DECISION MAKING WITH TOPSIS AND K-MEDOIDS: AN APPLICATION ON CUSTOMER RECOVERY WITH R PROGRAMMING LANGUAGE

Yıl 2018, Cilt: 16 Sayı: 1, 221 - 237, 30.09.2018

M.fevzi Esen Emrah Bilgiç

https://doi.org/10.11611/yead.446094

Öz

Large companies prefer the most profitable customers when they try to recover their old customers who left the company and acquired by the competitors. This choice requires many variables to be evaluated simultaneously under the constraints of cost and time. TOPSIS method is a decision- making technique for multi-criteria problems and is used in a wide range of applications such as: supplier and facility location selection, production systems, enterprise resource planning and marketing, management, health, safety and environmental management problems etc. In this study, data of 1145 customers such as usage of voice, data service and other value added services usage are analyzed by TOPSIS method. The customers analyzed in this study are churned by a competitor of a telecommunication company. In order to determine the most valuable customers to company’s profit,TOPSIS scores are clustered into four segments by k-medoids clustering algorithm. As a result of the analysis, customers are segmented into four groups and totally 37 customers which were placed in the third segment identified as golden customers, thus the company should start churn activities with the customers placing in this segment

Anahtar Kelimeler

TOPSIS, K-Medoids Algorithm, Customer Recovery

TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI

Öz

Büyük işletmeler, kendisinden ayrılan ve rakip işletmelere geçen eski müşterilerine yönelik gerikazanma faaliyetleri düzenleyeceği zaman, kârlılığa en çok katkı yapan müşterileri tercih etmektedir. Bu seçim, çoğu zaman maliyet ve zaman kısıtları altında birçok değişkenin aynı anda değerlendirilmesini gerektirmektedir. TOPSIS yöntemi, çok kriterli problemler için oluşturulmuş bir karar verme tekniği olup, tedarikçi ve kuruluş yeri seçimi, üretim sistemleri, pazarlama yönetimi, sağlık,güvenlik ve çevre yönetimi gibi geniş bir uygulama alanında kullanılmaktadır. Bu çalışmada, bir telekomünikasyon işletmesinin rakip firmalara geçen 1145 müşterisine ait ses, veri ve katma değerliservilerle alakalı kullanım bilgileri TOPSIS yöntemi ile analiz edilmiştir. Elde edilen TOPSIS skorları işletme kârlılığına katkı sağlayan en değerli müşterileri tespit etmek amacıyla, k-medoids (k-ortaylar)kümeleme algoritması kullanılarak dört segmente ayrılmıştır. Çalışma sonucunda, dört segmenteayrılan müşterilerden üçüncü segmentte yer alan toplam 37 müşteri altın müşteri olarak tespit edilmiş ve geri kazanma faaliyetlerine bu müşterilerden başlanması gerektiği ortaya çıkmıştır.

Anahtar Kelimeler

TOPSIS, K-Medoids Algoritması, Müşteri Kaybı

Kaynakça

• Aggarwal, C. C. (2015) “Data mining: The textbook”, Switzerland: Springer.
• Arora, P. ve Varshney, D.S. (2016) “Analysis of K-Means and K-Medoids Algorithm For Big Data”, Procedia Computer Science, 78: 507-512
• Beikkhakhian, Y., Javanmardi, M., Karbasian, M. ve Khayambashi, B. (2015) “The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods”, Expert Systems with Applications, 42 (15–16): 6224-6236
• Berget, I. (2018) “Statistical Approaches to Consumer Segmentation”, Methods in Consumer Research, New Approaches to Classic Methods, 1: 353–382
• Bianchi, F.M., Rizzi, A., Sadeghian, A. ve Moiso, C. (2016) “Identifying user habits through data mining on call data records”, Engineering Applications of Artificial Intelligence, 54: 49-61
• Brito, P.Q., Soares, C., Almeida, S., Monte, A. ve Byvoet, M. (2015) “Customer segmentation in a large database of an online customized fashion business”, Robotics and Computer-Integrated Manufacturing, 36: 93-100
• Brusco, M.J., Steinley, D., Cradit, J.D. ve Singh, R. (2012) “Emergent clustering methods for empirical OM research”, Journal of Operations Management, 30(6): 454-466
• Craig A. B. ve Howard J. S. (2018) “U.S. Consumer Preferences for Telephone and Internet Services, Evidence from the RAND American Life Panel”, RAND Corporation, https://www.rand.org/, (11.03.2018)
• Deloitte (2015) “Opportunities in Telecom Sector: Arising from Big Data”, (https://www2.deloitte.com), (23.04.2018)
• Duncan, W. J. (1978) “Essentials of Management, 2nd Edition”, Hinsdale, The Dryden Press, Illinois
• Fu, X., Chen, X., Shi, Y.T., Bose, I. ve Cai, S. (2017) “User segmentation for retention management in online social games”, Decision Support Systems, 101: 51-68
• Ghnemat, R. ve Jaser, E. (2015) “Classification of Mobile Customers Behavior and Usage Patterns using Self-Organizing Neural Networks”, International Journal of Interactive Mobile Technologies, 9(4): 4-11
• Halkidi, M., Batistakis, Y. ve Vazirgiannis, M. (2001) “On clustering validation techniques”, Procedia- Social and Behavioral Sciences, 189: 275-284
• Han, H. ve Trimi, S. (2018) “A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms”, Expert Systems with Applications, 103: 133-145
• Harikumar, S. ve Surya PV. (2015) “K-Medoid Clustering for Heterogeneous DataSets”, Procedia Computer Science, 70: 226-237
• Hwang, C. L. ve Yoon, K. (1981) “Multiple Attribute Decision Making: A State of the Art Survey”, Springer-Verlag, New York
• Jain, A. K. ve Dubes, R. C. (1988) “Algorithms for clustering data”, Prentice-Hall, Inc. Journal of intelligent information systems, 17(2-3): 107-145
• Kang, D., Jang, W. ve Park, Y. (2016) “Evaluation of e-commerce websites using fuzzy hierarchical TOPSIS based on E-S-QUAL”, Applied Soft Computing, 42: 53-65
• Kaufman, L. ve Rousseeuw, P. J. (1987) “Clustering by Means of Medoids, Statistical Data Analysis Based on The L1–Norm and Related Methods”, North-Holland: 405–416
• Kaufman, L. ve Rousseeuw, P. J. (1990) “Finding Groups in Data: An Introduction to Cluster Analysis”, USA: Wiley
• Kim, G., Park, C.S. ve Yoon, K.P. (1997) “Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement”, International Journal of Production Economics, 50: 23-33
• Kusumawardani, R.P. ve Agintiara, M. (2015) “Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process”, Procedia Computer Science, 72: 638-646
• Lord, E., Willems, M., Lapointe, F.J. ve Makarenkov, V. (2017) “Using the stability of objects to determine the number of clusters in datasets”, Information Sciences, 393: 29-46
• Mavi, R.K., Goh, M. ve Mavi, N.K. (2016) “Supplier Selection with Shannon Entropy and Fuzzy TOPSIS in the Context of Supply Chain Risk Management”, Procedia - Social and Behavioral Sciences, 235: 216-225
• Park, H.S. ve Jun, C.H. (2009) “A simple and fast algorithm for K-medoids clustering”, Expert Systems with Applications, 36(2): 3336-3341
• Reynolds, A. P., Richards, G. ve Rayward-Smith, V. J. (2004) “The application of k-medoids and pam to the clustering of rules”, In International Conference on Intelligent Data Engineering and Automated Learning, Springer, Berlin, Heidelberg.
• Saglam, B., Salman, F., Sayin, S. ve Trkay, M. (2006) “A mixed-integer programming approach to the clustering problem with an application in customer segmentation”, Eur. J. Oper. Res., 173(3), 866–879
• Sekhar, C., Patwardhan, M. ve Vyas, V. (2015) “A Delphi-AHP-TOPSIS Based Framework for the Prioritization of Intellectual Capital Indicators: A SMEs Perspective”, Procedia - Social and Behavioral Sciences, 189: 275-284
• Sirisawat, P. ve Kiatcharoenpol, T. (2018) “Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers”, Computers & Industrial Engineering, 117: 303-318
• Teichert, T., Shehu, E. Y. ve Von Wartburg, I. (2008) “Customer segmentation revisited: the case of the airline industry”, Transportation Research Part A, 42: 227-242
• Xu, R. ve Wunsch, D.C. (2009) “Clustering”, http://site.ebrary.com/id/10257659. (30.03.2018)
• Zaki, M. J., Meira, W. (2014) “Data mining and analysis: fundamental concepts and algorithms”, UK: Cambridge University Press
• Zyoud, S.H. ve Hanusch,D.F. (2017) “A bibliometric-based survey on AHP and TOPSIS techniques”, Expert Systems with Applications, 78: 158-181.