Rise of Ai-powered personalization: How brands are redefining customer experience in 2025
DOI:
https://doi.org/10.65453/ajbmr.v14i3.1321Keywords:
AI-powered personalization, Customer experience, Digital marketing, Machine learningAbstract
In 2025, AI-powered personalization has emerged as a transformative force reshaping how brands engage with customers in an increasingly digital marketplace. Leveraging advanced technologies such as machine learning, natural language processing, and predictive analytics, brands now deliver hyper-personalized, real-time interactions that transcend traditional segmentation methods. By analyzing vast datasets encompassing browsing behaviour, purchase history, and contextual factors, AI enables precise prediction of individual customer needs, enhancing both marketing effectiveness and customer satisfaction. This paper traces the evolution of personalization from broad demographic targeting to dynamic, AI-driven strategies, highlighting heightened consumer expectations for seamless and relevant experiences across multiple channels. Leading brands such as Amazon, Netflix, and Sephora exemplify the commercial benefits of AI personalization, reporting significant gains in engagement, conversion rates, and customer loyalty. However, the adoption of AI personalization also raises ethical challenges, particularly concerning data privacy, algorithmic bias, and the potential erosion of human connection. Addressing these concerns requires a balanced approach that integrates robust data protection, transparency, and ethical training for marketing teams. The study further explores emerging trends, including generative AI and immersive technologies, which promise to deepen customer engagement while empowering consumers with greater control over their data. Ultimately, this paper recommends strategic investments aligned with business goals, incremental implementation, continuous optimization, and a commitment to ethical practices. By embracing these principles, brands can harness AI personalization to redefine customer experiences sustainably, driving growth and fostering trust in the competitive digital economy of 2025.
References
Arora, A., de Bellefonds, C., & Samandari, H. (2021). The value of getting personalisation right—or wrong—is multiplying. McKinsey & Company. Retrieved from https://www.mckinsey.com
Boston Consulting Group. (2019). The $800 billion personalisation opportunity. Boston Consulting Group. Retrieved from https://www.bcg.com
Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations. The Bottom Line, 33(4), 359-375.
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2023). AI in marketing: An empirical study of email campaigns. Journal of Business Research, 159, 113504.
Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.
Hassan, M. U., & Mazhar, S. (2024). AI-based personalized marketing content and consumer engagement. International Journal of Marketing Science, 21(1), 55–72.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49, 30-50.
Kamakura, W. A., Ramón-Jerónimo, M. A., & Vecino Gravel, J. D. (2012). A dynamic perspective to the internationalization of small-medium enterprises. Journal of the Academy of Marketing Science, 40, 236-251.
Kaptein, M., & Parvinen, P. (2015). Advancing e-commerce personalization: Process framework and case study. International Journal of Electronic Commerce, 19(3), 7-33.
Kietzmann, J., Paschen, J., & Treen, E. (2023). Artificial intelligence in customer experience management: A conceptual framework. Business Horizons, 66(2), 201–210.
Kumar, V., Dixit, A., Javalgi, R. G., & Dass, M. (2016). Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing. Journal of the Academy of Marketing Science, 44, 24-45.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of marketing, 80(6), 69-96.
Liu, X., & Singh, S. (2025). AI-driven dynamic pricing models for online retailers: Real-time implementation and customer impact. Journal of Retailing and Consumer Services, 74, 103558.
Maheshwari, R. (2025). Automation in AI-driven content creation: Opportunities and challenges. Digital Marketing Review, 9(1), 33–48.
Martin, K., & Murphy, P. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155.
McKinsey & Company. (2023). How personalization drives Amazon’s revenue growth. McKinsey Digital. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-personalization-drives-amazons-revenue-growth
Moons, K. G., Damen, J. A., Kaul, T., Hooft, L., Navarro, C. A., Dhiman, P., ... & van Smeden, M. (2025). PROBAST+ AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. bmj, 388.
Muminov, D. (2025). The power of AI in personalized marketing campaigns: Enhancing user experience through data analytics. Journal of Data Analytics, 18(2), 144–158.
Nadarajah, D., & Cham, T. H. (2023). Big data and AI integration in customer targeting strategies. Journal of Strategic Marketing, 31(5), 489–506.
Nguyen, B., Simkin, L., & Canhoto, A. (2020). The dark side of CRM: Advantaged and disadvantaged customers. Journal of Business Research, 116, 209–222. https://doi.org/10.1016/j.jbusres.2019.09.013
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
Paschen, J., Pitt, L., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovative typology. Business Horizons, 63(2), 147–155.
Peppers, D., & Rogers, M. (2016). Managing customer experience and relationships: A strategic framework. John Wiley & Sons.
Pitt, C., Paschen, J., Kietzmann, J., Pitt, L. F., & Pala, E. (2023). Artificial intelligence, marketing, and the history of technology: Kranzberg’s laws as a conceptual lens. Australasian Marketing Journal, 31(1), 81-89.
Sivakolundhu, D., & Yagamurthy, S. (2024). Sentiment analysis in AI chatbots for customer service: Enhancing emotional responsiveness. International Journal of Communication Engineering, 9(4), 115–126.
Smith, B., & Linden, G. (2017). Two decades of recommender systems at Amazon.com. IEEE Internet Computing, 21(3), 12–18. https://doi.org/10.1109/MIC.2017.72
Smith, L., & Johnson, M. (2024). AI and customer engagement in the beauty industry: Sephora’s digital transformation. Journal of Retail Innovation, 12(1), 45–58.
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
Wan, T., Wang, A., Ai, B., Wen, B., Mao, C., Xie, C. W., ... & Liu, Z. (2025). Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314.
Wewelwala, P., & Sumanathilaka, H. (2025). Emotion recognition in AI-enabled customer interaction systems using multimodal analysis. Proceedings of the International Conference on AI and Human Interaction (ICAIHI). https://arxiv.org/abs/2503.21927
Zhang, T., & Wan, F. (2024). Personalization in e-commerce using deep learning: A customer-centric approach. Journal of Computer-Mediated Communication, 29(1), 67–85.



