Customer Satisfaction Using Data Mining Approach

  • Burcu ORALHAN
  • Kumru UYAR
  • Zeki ORALHAN
Keywords: Data Mining, Customer Churn Prediction, Customer Satisfaction, Knowledge Discovery in Database

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.

Downloads

Download data is not yet available.

References

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.

Kirui C., Hong L., Wilson C., Kirui H. (2013). Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining. IJCS, 10.

Mattison R. (2005). The Telco Churn Management Handbook, Oakwood Hills, Illinois: XiT Press.

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.

Huang B., Kechadi M. T. and Buckley B. (2012). Customer Churn Prediction In Telecommunications, Expert Systems with Applications, vol. 39, p. 1414–1425.

Basiri, Taghiyareh and Moshiri (2010). A Hybrid Approach to Predict Churn. Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific, pp.485 – 491.

Saradh and Palshikar (2011). Employees churn prediction. Expert Systems with Applications, vol. 38, pp. 1999-2006.

Geppert K. (2002). Customers churn management. KPMG International, A SWISS Association.

Edelstein H. (2000). Building Profitable Customer Relationships with Data Mining, Two Crows Corporations. [Online].Available: https://books.google.com.tr/

Published
2016-12-26
How to Cite
[1]
B. ORALHAN, K. UYAR, and Z. ORALHAN, “Customer Satisfaction Using Data Mining Approach”, IJISAE, pp. 63-66, Dec. 2016.
Section
Research Article