Today’s customer understanding has become essential for businesses, especially online once, as the influx of new consumers continues to grow rapidly each day. The challenges for companies are to cater to the requirements of different behaviour groups of consumers and attract them, calling for customer segmentation. This study focuses on analysing customer segmentation in the e-commerce industry. The study addresses the need for effective customer segmentation due to the exponential growth of online consumers, emphasising the benefits for businesses in understanding customer behaviours and tailoring marketing strategies. It involves a detailed examination of customer data types, including income, purchasing behaviour, and demographic information, using advanced data analysis and machine learning techniques. The research employs K-means clustering for customer segmentation, evaluating its effectiveness through the Silhouette Score and Davies-Bouldin Index. The study also highlights the significance of advanced data visualisation tools like Power BI and Python for extracting insights from complex datasets. The project’s contributions include improved e-commerce strategies through effective customer segmentation, enhanced decision-making using advanced visualisation, and in-depth analysis of customer behaviours. The research aims to enable businesses to customise marketing strategies, improving customer satisfaction and loyalty. Future work suggests expanding the dataset and exploring alternative clustering algorithms for more comprehensive customer segmentation in e-commerce.