Beyond Demographics: How Artificial Intelligence redefines customer segmentation in digital marketing

This article examines the transformative impact of artificial intelligence on customer segmentation strategies in contemporary marketing practices. By leveraging advanced machine learning algorithms, businesses can now transcend traditional demographic segmentation to identify nuanced behavioral patterns, preference structures, and predictive purchase indicators. The article synthesizes empirical evidence from multiple industry sectors to demonstrate how AI-driven segmentation enables the development of hyper-targeted campaigns with significantly enhanced engagement metrics. Through analysis of technological frameworks, implementation challenges, and case studies, this article provides a comprehensive understanding of how predictive analytics can optimize customer lifecycle management, reduce churn, and identify cross-selling opportunities. The article indicates that organizations implementing AI-powered segmentation strategies achieve more personalized customer experiences while simultaneously improving operational efficiency. This article contributes to marketing literature by proposing an integrated framework for AI adoption in segmentation practices while addressing critical considerations in data governance, privacy, and ethical implementation.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17222147
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
0.00
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article

Beyond Demographics: How Artificial Intelligence redefines customer segmentation in digital marketing

Zenodo (CERN European Organization for Nuclear Research)
Global Maternal and Child Health
article

Beyond Demographics: How Artificial Intelligence redefines customer segmentation in digital marketing

article en

Abstract

This article examines the transformative impact of artificial intelligence on customer segmentation strategies in contemporary marketing practices. By leveraging advanced machine learning algorithms, businesses can now transcend traditional demographic segmentation to identify nuanced behavioral patterns, preference structures, and predictive purchase indicators. The article synthesizes empirical evidence from multiple industry sectors to demonstrate how AI-driven segmentation enables the development of hyper-targeted campaigns with significantly enhanced engagement metrics. Through analysis of technological frameworks, implementation challenges, and case studies, this article provides a comprehensive understanding of how predictive analytics can optimize customer lifecycle management, reduce churn, and identify cross-selling opportunities. The article indicates that organizations implementing AI-powered segmentation strategies achieve more personalized customer experiences while simultaneously improving operational efficiency. This article contributes to marketing literature by proposing an integrated framework for AI adoption in segmentation practices while addressing critical considerations in data governance, privacy, and ethical implementation.

Zenodo (CERN European Organization for Nuclear Research)
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Global Maternal and Child Health
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