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
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.
TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI
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.
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.
Esen, M., & Bilgiç, E. (2018). TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI. Yönetim Ve Ekonomi Araştırmaları Dergisi, 16(1), 221-237. https://doi.org/10.11611/yead.446094
AMA
Esen M, Bilgiç E. TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI. Yönetim ve Ekonomi Araştırmaları Dergisi. September 2018;16(1):221-237. doi:10.11611/yead.446094
Chicago
Esen, M.fevzi, and Emrah Bilgiç. “TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI”. Yönetim Ve Ekonomi Araştırmaları Dergisi 16, no. 1 (September 2018): 221-37. https://doi.org/10.11611/yead.446094.
EndNote
Esen M, Bilgiç E (September 1, 2018) TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI. Yönetim ve Ekonomi Araştırmaları Dergisi 16 1 221–237.
IEEE
M. Esen and E. Bilgiç, “TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI”, Yönetim ve Ekonomi Araştırmaları Dergisi, vol. 16, no. 1, pp. 221–237, 2018, doi: 10.11611/yead.446094.
ISNAD
Esen, M.fevzi - Bilgiç, Emrah. “TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI”. Yönetim ve Ekonomi Araştırmaları Dergisi 16/1 (September 2018), 221-237. https://doi.org/10.11611/yead.446094.
JAMA
Esen M, Bilgiç E. TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI. Yönetim ve Ekonomi Araştırmaları Dergisi. 2018;16:221–237.
MLA
Esen, M.fevzi and Emrah Bilgiç. “TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI”. Yönetim Ve Ekonomi Araştırmaları Dergisi, vol. 16, no. 1, 2018, pp. 221-37, doi:10.11611/yead.446094.
Vancouver
Esen M, Bilgiç E. TOPSIS VE K-MEDOIDS YÖNTEMLERİYLE KARAR VERME: R PROGRAMLAMA DİLİ İLE MÜŞTERİ GERİ KAZANMA UYGULAMASI. Yönetim ve Ekonomi Araştırmaları Dergisi. 2018;16(1):221-37.