Öz
Veri madenciliği, büyük veri kümeleri içindeki anlamlı bilgiyi ortaya çıkarma sürecidir. Veri madenciliğinin yaygın olarak kullanıldığı uygulama alanlarından biri, ayrılma eğilimi gösteren müşterilerin tahmin edilmesidir. Churn adı verilen bu analiz, şirketlerin kaybetme potansiyeli olan müşterilerine özel pazarlama kampanyalarını geliştirmelerini sağlamaya yöneliktir. Bu çalışma, Türkiye’de telekomünikasyon sektöründe faaliyet gösteren büyük bir firmanın, ayrılma eğilimi gösteren müşterilerini belirleyerek; bu müşterilere özel pazarlama stratejileri geliştirilmesini hedeflemektedir. Ayrılacak müşteri profilini belirlemek için Lojistik Regresyon Analizi ve sınıflandırma tekniklerinden Karar Ağaçları kullanılmış ve uygulamanın sonuçları sunulmuştur.
Anahtar Kelimeler
Müşteri ayrılma analizi veri madenciliği lojistik regresyon karar ağaçları telekomünikasyon
Kaynakça
- C. Rygielski, J.C. Wang, D.C. Yen, Data Mining Techniques for Customer Relationship Management. Technology in Society. 24(4), 483-502 (2002).
- P. Fule, Exploratory Medical Knowledge Discovery: Experiences and Issues. ACM SIGKDD Explorations Newsletter. 5(1), 94-99 (2003).
- H. Ren, Y. Zheng, Y. Wu, Clustering Analysis of Telecommunication Customers. The Journal of China Universities of Post and Telecommunications. 16(2), 114-116 (2009).
- Y.T. Chang, Applying Data Mining to Telecom Churn Management. International Journal of Reviews in Computing. 69-77, (2009).
- B.Turhan, G. Koçak, A. Bener, Data Mining Source Code for Locating Software Bugs: A Case Study in Telecommunication Industry. Expert Systems with Applications. 36, 9986- 9990 (2009).
- C.S. Hilas, Designing an Expert System for Fraud Detection in Private Telecommunications Networks. Expert Systems with Applications. 36, 11559-69 (2009).
- S. Daskalaki, et al., Data Mining for Decision Support on Customer Insolvency in Telecommunications Business. European Journal of Operational Research. 145, 239-255 (2003).
- C.P. Wei, I.T. Chiu, Turning Telecommunications Call Details to Churn Prediction: A Data Mining Approach. Expert Systems with Applications. 23, 103-112 (2002).
- N. Akkaş, Kahin Şirketlerin Kehanetleri. http://www.sas.com/offices/europe/turkey/news/basindasas/inthenews_new_0109 08.htm (2010), (Erişim: 15.02.2010).
- E. Acar, Turkcell SAS Veri Madenciliği Çözümü İle Abonelikten Terk Oranını Yüzde 50 Azalttı. http://www.sas.com/offices/europe/turkey/news/pressreleases/ october2005/news02_131005.htm (2005), (Erişim: 08.01.2010).
- M. Çehreli, Numara Taşınabilirliğinde 463000 Kişinin Avea’ya Geçtiği Açıklandı. http://www.turk.internet.com/portal/yazigoster.php?yaziid=23087 (2009), (Erişim: 02.02.2010). (Erişim: 08.01.2010).
- Cross Industry Standard Process for Data Mining, www.crisp-dm.org (2010), (Erişim: 05.02.2010).
- M.L. Cohen, J.E. Rolph, D.L. Steffey, Statistics, Testing and Defense Acquisitions: New Approaches and Methodological Improvements. The National Academies Press, 1998, p.91.
- J. Walker, Methodology Application: Logistic Regression Using The CODES Data. Department of Transportation National Highway Traffic Safety Administration (NHTSA) and National Center for Statistics and Analysis. 8 (1996).
- T. Fears, J. Benichou, M.H. Gail, A Reminder of The Fallibility of The Wald Statistics. The American Statistician. 50 (1996).
- M.J. Norusis, Straight Talk About Data Analysis and SPSS. SPSS Professional Statistics. Chicago: SPSS, Inc. (1997).
- M.F. Kraska, J.R. Larkins, Factors Affecting Master Sergeants' Completion of Community College of the Air Force AAS Degree Requirements. Journal of Information Techonology Education. 36 (3) (1999).
Öz
Data mining is used to analyze mass databases for having meaningful output. One of the most common applications of the data mining, which is called as Churn Analysis is used to predict behavior of customers who are most likely to change provided service, and to create special marketing tools for them. The aim of this paper is to determine customers who want to churn, and to create specific campaigns to them by using a customer data of a major telecommunication firm in Turkey. To determine the reasons of the customer churn, logistic regression and decision trees analysis, which is one of the classification techniques, are applied.
Anahtar Kelimeler
Churn analysis data mining logistic regression decision trees telecommunication
Kaynakça
- C. Rygielski, J.C. Wang, D.C. Yen, Data Mining Techniques for Customer Relationship Management. Technology in Society. 24(4), 483-502 (2002).
- P. Fule, Exploratory Medical Knowledge Discovery: Experiences and Issues. ACM SIGKDD Explorations Newsletter. 5(1), 94-99 (2003).
- H. Ren, Y. Zheng, Y. Wu, Clustering Analysis of Telecommunication Customers. The Journal of China Universities of Post and Telecommunications. 16(2), 114-116 (2009).
- Y.T. Chang, Applying Data Mining to Telecom Churn Management. International Journal of Reviews in Computing. 69-77, (2009).
- B.Turhan, G. Koçak, A. Bener, Data Mining Source Code for Locating Software Bugs: A Case Study in Telecommunication Industry. Expert Systems with Applications. 36, 9986- 9990 (2009).
- C.S. Hilas, Designing an Expert System for Fraud Detection in Private Telecommunications Networks. Expert Systems with Applications. 36, 11559-69 (2009).
- S. Daskalaki, et al., Data Mining for Decision Support on Customer Insolvency in Telecommunications Business. European Journal of Operational Research. 145, 239-255 (2003).
- C.P. Wei, I.T. Chiu, Turning Telecommunications Call Details to Churn Prediction: A Data Mining Approach. Expert Systems with Applications. 23, 103-112 (2002).
- N. Akkaş, Kahin Şirketlerin Kehanetleri. http://www.sas.com/offices/europe/turkey/news/basindasas/inthenews_new_0109 08.htm (2010), (Erişim: 15.02.2010).
- E. Acar, Turkcell SAS Veri Madenciliği Çözümü İle Abonelikten Terk Oranını Yüzde 50 Azalttı. http://www.sas.com/offices/europe/turkey/news/pressreleases/ october2005/news02_131005.htm (2005), (Erişim: 08.01.2010).
- M. Çehreli, Numara Taşınabilirliğinde 463000 Kişinin Avea’ya Geçtiği Açıklandı. http://www.turk.internet.com/portal/yazigoster.php?yaziid=23087 (2009), (Erişim: 02.02.2010). (Erişim: 08.01.2010).
- Cross Industry Standard Process for Data Mining, www.crisp-dm.org (2010), (Erişim: 05.02.2010).
- M.L. Cohen, J.E. Rolph, D.L. Steffey, Statistics, Testing and Defense Acquisitions: New Approaches and Methodological Improvements. The National Academies Press, 1998, p.91.
- J. Walker, Methodology Application: Logistic Regression Using The CODES Data. Department of Transportation National Highway Traffic Safety Administration (NHTSA) and National Center for Statistics and Analysis. 8 (1996).
- T. Fears, J. Benichou, M.H. Gail, A Reminder of The Fallibility of The Wald Statistics. The American Statistician. 50 (1996).
- M.J. Norusis, Straight Talk About Data Analysis and SPSS. SPSS Professional Statistics. Chicago: SPSS, Inc. (1997).
- M.F. Kraska, J.R. Larkins, Factors Affecting Master Sergeants' Completion of Community College of the Air Force AAS Degree Requirements. Journal of Information Techonology Education. 36 (3) (1999).
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 3 Aralık 2009 |
| Yayımlandığı Sayı | Yıl 2010 Cilt: 39 Sayı: 1 |
