Akademik Veri Yönetim Sistemi
İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi, cilt.16, sa.4, ss.551-573, 2024 (Hakemli Dergi)
Businesses that carry out marketing efforts moved away from product-oriented work, understood the importance of the customer, and shifted towards customercentered practices. This situation has made customer-centered efforts more important and has caused businesses to focus more on activities related to customer relations. Today with tech development and increasing competition, company-customer relations become more important. Creating a customer profile is critical for businesses to recognize their customers and distinguish their most profitable customers. By understanding their customers’ behavior, businesses can tailor their marketing and customer relationship management strategies. Thus, they can meet their customers’ needs, increase their satisfaction and loyalty to their businesses and encourage them to shop with them again. Thus, this study aims to categorize customers based on RFM metrics and interpret the obtained clusters from a marketing perspective. At the segmentation phase, hierarchical and non-hierarchical clustering methods, namely k-means, AGNES, and DBSCAN, are used and the results are compared. First, data, which consist of the shopping information of 38975 customers who shopped from e-commerce in one year, are collected from a textile retail company in Istanbul. Then, the purchase amount spent by customers is additionally scored to reveal the most valuable customers. It is observed that better results are mined from the k-means algorithms. As a result, four different customer types are determined: loyal customer, potential customer, new customer, and lost customer types. In conclusion, profile-oriented marketing strategies are presented.