Customer Personalization Using Data Science in E-Commerce: Integrating Foundational and Emerging Research
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I2P106Keywords:
Customer personalization, E commerce personalization, Personalized recommendations, Recommender systems, Personalized search, Personalization enginesAbstract
Personalization has become a critical differentiator in e-commerce, especially in the wake of the COVID-19 pandemic, as businesses leverage data science to deliver tailored customer experiences that drive loyalty, engagement, and revenue [1], [2], [3]. This paper provides a comprehensive review of the evolving landscape of personalization, examining the interplay between segmentation, recommendation systems, real-time analytics, and the growing importance of privacy and ethical considerations [1], [4], [5], [13]. We synthesize both foundational and recent research on system architecture, NoSQL databases, analytics pipelines, and public health data science, highlighting how these technologies enable scalable, adaptive, and secure personalization at scale [6], [9], [11]. The review also discusses the measurable business impacts of personalization, including increased conversion rates and customer retention, and explores challenges such as data governance, regulatory compliance, and the integration of AI and NLP for next-generation customer insights [1]-[19]. By referencing a broad set of academic and industry sources, this study offers actionable guidance for practitioners and researchers seeking to implement effective, responsible personalization in digital commerce
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