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MAKİNE ÖĞRENME ALGORİTMALARI İLE BİR TELEKOMUNİKASYON ŞİRKETİNDE MÜŞTERİ KAYIP TAHMİNİ ANALİZİ

Yıl 2021, Cilt: 32 Sayı: 3, 496 - 512, 31.12.2021

Öz

Bu araştırmanın amacı, makine öğrenimi algoritmalarının değerlendirilmesinin etkili bir müşteri kayıp tahmini (MKT) metodolojisine yönelik açıklayıcı bir analizini sağlamaktır. Hızla gelişen Müşteri İlişkileri Yönetimi (MİY) alanında, kaybetme eğiliminde olan müşterileri tutmak için uygun bir MKT metodolojisi önermek için, belirli müşterilerden açık kaynaklı bir veri madenciliği yazılımı olan WEKA'da oluşturulan makine öğrenimi algoritmalarını kullanarak bir telekomünikasyon şirketinden gelen anonim büyük bir veri setinden müşteri kaybını tahmin etmek için bir dizi veri madenciliği analizi yapılmıştır. Çalışma boyunca, Türkiye'deki özel bir telekomünikasyon şirketinden sırasıyla 195712, 32905 ve 228617 müşteri sayılarına sahip bireysel, kurumsal ve birleşik veri setleri kullanılarak müşteri kayıp tahminine ilişkin bir dizi deneysel analiz yapılmıştır. Müşteri kayıp durumunun tahmini için altı veri madenciliği algoritması değerlendirildi: Lojistik Regresyon, Naive Bayes, J48 ve RandomForest, Bagging ve Boosting gibi ELM şemaları. RandomForest, RandomTree'yi kullanırken, Bagging, temel öğrenme olarak J48'i kullanmaktadır. Deneysel analizler, MKT için uygulanan bu tür veri madenciliği analizlerine dayalı olarak gelecekteki müşteri kayıplarının olasılığının belirlenmesi için bazı karar ağaçlarının ve topluluk makine öğrenme sınıflandırıcılarının etkinliğini doğrulamak için şirketin tarihsel veri tabanından elde edilen reel veri kümeleri ile gerçekleştirilir. Sonuçlar, J48'in tüm veri kümelerine göre Naive Bayes'ten daha iyi performans gösterdiğini ve Lojistik Regresyon sınıflandırıcı şemasına çok benzer sonuçlar verdiğini göstermektedir. Ayrıca, Bagging büyük boyutlu veritabanını çözmediğinden ve J48, bireysel ve eksiksiz veri setlerinde benzer doğru sonuçlar verdiğinden, J48 karar ağacı sınıflandırıcısının yanı sıra müşteri kaybı tahmini için Bagging seçilebilir.

Kaynakça

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  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158(2019), 688–695.
  • Anuşlu, M. D., & Fırat, S. Ü. (2020). Ülkelerin Endüstri 4.0 Seviyesinin Sürdürülebilir Kalkınma Düzeylerine Etkisinin Analizi. Endüstri Mühendisliği, 1(0), 44–58.
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  • Baier, L., Kühl, N., Schüritz, R., & Satzger, G. (2020). Will the customers be happy? Identifying unsatisfied customers from service encounter data. Journal of Service Management. https://doi.org/10.1108/JOSM-06-2019-0173
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CUSTOMER CHURN PREDICTION ANALYSIS IN A TELECOMMUNICATION COMPANY WITH MACHINE LEARNING ALGORITHMS

Yıl 2021, Cilt: 32 Sayı: 3, 496 - 512, 31.12.2021

Öz

The purpose of this study is to provide a descriptive analysis of the assessment of machine learning algorithms to an effective customer churn prediction (CCP) methodology. In the rapidly developing field of Customer Relation Management (CRM), to propose a convenient CCP methodology for retaining the customers who tend to churn, a set of data-mining analyses has been conducted to predict customer churn from a bulky dataset from customers with specific attributes in a telecommunication company by using machine learning (ML) algorithms built in an open-source data mining software, WEKA. Throughout the study, a set of experimental analyses regarding customer churn prediction are conducted by using residential, corporate, and combined datasets with the number of incidences of 195712, 32905, and 228617 respectively a private telecommunication company in Turkey. Six data mining algorithms have been evaluated to predict the customer churn status: Logistic Regression, Naive Bayes, J48, and ELM schemes such as RandomForest, Bagging, and Boosting. RandomForest uses RandomTree, whereas Bagging uses J48 as a base learner. The experimental analyses are conducted with real-world datasets acquired from the company's historical database to validate some decision trees' effectiveness and ensemble ML classifiers to determine the likelihood of future churning customers based on such data mining analyses implemented for CCP. The results show that the J48 outperforms Naïve Bayes based on all datasets, and it provides very similar results as the Logistic Regression classifier scheme. Besides, since Bagging has not solved the large-sized database and J48 has given similar accurate results in the residential and complete data sets, the J48 decision tree classifier can be chosen and Bagging for customer churn prediction.

Kaynakça

  • Abbasimehr, H. (2011). A Neuro-Fuzzy Classifier for Customer Churn Prediction. International Journal of Computer Applications, 19(08), 35–41.
  • Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0191-6
  • Akan, M. Ö. A., Selam, A. A., Oktay Firat, S. Ü., Er Kara, M., & Özel, S. (2015). Comparative analysis of renewable energy use and policies: Global and Turkish perspectives. Sustainability (Switzerland), 7(12), 16379–16407. https://doi.org/10.3390/su71215820
  • Al-Mashraie, M., Chung, S. H., & Jeon, H. W. (2020). Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach. Computers and Industrial Engineering, 144(October 2019), 106476. https://doi.org/10.1016/j.cie.2020.106476
  • Altay, T. (2005). Knowledge Discovery in Databases and Data Mining Techniques: An Applied Study. Marmara University Institute of Science.
  • Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in the telecommunication industry using data certainty. Journal of Business Research, 94(March), 290–301. https://doi.org/10.1016/j.jbusres.2018.03.003
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158(2019), 688–695.
  • Anuşlu, M. D., & Fırat, S. Ü. (2020). Ülkelerin Endüstri 4.0 Seviyesinin Sürdürülebilir Kalkınma Düzeylerine Etkisinin Analizi. Endüstri Mühendisliği, 1(0), 44–58.
  • Anyanwu, M. N. (2011). Comparative Analysis of Serial Decision Tree Classification Algorithms. Journal of Computer Science, 3(3), 230–240. http://www.cscjournals.org/csc/manuscriptinfo.php?ManuscriptCode=72.73.66.82.82.44.55.49.99
  • Aoga, J. O. R., Guns, T., Nijssen, S., & Schaus, P. (2018). Finding Probabilistic Rule Lists using the Minimum Description Length Principle. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11198 LNAI, 66–82. https://doi.org/10.1007/978-3-030-01771-2_5
  • Avni Es, H., Hamzacebi, C., & Oktay Firat, S. U. (2018). Assessing the logistics activities aspect of economic and social development. International Journal of Logistics Systems and Management, 29(1), 1–16. https://doi.org/10.1504/IJLSM.2018.088577
  • Baier, L., Kühl, N., Schüritz, R., & Satzger, G. (2020). Will the customers be happy? Identifying unsatisfied customers from service encounter data. Journal of Service Management. https://doi.org/10.1108/JOSM-06-2019-0173
  • Bandara, R., Fernando, M., & Akter, S. (2020). Explicating the privacy paradox: A qualitative inquiry of online shopping consumers. Journal of Retailing and Consumer Services, 52(May), 101947. https://doi.org/10.1016/j.jretconser.2019.101947
  • Biçen, P., & Fırat, S. Ü. (2003). Knowledge Discovery in Databases KDD and Data Mining An Application of Customer Segmentation Analysis in Banking Sector. International Statistical Institute 54 Th Session.
  • Biçen, Pelin. (2002). Veri madenciliği: Sınıflandırma ve tahmin yöntemlerini kullanarak bir uygulama / Data mining: Application by using predictive and classification modelling. Yıldız Teknik Üniversitesi / Sosyal Bilimler Enstitüsü.
  • Ćamilović, D. (2008). Data Mining and CRM in Telecommunications. Serbian Journal of Management, 3(1), 61–72. Celik, H., & Güler, M. (2019). The Importance of Customer Loyalty With Corporate Governance in the Telecommunication Sector. 50–73. https://doi.org/10.4018/978-1-5225-9265-5.ch003
  • Çelik, U., & Başarır, C. (2017). The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner. Alphanumeric Journal, 5(1), 45–45. https://doi.org/10.17093/alphanumeric.290381
  • Çiçek, A., & Arslan, Y. (2020). Müşteri Kayıp Analizi İçin Sınıflandırma Algoritmalarının Karşılaştırılması. İleri Mühendislik Çalışmaları ve Teknolojileri Dergisi, 1(1), 13–19.
  • Çınar, A., & Silahtaroğlu, G. (2013). Veri Madenciliği Teknikleri ile Müşteri Memnuniyetine Etki Eden Gizli Nedenlerin Keşfi. Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. In Decision Support Systems (Vol. 95). Elsevier BV https://doi.org/10.1016/j.dss.2016.11.007
  • Craven, M. W., & Shavlik, J. W. (1997). Using neural networks for data mining. Future Generation Computer Systems, 13(2–3), 211–229. https://doi.org/10.1016/s0167-739x(97)00022-8
  • Dahiya, K., & Bhatia, S. (2015). Customer churn analysis in the telecom industry. 2015 4th International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions, ICRITO 2015, 1–6. https://doi.org/10.1109/ICRITO.2015.7359318
  • Dai, Q., Zhang, C., & Wu, H. (2016). Research of Decision Tree Classification Algorithm in Data Mining. International Journal of Database Theory and Application, 9(5), 1–8. https://doi.org/10.14257/ijdta.2016.9.5.01
  • Deligiannis, A., & Argyriou, C. (2020). Designing a Real-Time Data-Driven Customer Churn Risk Indicator for Subscription Commerce. International Journal of Information Engineering and Electronic Business, 12(4), 1–14. https://doi.org/10.5815/ijieeb.2020.04.01
  • Er Kara, M., Oktay Fırat, S. Ü., & Ghadge, A. (2020). A data mining-based framework for supply chain risk management. Computers and Industrial Engineering, 139(December 2018). https://doi.org/10.1016/j.cie.2018.12.017
  • Es, H. A. (2013). Yapay Sinir Aglari ile Turkiye Net Enerji Talep Tahmini. Gazi University.
  • Es, H. A. (2018). A novel classification approach based on multicriteria decision aiding / Çok kriterli karar destekli yeni bir sınıflandırma yaklaşımı. Marmara University / Institute of Science
  • Fırat, S. Ü., & Biçen, P. (2003). Veri Madenciliği Tekniklerini Kullanarak Banka Müşterileri Bölümlendirmesi ve Kredi Skorlama Modeli. Türkiye İstatistik Kurumu İstatistik Araştırma Dergisi, 2(2), 135–150.
  • Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64(3), 65–87. https://doi.org/10.1509/jmkg.64.3.65.18028
  • Geetha, A., & Nasira, G. (2014). Artificial neural networks’ application in weather forecasting – Using RapidMiner. International Journal of Computational Intelligence and Informatics, 4(3), 177–182.
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  • Gr, K. (2014). Meta-Learning in Decision Tree Induction. Springer International Publishing.
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  • Hassouna, M., Tarhini, A., Elyas, T., & Abou Trab, M. S. (2015). Customer Churn in Mobile Markets: A Comparison of Techniques. International Business Research, 8(6), 224–237. https://doi.org/10.5539/ibr.v8n6p224
  • Karakoç, Ö., Avni Es, H., & Firat, S. Ü. (2019). Evaluation of the development level of prov inces by grey cluster analysis. Procedia Computer Science, 158, 135–144. https://doi.org/10.1016/j.procs.2019.09.036
  • Karakurt, O., Erdal, H. I., Namlı, E., Yumurtacı-Aydoğmuş, H., & Türkkan, Y. S. (2013). Comparing Ensembles Of Decision Trees And Neural Networks For One-day-ahead Stream Flow Predict. Scientific Research Journal (SCIRJ), Volume I, Issue IV, November 2013, 1(4), 43–55. https://doi.org/10.9780/23218045/1172013/41
  • Karvana, K. G. M., Yazid, S., Syalim, A., & Mursanto, P. (2019). Customer Churn Analysis and Prediction Using Data Mining Models in Banking Industry. 2019 International Workshop on Big Data and Information Security, IWBIS 2019, 33–38. https://doi.org/10.1109/IWBIS.2019.8935884
  • Kayaalp, F. (2017). Review of Customer Churn Analysis Studies in Telecommunications Industry. Karaelmas Science and Engineering Journal, 7(2), 696–705.
  • Kelvin, K., Cindy, C., Charles, C., Leonardo, D. P., & Yennimar, Y. (2020). Customer Churn's Analysis In Telecommunications Company Using Fp-Growth Algorithm: Customer Churn's Analysis In Telecommunications Company Using Fp-Growth Algorithm. Jurnal Mantik, 4(2), 1285–1290.
  • Li, H., Yang, D., Yang, L., Lu, Y., & Lin, X. (2016). Supervised massive data analysis for telecommunication customer churn prediction. Proceedings - 2016 IEEE International Conferences on Big Data and Cloud Computing, BDCloud 2016, Social Computing and Networking, SocialCom 2016 and Sustainable Computing and Communications, SustainCom 2016, 163–169. https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.35
  • Machado, M. R., Karray, S., & De Sousa, I. T. (2019). LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry. 14th International Conference on Computer Science and Education, ICCSE 2019, ICCSE, 1111–1116. https://doi.org/10.1109/ICCSE.2019.8845529
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  • Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., & Kim, S. W. (2019). A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector. IEEE Access, 7, 60134–60149. https://doi.org/10.1109/ACCESS.2019.2914999
  • Wang, Q. F., Xu, M., & Hussain, A. (2019). Large-scale Ensemble Model for Customer Churn Prediction in Search Ads. Cognitive Computation, 11(2), 262–270. https://doi.org/10.1007/s12559-018-9608-3 Witten, I. H. (2020). Data-mining-with-weka. The University of Waikato.
  • Yeboah-Asiamah, E., Narteh, B., & Mahmoud, M. A. (2018). Preventing Customer Churn in the Mobile Telecommunication Industry: Is Mobile Money Usage the Missing Link? Journal of African Business, 19(2), 174–194. https://doi.org/10.1080/15228916.2018.1440462
  • Yu, R., An, X., Jin, B., Shi, J., Move, O. A., & Liu, Y. (2018). Particle classification optimization-based BP network for telecommunication customer churn prediction. Neural Computing and Applications, 29(3), 707–720. https://doi.org/10.1007/s00521-016-2477-3
  • Yulianti, Y., & Saifudin, A. (2020). Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes. IOP Conference Series: Materials Science and Engineering, 879(1). https://doi.org/10.1088/1757-899X/879/1/012090
  • Zhang, C. X., Zhang, J. S., & Wang, G. W. (2008). An empirical study of using Rotation Forest to improve regressors. Applied Mathematics and Computation, 195(2), 618–629. https://doi.org/10.1016/j.amc.2007.05.010
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Zeynep Uyar Erdem 0000-0003-4626-125X

Banu Çalış 0000-0001-8214-825X

Seniye Ümit Fırat 0000-0002-0271-5865

Yayımlanma Tarihi 31 Aralık 2021
Kabul Tarihi 14 Ekim 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 32 Sayı: 3

Kaynak Göster

APA Uyar Erdem, Z., Çalış, B., & Fırat, S. Ü. (2021). CUSTOMER CHURN PREDICTION ANALYSIS IN A TELECOMMUNICATION COMPANY WITH MACHINE LEARNING ALGORITHMS. Endüstri Mühendisliği, 32(3), 496-512.

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