Cluster-Based Customer Segmentation for Targeted Marketing in Retail Using Unsupervised Learning Algorithms

Vakacharla, Leela Sri Venkata Sai Narayana (2025) Cluster-Based Customer Segmentation for Targeted Marketing in Retail Using Unsupervised Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

The research uses unsupervised machine learning to categorise retail customers with Recency, Frequency, and Monetary (RFM) data. A Kaggle marketing dataset was cleaned, transformed and modelled with K-Means, DBSCAN, Hierarchical clustering and Gaussian Mixture Model. The models were assessed using a multi-metric model such as the Silhouette Score, Davies-Bouldin Index and Calinski-Harabasz Index. The results indicate that K-Means has the most differentiated and consistent clusters that are entrenched with the highest scores of separations, and DBSCAN is not successful because of the density constraints. The study is valuable as it bridges the gaps in the single-metric assessment and proves the power of deep segmentation as the source of targeted marketing and customer relationship options.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HF Commerce > Marketing H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Retail Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	09 Sep 2026 10:38
Last Modified:	09 Sep 2026 10:38
URI:	https://norma.ncirl.ie/id/eprint/9923

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