Research Article
BibTex RIS Cite
Year 2016, , 63 - 66, 26.12.2016
https://doi.org/10.18201/ijisae.266801

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/

Customer Satisfaction Using Data Mining Approach

Year 2016, , 63 - 66, 26.12.2016
https://doi.org/10.18201/ijisae.266801

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.

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/
There are 9 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Burcu Oralhan

Kumru Uyar This is me

Zeki Oralhan

Publication Date December 26, 2016
Published in Issue Year 2016

Cite

APA Oralhan, B., Uyar, K., & Oralhan, Z. (2016). Customer Satisfaction Using Data Mining Approach. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 63-66. https://doi.org/10.18201/ijisae.266801
AMA Oralhan B, Uyar K, Oralhan Z. Customer Satisfaction Using Data Mining Approach. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(Special Issue-1):63-66. doi:10.18201/ijisae.266801
Chicago Oralhan, Burcu, Kumru Uyar, and Zeki Oralhan. “Customer Satisfaction Using Data Mining Approach”. International Journal of Intelligent Systems and Applications in Engineering 4, no. Special Issue-1 (December 2016): 63-66. https://doi.org/10.18201/ijisae.266801.
EndNote Oralhan B, Uyar K, Oralhan Z (December 1, 2016) Customer Satisfaction Using Data Mining Approach. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 63–66.
IEEE B. Oralhan, K. Uyar, and Z. Oralhan, “Customer Satisfaction Using Data Mining Approach”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 63–66, 2016, doi: 10.18201/ijisae.266801.
ISNAD Oralhan, Burcu et al. “Customer Satisfaction Using Data Mining Approach”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 2016), 63-66. https://doi.org/10.18201/ijisae.266801.
JAMA Oralhan B, Uyar K, Oralhan Z. Customer Satisfaction Using Data Mining Approach. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:63–66.
MLA Oralhan, Burcu et al. “Customer Satisfaction Using Data Mining Approach”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, 2016, pp. 63-66, doi:10.18201/ijisae.266801.
Vancouver Oralhan B, Uyar K, Oralhan Z. Customer Satisfaction Using Data Mining Approach. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):63-6.