An Analytical Study of Consumer Perception of AI-Driven Personalisation in Digital Marketing

Abstract

The emergence of Artificial Intelligence (AI) has greatly changed the manner in which companies comprehend and engage with consumers. AI-based personalization helps businesses to analyze consumer data, forecast preferences and deliver personalized offers, products, services, recommendations, promotions and many more. This paper seeks to investigate consumer perception of AI based personalization and examines socio-economic factors that affect consumers\' perception and evaluation of personalized offerings. 120 sample online consumers from Ernakulam District of Kerala were chosen by applying convenience sampling. Primary data was gathered through a structured survey among consumers and appropriate statistical tool i.e. multiple regression analysis has been used for analysis of the gathered data. The findings help to gain an understanding of consumers\' perceptions and response to AI personalization being used increasingly in marketing activities. In marketing, AI-driven personalisation supports a variety of activities, including customised email communication, targeted digital advertising, product recommendations, and personalised promotional content. By analysing consumer characteristics and behavioural information, marketers can develop communications that are more closely aligned with specific consumer interests and preferences. Consequently, AI-driven personalisation has become an important component of contemporary digital marketing, while consumer trust, privacy, transparency, and perceived relevance remain critical factors in determining how consumers respond to personalised interactions.

Introduction

The text examines consumer perceptions of AI-driven personalization in digital marketing. AI personalization uses consumer data, preferences, and behavioral patterns to provide customized recommendations, advertisements, emails, products, and digital experiences. Its growing use has enabled hyperpersonalization, where customers receive consistent and personalized interactions across different digital channels.

AI-driven personalization can improve customer experience, engagement, satisfaction, and business performance. In e-commerce, for example, AI can analyze browsing history, searches, purchases, and interests to recommend products that are more relevant to individual consumers. However, personalization also creates concerns about privacy, transparency, consumer control, trust, and potential price discrimination. Consumers may not understand how AI makes recommendations or how their personal data are collected, stored, analyzed, and shared.

Significance and research problem

The study highlights the importance of AI personalization throughout the customer journey, from product searches to post-purchase communication. Although personalization can contribute to stronger customer relationships and business growth, companies need to use consumer data responsibly. Lack of transparency or excessive data collection may reduce consumer trust.

The study therefore focuses on understanding how consumers perceive AI-driven personalization in digital marketing, particularly in relation to its benefits and concerns.

Literature review

Previous research shows a personalization–privacy paradox: consumers appreciate relevant and convenient personalized experiences but may become concerned about privacy, surveillance, loss of control, and excessive data collection. Trust, perceived usefulness, perceived value, engagement, and consumer control are important factors affecting acceptance of AI personalization.

Methodology

The study used a quantitative research approach involving:

• 120 online consumers from Ernakulam District, Kerala.

• Respondents were selected using convenience sampling.

• Data were collected through an online questionnaire.

• The responses were statistically analyzed using multiple regression analysis.

• Ten independent variables were examined, including age, gender, income, occupation, education, shopping frequency, and experience with different forms of personalization.

Key findings

The regression model produced an R value of 0.528, indicating a moderate relationship between the independent variables and consumer perception of AI personalization.

The R² value of 0.279 indicates that approximately 27.9% of the variation in consumer perception is explained by the ten variables included in the model. The adjusted R² was 0.212, meaning that about 21.2% of the variation remains explained after accounting for the number of predictors.

Among the variables examined:

• Age had a statistically significant association with consumer perception.

• Gender had a statistically significant association.

• Income level had a statistically significant association.

• Occupation, forms of personalization experienced, family size, shopping platform, education, shopping frequency, and type of product did not show statistically significant individual effects at the 5% significance level.

Conclusion

Personalization using AI has emerged as a crucial aspect of online consumer interaction as it allows organizations to give personalized offers, recommendations, content, and shopping experience to customers. The current study analyzed the consumer perception of AI personalization in terms of online consumers from Ernakulam district in Kerala state. The results from multiple regression analysis reveal that the independent variables taken into consideration have a significant relationship with consumer perception, which explains 27.9% variation in perception, and adjusted explanatory ability of 21.2%. The age, gender, and income level are found to be significant at the 5% level, while occupation, form of personalization used, family size, type of online shopping website, education level, frequency of online shopping, and product type are not statistically significant at 5% level. It is further observed that the residual histogram shows that the regression residuals are normally distributed. Therefore, the study indicated that consumer perception of AI-driven personalisation is associated with selected consumer characteristics, while the effectiveness and acceptance of personalisation also depend on how consumers perceive its relevance, transparency, privacy and fairness. So, organisations should adopt a consumer-centric and responsible approach to AI-driven personalisation, balancing customised experiences with privacy protection, transparency, consumer control and fair practices. This approach can contribute to more meaningful and trustworthy interactions between online consumers and AI-enabled platforms. Awareness programmes and clear communication can help consumers understand the benefits, limitations and privacy implications of AI-driven personalisation. Consumers should be given easy-to-use options to manage personalisation preferences, modify data-sharing settings and opt out of personalised recommendations where appropriate.

References

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Copyright

Copyright © 2026 Dr. V. K. Veerakumar, Ann Rose Angels T. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.