CFP last date
21 October 2024
Reseach Article

Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm

by Riddhima Rikhi Sharma, Rajan Sachdeva
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 6
Year of Publication: 2017
Authors: Riddhima Rikhi Sharma, Rajan Sachdeva
10.5120/ijca2017912959

Riddhima Rikhi Sharma, Rajan Sachdeva . Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm. International Journal of Computer Applications. 159, 6 ( Feb 2017), 14-19. DOI=10.5120/ijca2017912959

@article{ 10.5120/ijca2017912959,
author = { Riddhima Rikhi Sharma, Rajan Sachdeva },
title = { Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 6 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number6/27005-2017912959/ },
doi = { 10.5120/ijca2017912959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:02.739110+05:30
%A Riddhima Rikhi Sharma
%A Rajan Sachdeva
%T Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 6
%P 14-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Various algorithms of Data Mining have been used for making distinguish between customers into loyal and churn. Boosting algorithms are iterative studying process that will combines poor classifiers as a way to create a powerful a classifiers. SVM is utilized for segmentation associated with churn clients. This paper represents the proposed Hybrid approach is an integration of two techniques named random forest and Support Vector Machine(SVM) that have feature of Artificial bee colony (ABC), provides better and accurate results in the prediction of churn customers.

References
  1. Adnan Idris,Asifullah Khan and Yeon Soo Lee( 2012) ," Genetic Programming and Adaboosting based churn prediction for Telecom”, Korean National Research Foundation, COEX, Seoul, Korea.
  2. Afaq Alam Khan, Sanjay Jamwal and M.M.Sepehri (2010), "Applying Data Mining to Customer Churn Prediction in an Internet Service Provider,” Vol. 9, No.7, Pp.8-14.
  3. Fröhlich, Ahn, P. Hana, and S. Lee (2006), “Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry, Telecommunications Policy”, International Journal of Computer Applications, Vol. 30(10-11), pp. 552-568
  4. A.Churi, M. Divekar and Reena Mahe(2015) , " Prediction Of Customer Churn In Mobile Industry Using Probabilistic Classifiers, " International Journal of Advance Foundation And Research In Science & Engineering, Vol. 1, No. 10,Pp. 41-49
  5. Alejandro Correa Bahnsen, Djamila Aouada and Bjorn Ottersten (2015), “A novel cost-sensitive frameworks for customer churn predictive modeling, " Decision Analytics a SpringerOpen Journal, Pp.1-15
  6. Alok Kumar Rai and Medha Srivastava(2012) , " Customer Loyalty Attributes: A Perspective, " NMIMS Management Review, Vol. 22, Pp.49-76
  7. Amal M. Almana, Mehmet Sabih Aksoy and Rasheed Alzahrani( 2014) , "A Survey On Data Mining Techniques In Customer Churn Analysis For Telecom Industry," Int. Journal of Engineering Research and Applications, Vol. 4, No.5, Pp.165-171
  8. A. Hudaib, R. Dannoun, O. Harfoushi, R. Obiedat and H. Faris(2015), " Hybrid Data Mining Models for Predicting Customer Churn " Int. J. Communications, Network and System Sciences, Vol. 8, Pp. 91-96
  9. Anuj Sharma and Dr. Prabin Kumar Panigrah(2011), “A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services”, International Journal of Computer Applications, Vol. 27, No.11,pp. 26-31
  10. A. O. Oyeniyi and A.B. Adeyemo(2015), " Customer Churn Analysis In Banking Sector Using Data Mining Techniques, " African Journal of Computing & ICT, Vol 8. No. 3, Pp.165-174
  11. Bart Baesens, Geert Verstraeten, Dirk Van den Poel, Michael Egmont (2004), “ Bayesian network classifiers for identifying the slope of the customer lifecycle of long life customers”, European Journal of Operational Research, Vol. 156, Pp. 508-523
  12. Burez J. and Van D. (2008), “Separating Financial from Commercial Customer Churn: A Modeling Step towards Resolving the Conflict between the Sales and Credit Department,” Expert Systems with Applications, Vol. 35, Issue 1, pp. 497-514
  13. Chih-Fong Tsai and Mao-Yuan Chen (2009), "Variable selection by association rules for customer churn prediction of multimedia on demand,” Expert Systems with Applications, Vol.30, Pp. 1-10
  14. Chris Rygielski , Jyun-Cheng Wang and David C. Yen(2002) , " Data mining techniques for customer relationship management, " Technology in Society, Vol. 24, Pp. 483–502
  15. Dr. M.Balasubramanian, M.Selvarani(2014), “Churn prediction in mobile telecom system”, International Journal of Scientific and Research Publications”, Vol. 4, No. 4, Pp.1-5
  16. Dr. U. Devi Prasad and S. Madhavi (2012)," Prediction Of Churn Behavior Of Bank Customers Using Data Mining Tools, " Business Intelligence Journal, Vol.5, No.1, Pp. 96-101
  17. H. Abbasimehr, M. Setak, and M. Tarokh (2014), "A Comparative Assessment of the Performance of Ensemble Learning in Customer Churn Prediction," The International Arab Journal of Information Technology, Vol. 11, No. 6, Pp. 599-606
  18. Emtiyaz, Mohammad Reza Keyvanpour(2011), “Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship management “, Advances in information sciences and Service Science, Vol.3, No. 9, Pp. 229-236
  19. E. Shaaban, Y. Helmy, A. Khedr and M. Nasr( 2012) , " A Proposed Churn Prediction Model, " International Journal of Engineering Research and Applications, Vol. 2, No. 4, Pp.693-697
  20. Georges D. Olle Olle and Shuqin Cai( 2014), "A Hybrid Churn Prediction Model in Mobile Telecommunication Industry, " International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 4, No. 1,Pp.55-62
Index Terms

Computer Science
Information Sciences

Keywords

SVM Customer churn behavior artificial bee colony algorithm Churn customers .