Abstract
References
- [1] Hung S.Y., Yen D.C. and Wang H.Y ,(2006). Applying Data Mining To Telecom Churn Management.. Expert Systems with Applications, vol. 31, pp.515–524.
- [2] Kirui C., Hong L., Wilson C., Kirui H. (2013). Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining. IJCS, 10.
- [3] Mattison R. (2005). The Telco Churn Management Handbook, Oakwood Hills, Illinois: XiT Press.
- [4] Wei C. P. and Chiu I.-T. (2002). Turning Telecommunication Calls Details To Churn Prediction: A Data Mining Approach, Expert Systems with Applications, vol. 31, pp. 103-112.
- [5] Huang B., Kechadi M. T. and Buckley B. (2012). Customer Churn Prediction In Telecommunications, Expert Systems with Applications, vol. 39, p. 1414–1425.
- [6] Basiri, Taghiyareh and Moshiri (2010). A Hybrid Approach to Predict Churn. Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific, pp.485 – 491.
- [7] Saradh and Palshikar (2011). Employees churn prediction. Expert Systems with Applications, vol. 38, pp. 1999-2006.
- [8] Geppert K. (2002). Customers churn management. KPMG International, A SWISS Association.
- [9] Edelstein H. (2000). Building Profitable Customer Relationships with Data Mining, Two Crows Corporations. [Online].Available: https://books.google.com.tr/
Abstract
Customers and products are the main
assets for every business. Companies make their best to satisfy customers
because of coming back to their companies. After sales service related to
different steps that make customers are satisfied with the company service and
products. After sales service covers different many activities to investigate
whether the customer is satisfied with the service, products or not? Hence,
after sales service is acting very crucial role for customer satisfaction,
retention and loyalty. If the after sales service customer and services data is
saved by companies, this data is the key for growing companies. Companies can add value their brand value
with the managing of this data. In this study, we aim to investigate effect of
6 factors on customer churn prediction via data mining methods. After sale
service software database is the source of our data. Our data source variables
are Customer Type, Usage Type, Churn Reason, Subscriber Period and Tariff The data is examined by data mining program.
Data are compared 8 classification algorithm and clustered by simple K means
method. We will determine the most effective variables on customer churn
prediction. As a result of this research we can extract knowledge from
international firms marketing data.
Keywords
Data Mining Customer Churn Prediction Customer Satisfaction Knowledge Discovery in Database
References
- [1] Hung S.Y., Yen D.C. and Wang H.Y ,(2006). Applying Data Mining To Telecom Churn Management.. Expert Systems with Applications, vol. 31, pp.515–524.
- [2] Kirui C., Hong L., Wilson C., Kirui H. (2013). Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining. IJCS, 10.
- [3] Mattison R. (2005). The Telco Churn Management Handbook, Oakwood Hills, Illinois: XiT Press.
- [4] Wei C. P. and Chiu I.-T. (2002). Turning Telecommunication Calls Details To Churn Prediction: A Data Mining Approach, Expert Systems with Applications, vol. 31, pp. 103-112.
- [5] Huang B., Kechadi M. T. and Buckley B. (2012). Customer Churn Prediction In Telecommunications, Expert Systems with Applications, vol. 39, p. 1414–1425.
- [6] Basiri, Taghiyareh and Moshiri (2010). A Hybrid Approach to Predict Churn. Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific, pp.485 – 491.
- [7] Saradh and Palshikar (2011). Employees churn prediction. Expert Systems with Applications, vol. 38, pp. 1999-2006.
- [8] Geppert K. (2002). Customers churn management. KPMG International, A SWISS Association.
- [9] Edelstein H. (2000). Building Profitable Customer Relationships with Data Mining, Two Crows Corporations. [Online].Available: https://books.google.com.tr/
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 26, 2016 |
| Published in Issue | Year 2016 Volume: 4 Issue: Special Issue-1 |