E-ticaret Müşteri Bağlılığı Gri İlişkisel Kümeleme Analizi

Yıl 2018, , 163 - 182, 01.04.2018

Hüseyin Fidan

https://doi.org/10.5824/1309-1581.2018.2.010.x

Öz

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.

Anahtar Kelimeler

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

Gray Relational Clustering Analysis of E-Commerce Customers Loyalty

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Afrin, F., Al-Amin M., & Tabassum, M. (2015). Comparative Performance Of Using PCA With KMeans And Fuzzy C Means Clustering For Customer Segmentation. International Journal of Scientific & Technology Research, 4(10), 70-74.
• Bafghi, E. P. (2017). Clustering of Customers Based on Shopping Behavior and Employing Genetic Algorithms. Engineering, Technology & Applied Science Research, 7(1), 1420-1424.
• Bishop, C. M. (2007). Pattern Recognition and Machine Learning. Springer.
• Brown, S. P., & Peterson, R. A. (1993). Antecedents and Consequences of Salesperson Job Satisfaction: Meta-analysis and Assessment of Causal Effects. Journal of Marketing Research, 12, 161–173.
• Brown, M., Pope, N., & Voges, K. (2003). Buying or Browsing? An Exploration of Shopping Orientations and Online Purchase Intention. European Journal of Marketing, 37(11/12), 1666- 1684.
• Cheng, C. H., & Chen Y. S. (2009). Classifying the Segmentation of Customer Value via RFM Model and RS Theory. Expert Systems with Applications, 36(3), 4176-4184.
• Corstjens, M., & Lal, R. (2000). Building Store Loyalty Through Store Brands. Journal of Marketing Research, 37(3), 281–291.
• Işık, M., ve Çamurcu, A. Y. (2007). K-means, K-medoids ve Bulanık C-means Algoritmalarının Uygulamalı Olarak Performanslarının Tespiti. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6(11), 31-45.
• Davidson, I. (2002). Understanding K-means Non-hierarchical Clustering. SUNY Albany Technical Report 02-2.
• Deng, J. (1982). Control Problems of Grey Systems. System and Control Letters, 1(5), 288-294.
• Ellison, G., & Ellison, S. F. (2005). Lessons About Markets from the Internet. The Journal of Economic Perspectives, 19(2): 139-158.
• Ertuğrul, I., Öztaş, T., Özçil, A., & Öztaş, G. Z. (2016). Grey Relational Analysis Approach In Academic Performance Comparison Of University: A Case Study Of Turkish Universities. European Scientific Journal, June 2016 SPECIAL edition, 128-139.
• Fidan, H. (2014). Asimterik Bilginin E-ticaret Üzerindeki Etkileri: Tüketici Güveni Üzerine Bir Uygulama. Yayınlanmış doktora tezi, Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü, Isparta.
• Fidan, H. (2016). Measurement of the Intersectoral Digital Divide with the Gini Coefficients: Case Study Turkey and Lithuania. Inzinerine Ekonomika-Engineering Economics, 27(4), 439–451.
• Fidan, H., & Albeni, M. (2014). Asimetrik Bilginin E-Ticaret Üzerindeki Etkileri: Tüketicilerin Güven Eğilimleri Üzerine Bir Araştırma, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(2), 287-298.
• Gromov, G. (2012). Internet History with a Human Face. Retrieved November 15, 2017, from http://history-of-internet.com/history_of_internet.pdf .
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (Third Edition), USA: Morgan Kaufmann Publications.
• Hinduja, A., & Pandey, M. (2017). Multicriteria Recommender System for Life Insurance Plans based on Utility Theory. Indian Journal of Science and Technology, 10(14), 1-8, DOI: 10.17485/ijst/2017/v10i14/111376.
• Höppner, F., Klawonn, F., Kruse, R., & Runkler, T., (2000), Fuzzy Cluster Analysis, Chichester: John Wiley&Sons.
• Huang, X. ve Song, Z. (2014). Clustering Analysis on E-commerce Transaction Based on K-means Clustering. Journal of Networks, 9(2), 443-450.
• Ilsever, J., Cyr, D., & Parent, M. (2006). Extending Models of Flow and E-loyalty. Journal of Information Science and Technology, 3(4), 3–22.
• Islam M.A., Khadem, M. & Sayem, A. (2012). Service quality, customer satisfaction and customer loyalty analysis in Bangladesh apparel fashion retail: an empirical study, International Journal of Fashion Design. Technology and Education, 5(3), 213-224.
• Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666.
• Jain, A., Murty, M., & Flynn, P. (1999). Data Clustering: A review, ACM Comput. Surv, 31(3), 264–323.
• Kalaiselvi, B. (2015). A Comprehensive Usage of Enhanced K-Medoid Clustering Algorithm in Banking Sector. International Advanced Research Journal in Science Engineering and Technology, 2(7), 102-105.
• Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Chichester: John Wiley and Sons.
• Kaushik, S. (2016). An Introduction to Clustering and Different Methods of Clustering. Retrieved November 21, 2017, from https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to- clustering-and-different-methods-of-clustering.
• Kim K., & Ahn H. (2005). Using a Clustering Genetic Algorithm to Support Customer Segmentation for Personalized Recommender Systems. In: Kim T.G. (eds) Artificial Intelligence and Simulation (pp. 409-415), Berlin, Heidelberg: Springer.
• Lee, E. J., & Overby, J. W. (2004). Creating Value for Online Shoppers: Implications for Satisfaction and Loyalty. Journal of Consumer Satisfaction. Dissatisfaction and Complaining Behavior, 17, 54–64.
• Liu, S., Forrest J., & Yang, Y. (2012). A Brief Introduction to Grey Systems Theory. Grey Systems: Theory and Application, 2(2) 89-104.
• Liu, S., & Lin, Y. (2006). Grey Information Theory and Practical Applications. NewYork, USA: Springer Science+Business Media.
• MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. In Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, 281-297.
• Min, S. H. & Han I. (2005). Recommender Systems Using Support Vector Machines. In: Lowe D., Gaedke M. (Ed.) Web Engineering. ICWE 2005. Lecture Notes in Computer Science, (vol 3579, pp. 387-393). Berlin, Heidelberg: Springer.
• Niknam, T., & Amiri, B. (2010). An Efficient Hybrid Approach Based on PSO, ACO and K-means for Cluster Analysis. Applied Soft Computing, 10, 183–197.
• Park, H. S., & Jun, C. H. (2009). A Simple and Fast Algorithm for K-medoids Clustering. Expert Systems with Applications, 36, 3336–3341.
• Reichheld, F. (1995). Loyalty and the Renaissance of Marketing. Marketing Management, 2(4), 10-21.
• Reicheld, F. & Schefter, P. (2000). E-loyalty: your secret weapon on the Web. Harvard Business Review, July-August, 105-113.
• Rosen, S. (2001). Sticky Web site is Key to Success. Communication World, 18(3), 36-37.
• Spector, R. (2001). Amazon.com Ve Yaratıcısı Jeff Bezos. İstanbul: Scala Yayıncılık.
• Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer Loyalty in E-commerce: An Exploration of its Antecedents and Consequences. Journal of Retailing, 78, 41–50.
• Suganya, R., & Shanthi, R. (2012). Fuzzy C- Means Algorithm- A Review. International Journal of Scientific and Research Publications, 2(11).
• Sutton, R. S., & Barto, A. G. (2005). Reinforcement Learning: An Introduction. London, England: The MIT Press.
• Tajunisha, S. (2010). Performance Analysis of K-means with Different Initialization Methods for High Dimensional Data. International Journal of Artificial Intelligence & Applications, 44-52.
• Tan, P. N., Steinbach, M., & Kumar, V. (2013). Introduction to Data Mining, USA: Pearson Education Limited.
• Toufaily, E., Ricard, L., ve Perrien, J. (2013). Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and proposal of an integrative model. Journal of Business Research, 66, 1436–1447.
• Uncles, M. D., Dowling, G. R., & Hammond, K. (2003). Customer Loyalty and Customer Loyalty Programs. Journal of Consumer Marketing, 20(4), 294–316.
• Verona, G., & Prandelli, E. (2002). A Dynamic Model of Customer Loyalty to Sustain Competitive Advantage on the Web. European Management Journal, 20(3), 299–309.
• Wentian, J., Qingju, G., Sheng, Z., & En, Z. (2013).Improved K-medoids Clustering Algorithm under Semantic Web. Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), 731-733.
• Wu, W. H., Lin, C. T., Peng K. H. & Huang, C. C. (2012). Applying Hierarchical Grey Relation Clustering Analysis to Geographical Information Systems – A Case Study of the Hospitals in Taipei City. Expert Systems with Applications, 39, 7247–7254.
• Yıldırım, B. F. (2015). Gri İlişkisel Analiz.. In Yıldırım, B. F., & Önder, E. (Ed.), Çok Kriterli Karar Verme Yöntemleri (pp. 229-236 ). Bursa, Turkey: Dora Basım Yayın.
• Yuliari, N. P. P., Putra, K. G. D., & Rusjayanti, N. K. D. (2015).Customer Segmentation Through Fuzzy C-Means and Fuzzy RFM Method. Journal of Theoretical and Applied Information Technology, 78(3), 380-385.
• Yun, Z. S., & Good, L. K. (2007). Developing Customer Loyalty from E-tail Store Image Attributes. Managing Service Quality, 17(1), 4–22.