  Recently, peoples awareness of online purchases has significantly risen. This
has given rise to online retail platforms and the need for a better
understanding of customer purchasing behaviour. Retail companies are pressed
with the need to deal with a high volume of customer purchases, which requires
sophisticated approaches to perform more accurate and efficient customer
segmentation. Customer segmentation is a marketing analytical tool that aids
customer-centric service and thus enhances profitability. In this paper, we aim
to develop a customer segmentation model to improve decision-making processes
in the retail market industry. To achieve this, we employed a UK-based online
retail dataset obtained from the UCI machine learning repository. The retail
dataset consists of 541,909 customer records and eight features. Our study
adopted the RFM (recency, frequency, and monetary) framework to quantify
customer values. Thereafter, we compared several state-of-the-art (SOTA)
clustering algorithms, namely, K-means clustering, the Gaussian mixture model
(GMM), density-based spatial clustering of applications with noise (DBSCAN),
agglomerative clustering, and balanced iterative reducing and clustering using
hierarchies (BIRCH). The results showed the GMM outperformed other approaches,
with a Silhouette Score of 0.80.
