Customer Segmentation Using RFM Model and Clustering Methods in Online Retail Industry

In our era, the issue of analyzing and predicting customer behavior and thoroughly aligning their business strategies and marketing activities for companies increases its inevitability everyday much more than before. In this context, segmenting customers has become the most necessary action for the firms all around the world. This study aims to make customer segmentation using the invoice data of an eCommerce company in Turkey. Accordingly, customer segmentation is carried out by the application of the RFM (Recency, Frequency and Monetary) model which is one of the most significant models used in customer segmentation to identify valuable customers. More on that, clustering methods are applied on the data retrieved from the RFM model and characteristics of each customer group created are analyzed. For this purpose, the most widely used K-Means and Fuzzy C-Means algorithms in the literature were selected. Followingly, by Silhouette and Dunn Indexes, the best performing algorithm and optimum number of clusters of this eCommerce company located in Turkey are provided as an insight for strategies at the end of the study.