AI-Driven Personalization: Transforming Customer Engagement in the Digital Age

Artificial Intelligence (AI) is transforming marketing management through data driven decision making and personalized customer experiences. This paper examines AI driven personalization and its role in strengthening customer engagement. It discusses key technologies, including machine learning, natural language processing, predictive analytics, recommendation engines, and computer vision. The paper explores personalization techniques such as collaborative filtering, content based filtering, behavioral targeting, and dynamic pricing. Corporate applications by Amazon, Netflix, Starbucks, Sephora, and Spotify illustrate practical implementations. The discussion highlights benefits such as improved targeting accuracy, cost efficiency, real time decision making, customer satisfaction, and brand loyalty. It also examines AI adoption within India's diverse digital marketplace. However, data privacy, algorithmic bias, excessive personalization, and reduced human interaction present significant challenges. Transparent consent, responsible data practices, fairness audits, and human oversight remain essential. Emerging developments include generative AI, conversational systems, predictive personalisation, autonomous marketing agents, and privacy focused technologies. The paper emphasizes strategic integration and ethical governance. Ultimately, AI should complement human marketing judgment while enabling meaningful customer relationships, sustainable competitive advantage, and improved organizational performance.

Authors

Institutions

Publication Details

Published
2026-11-09
DOI
https://doi.org/10.33774/coe-2026-4rlmv
Primary Topic
AI in Service Interactions
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
preprint

AI-Driven Personalization: Transforming Customer Engagement in the Digital Age

Mohammad Shaquib
AI in Service Interactions
preprint

AI-Driven Personalization: Transforming Customer Engagement in the Digital Age

Mohammad Shaquib
preprint en

Abstract

Artificial Intelligence (AI) is transforming marketing management through data driven decision making and personalized customer experiences. This paper examines AI driven personalization and its role in strengthening customer engagement. It discusses key technologies, including machine learning, natural language processing, predictive analytics, recommendation engines, and computer vision. The paper explores personalization techniques such as collaborative filtering, content based filtering, behavioral targeting, and dynamic pricing. Corporate applications by Amazon, Netflix, Starbucks, Sephora, and Spotify illustrate practical implementations. The discussion highlights benefits such as improved targeting accuracy, cost efficiency, real time decision making, customer satisfaction, and brand loyalty. It also examines AI adoption within India's diverse digital marketplace. However, data privacy, algorithmic bias, excessive personalization, and reduced human interaction present significant challenges. Transparent consent, responsible data practices, fairness audits, and human oversight remain essential. Emerging developments include generative AI, conversational systems, predictive personalisation, autonomous marketing agents, and privacy focused technologies. The paper emphasizes strategic integration and ethical governance. Ultimately, AI should complement human marketing judgment while enabling meaningful customer relationships, sustainable competitive advantage, and improved organizational performance.

University of Lucknow (IN)
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon