Customer segmentation on clustering algorithms

Toh, Wei Xuan (2023) Customer segmentation on clustering algorithms. Final Year Project, UTAR.

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Abstract

This report presents an analysis of customer segmentation using various clustering algorithms, including k-means, DBSCAN, GMM, and RFM. The aim of the study is to identify customer groups based on their buying behaviour and demographic characteristics. The study utilizes a dataset consisting of transactional and demographic data of customers from an e-commerce company. Firstly, descriptive analysis is performed to explore the characteristics of the dataset. Then, k-means, DBSCAN, and GMM clustering algorithms are applied to segment customers based on their buying behaviour. Finally, RFM (Recency, Frequency, Monetary) analysis is used to segment customers based on their purchasing history. The results show that all clustering algorithms were able to identify distinct customer groups with varying characteristics. Furthermore, the RFM analysis was able to segment customers based on their buying patterns, and provide insights into their behaviour. Overall, the study demonstrates the effectiveness of different clustering algorithms and RFM analysis in identifying customer segments. The insights gained from this study could potentially be used by the e-commerce company to improve their marketing strategies and customer engagement.

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