E-ticaret Müşteri Bağlılığı Gri İlişkisel Kümeleme Analizi

E-commerce, which is one of the biggest developments that change our life with internet technologies, brings significant advantages to consumers and firm. Nowadays, e-commerce has become a necessity for firms to survive rather than as a competitive tool. In this context, e-commerce strategies such as acquiring new customers, retaining customers, building trust and providing customer loyalty have become important issues in terms of companies. Especially creating and maintaining customer loyalty are crucial issues to increase the profitability of the firm. For this reason, the identification of loyalty of customer groups is important in terms of ing the right sales strategies to be applied to these groups. Clustering analyzes are performed to determine customer groups, using K-means, K-medoids and fuzzy C-means algorithms or methods based on these algorithms for this purpose. However, these algorithms, known as central clustering algorithms, require values such as cluster number and cluster center, which are uncertain, as parameters before analysis. In this study, a customer loyalty clustering analysis was conducted based on actual transaction data from an e-commerce site, including the total number of purchases, total transaction amount, average transaction amount, number of entries on site, number of complaints, number of product return. Since the number of clusters and cluster centers are uncertain before the analysis, clustering was performed by Gray Relational Analysis. According to the results of the research, e-commerce customers' loyalty clusters have been realized with Gray Relational Clustering analysis, which shows that the clusters can be realized without determining the number of clusters and cluster centers before analysis.