(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Öniz, Bengisu; Demir, Şeniz
Data about customers and their shopping habits is one of the most valuable assets of many organizations. Processing customer data, discovering unknown patterns, and getting useful results from them are primary purposes of customer segmentation. In this study, it is aimed to segment the customers of one of the leading apparel retail companies in Turkey. The data gathered from the company's e-commerce web page consists of web analytics and product purchases of customers. For clustering customer data, K-means and Agglomerative are used, and the number of clusters is determined via different distance metrics and silhouette scores. Our analysis results show that there are differences in purchasing frequencies, quantities, campaign sensitivities, and site usage patterns among clusters. Since customers in the same cluster are expected to share common purchasing habits, we argue that this study would be of great use in loss churn analysis or in a product recommendation system.