation, and purchase intention among consumers. A quantitative descriptive research design was adopted and data were collected from 385 respondents using a structured questionnaire. Reliability, correlation, and regression analyses were therefore carried out to test four hypotheses. Indeed, the results indicate that AI-powered personalization enhances the trust of consumers, given that transparency, perceived usefulness, and assurance of data privacy are sustained. In addition, AI personalization was strongly positively related to perceived relevance, satisfaction, and finally, to trust. Trust turned out to be a strong predictor of purchase intention. Demographic evidence showed age and digital literacy impact consumer trust levels, though there was no significant difference based on gender. The study concludes that while AI-driven personalization increases marketing effectiveness, trust remains a critical mediator and may be strengthened by ethical AI use, clear communication, and robust privacy frameworks. This research contributes to the following practical implications for marketers to balance personalization with transparency and responsible data handling.},
keywords = {Artificial Intelligence AI, Consumer Behaviour, Consumer Trust, Data Privacy, Digital Personalization, Machine Learning, Personalized Marketing, Predictive Analytics, Purchase Intention, Recommendation Systems},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712499}
}