AI - Powered Customer Segmentation and Targeting: Predicting Customer Behaviour for Strategic Impact

Shantanu Seth, Phani Chilakapati, Rahul Prathikantam, and Anilkumar Jangili, USA

Abstract

Customer targeting has become a critical component of modern marketing strategies, driven by advancements in Artificial Intelligence (AI). This paper presents a novel AI-powered customer segmentation framework that integrates K-Means clustering, Principal Component Analysis (PCA), and Random Forest classification to enhance predictive analytics for strategic marketing impact. The rationale for selecting these methods is thoroughly discussed, highlighting their strengths over alternatives like DBSCAN, LDA, and SVM. Additionally, baseline comparisons and experimental evaluations demonstrate the effectiveness of the proposed approach. Real-world e-commerce datasets are leveraged to illustrate the model’s ability to generate granular customer insights. Unlike prior studies that relied on standalone methods, this research evaluates the comparative advantages of these techniques over alternative clustering and classification approaches. The study also explores emerging trends such as real-time personalization and ethical challenges related to AI-driven targeting.

Keywords

Customer targeting, Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Clustering, Personalization, Recommendation Systems.

Original Source URL: https://aircconline.com/ijdkp/V15N1/15125ijdkp03.pdf

https://airccse.org/journal/ijdkp/vol15.html