Enhancing Customer Segmentation and Behaviour Analysis with RFM Clustering: A Machine Learning Approach

Rajendran, Nandheeswari (2023) Enhancing Customer Segmentation and Behaviour Analysis with RFM Clustering: A Machine Learning Approach. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (911kB) | Preview

Abstract

This research project examines to tackle the challenge of customer segmentation and clustering through the utilization of Recency, Frequency, and Monetary (RFM) analysis with an extensive transactional dataset. RFM analysis is a potent marketing approach that categorizes, and groups customers based on recency, frequency, and monetary metrics. Additionally, time series analysis is conducted on both a monthly and daily basis to gain insights into customer interactions at various times of the day. In the clustering phase several distinct algorithms such as K-Means, Agglomerative, and Meanshift are employed, using standardized RFM scores as input features. The research evaluates performance using the metrics Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. The research methodology encompasses data collection, preprocessing, feature engineering, detailed exploratory data analysis to extract meaningful customer attributes. These findings highlight valuable customer segments that can be targeted for specific marketing strategies. The comprehensive analysis of this study reveals that the Agglomerative clustering model consistently outperforms both KMeans and Mean-shift models, showcasing its superiority in effectively grouping customers based on their transactional behaviours, thus highlighting its importance in the online retail industry's optimisation of customer segmentation.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Muntean, Cristina Hava UNSPECIFIED
Uncontrolled Keywords:	Clustering; Monetary; Frequency; Recency; K-Means; Agglomerative; Mean-shift; Customer Segmentation; RFM Analysis; Distribution
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	21 May 2025 10:14
Last Modified:	21 May 2025 10:14
URI:	https://norma.ncirl.ie/id/eprint/7598

Actions (login required)

View Item