E-ticaret Müşteri Bağlılığı Gri İlişkisel Kümeleme Analizi

Abstract

İnternet teknolojileriyle hayatımızı değiştiren en büyük gelişmelerden olan e-ticaret tüketicilere ve firmalara önemli avantajlar getirmektedir. Günümüzde e-ticaret bir rekabet aracı olmaktan çok firmaların ayakta kalabilmesi için bir zorunluluk haline gelmiştir. Bu bağlamda yeni müşteri kazanmak, müşterileri elde tutmak, güven oluşturmak ve müşteri bağlılığını sağlamak gibi e-ticaret stratejileri, firmalar açısından önemli konular haline gelmiştir. Özellikle müşteri bağlılığını oluşturmak ve sürdürmek firma karlılığını arttırmak için hayati bir konudur. Bu sebeple bağlılık oluşan müşteri gruplarının belirlenmesi, bu gruplara uygulanacak doğru satış stratejilerinin seçilmesi açısından önem arz etmektedir. Müşteri gruplarının belirlenmesi için kümeleme analizleri gerçekleştirilmekte, bu amaçla K-ortalamalar, K-medoids ve bulanık C-ortalamalar algoritmaları veya bu algoritmaları temel alan metotlar kullanılmaktadır. Ancak merkezi kümeleme algoritmaları olarak bilinen bu algoritmalar belirsiz olan küme sayısı ve küme merkezi gibi değerleri analiz öncesi parametre olarak istemektedir. Bu çalışmada, bir e-ticaret sitesinden temin edilen, toplam satın alma işlem sayısı, toplam işlem tutarı, ortalama işlem tutarı, siteye giriş sayısı, şikayet sayısı ve ürün geri iade sayısı bilgilerini içeren gerçek işlem verileri temel alınarak müşteri bağlılığı kümeleme analizi gerçekleştirilmiştir. Analiz öncesinde küme sayısı ve küme merkezleri belirsiz olduğu için kümeleme işlemi Gri İlişkisel Analiz ile gerçekleştirilmiştir. Araştırma sonuçlarına göre, analiz öncesi küme sayısı ve küme merkezleri belirlenmeksizin kümelenmenin gerçekleştirilebileceği ortaya konulmuş, Gri İlişkisel Kümeleme analizi ile e-ticaret müşterilerinin bağlılık kümelenmeleri gerçekleştirilmiştir.

Keywords

• Gri Teori
• Gri İlişkisel Analiz
• Gri İlişkisel Kümeleme
• Bağlılık
• E-ticaret

Gray Relational Clustering Analysis of E-Commerce Customers Loyalty

Abstract

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.

Keywords

• Gray Theory
• Gray Relational Analysis
• Gray Relational Clustering
• Loyalty
• E-commerce

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