Skip to main content

Advertisement

Springer Nature Link
Log in

Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A vital issue for customer correlation management and consumer conservation in the telecommunications business is increased customer churn. The data mining approaches can aid in the prediction of churn behavior of consumers. This article aims to propose a system to predict customer churn through hybrid probabilistic possibilistic fuzzy C-means clustering (PPFCM) along with artificial neural network (PPFCM-ANN). This paper comprises of two modules: (1) proposing clustering component on the basis of PPFCM and (2) churn prediction component on the basis of ANN. The input dataset is gathered into clusters, with the help of probabilistic possibilistic fuzzy C-means clustering algorithm in the clustering module. The obtained clustered information is used in the artificial neural network, and this hybrid construction is further used in the churn prediction module. During the testing process, the clustered test data select the most accurate ANN classifier which corresponds to the closest cluster of the test data, according to minimum distance or similarity measures. Finally, to predict the churn customer the output score value is utilized. Three sets of experiments are carried out: the primary set of experiments comprises PPFCM clustering algorithm, the secondary set assesses the classification result, and the third set authenticates the proposed hybrid model presentation. The proposed hybrid PPFCM-ANN model provides maximum accuracy when compared to any single model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Mattison R (2001) Telecom churn management: the golden opportunity. APDG Publishing, Fuquay Varina

    Google Scholar 

  2. Payne A, Frow P (2005) A strategic framework for customer relationship management. J Mark 69(4):167–176

    Article  Google Scholar 

  3. Reinartz W, Krafft M, Hoyer WD (2004) The customer relationship management process: its measurement and impact on performance. J Mark Res 41(3):293–305

    Article  Google Scholar 

  4. Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH (2006) Defection detection: measuring and understanding the predictive accuracy of customer churn models. J Mark Res 43(2):204–211

    Article  Google Scholar 

  5. Van den Poel D, Lariviere B (2004) Customer attrition analysis for financial services using proportional hazard models. Eur J Oper Res 157(1):196–217

    Article  MATH  Google Scholar 

  6. El-Zehery AM, El-Bakry HM, El-Ksasy MS (2013) Applying data mining techniques for customer relationship management: a survey. Int J Comput Sci Inf Secur 11(11):76

    Google Scholar 

  7. Reinartz WJ, Kumar V (2003) The impact of customer relationship characteristics on profitable lifetime duration. J Mark 67(1):77–99

    Article  Google Scholar 

  8. Lin SC, Tung CH, Jan NY, Chiang DA (2011) Evaluating churn model in CRM: a case study in Telecom. J Converg Inf Technol 6(11):192200

    Google Scholar 

  9. Sharma A, Panigrahi D, Kumar P (2013) A neural network based approach for predicting customer churn in cellular network services. arXiv preprint arXiv:1309.3945

  10. Huang Y, Huang B, Kechadi MT (2011) A rule-based method for customer churn prediction in telecommunication services. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 411–422

  11. Hwang H, Jung T, Suh E (2004) An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Syst Appl 26(2):181–188

    Article  Google Scholar 

  12. Larivire B, Van den Poel D (2005) Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst Appl 29(2):472–484

    Article  Google Scholar 

  13. Wei CP, Chiu IT (2002) Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst Appl 23(2):103–112

    Article  Google Scholar 

  14. Xia GE, Jin WD (2008) Model of customer churn prediction on support vector machine. Syst Eng Theory Pract 28(1):71–77

    Article  Google Scholar 

  15. Dietterich TG (2000) Ensemble methods in machine learning. Mult Classif Syst 1857:1–15

    Article  Google Scholar 

  16. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New York

    Book  MATH  Google Scholar 

  17. Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39(1):1414–1425

    Article  Google Scholar 

  18. De Bock KW, Van den Poel D (2011) An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Syst Appl 38(10):12293–12301

    Article  Google Scholar 

  19. Ali G, Artrk U (2014) Dynamic churn prediction framework with more effective use of rare event data: the case of private banking. Expert Syst Appl 41(17):7889–7903

    Article  Google Scholar 

  20. Dejaeger K, Verbeke W, Martens D, Baesens B (2012) Data mining techniques for software effort estimation: a comparative study. IEEE Trans Softw Eng 38(2):375–397

    Article  Google Scholar 

  21. Keramati A, Jafari-Marandi R, Aliannejadi M, Ahmadian I, Mozaffari M, Abbasi U (2014) Improved churn prediction in telecommunication industry using data mining techniques. Appl Soft Comput 24:994–1012

    Article  Google Scholar 

  22. Kim K, Jun CH, Lee J (2014) Improved churn prediction in telecommunication industry by analyzing a large network. Expert Syst Appl 41(15):6575–6584

    Article  Google Scholar 

  23. Au WH, Chan KC, Yao X (2003) A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Trans Evol Comput 7(6):532–545

    Article  Google Scholar 

  24. Coussement K, Van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst Appl 34(1):313–327

    Article  Google Scholar 

  25. Mozer MC, Wolniewicz R, Grimes DB, Johnson E, Kaushansky H (2000) Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans Neural Netw 11(3):690–696

    Article  Google Scholar 

  26. Zhang X, Zhu J, Xu S, Wan Y (2012) Predicting customer churn through interpersonal influence. Knowl Based Syst 28:97–104

    Article  Google Scholar 

  27. Verbeke W, Martens D, Baesens B (2014) Social network analysis for customer churn prediction. Appl Soft Comput 14:431–446

    Article  Google Scholar 

  28. Khashei M, Hamadani AZ, Bijari M (2012) A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Syst Appl 39(3):2606–2620

    Article  Google Scholar 

  29. Yeshwanth V, Raj VV, Saravanan M (2011) Evolutionary churn prediction in mobile networks using hybrid learning. In: Twenty-fourth international FLAIRS conference

  30. Bose I, Chen X (2009) Hybrid models using unsupervised clustering for prediction of customer churn. J Organ Comput Electr Commer 19(2):133–151

    Article  Google Scholar 

  31. Le M, Gabrys B, Nauck D (2014) A hybrid model for business process event and outcome prediction. Expert Syst 34(5):e12079

    Article  Google Scholar 

  32. Duke University (2005) Case studies, presentations and video modules. http://www.fuqua.duke.edu/centers/ccrm/datasetsdownload.html#data

  33. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, New York

    Book  MATH  Google Scholar 

  34. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110

    Article  Google Scholar 

  35. Iyigun C (2007) Probabilistic distance clustering. Wiley, New York

    Google Scholar 

  36. Pal NR, Pal K, Bezdek JC (1997) A mixed c-means clustering model. In: Proceedings of the sixth IEEE international conference on fuzzy systems, no 1. IEEE, pp 11–21

  37. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530

    Article  Google Scholar 

  38. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, Zhou ZH (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37

    Article  Google Scholar 

  39. Huang Y, Kechadi T (2013) An effective hybrid learning system for telecommunication churn prediction. Expert Syst Appl 40(14):5635–5647

    Article  Google Scholar 

  40. Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5(3):239–266

    Google Scholar 

  41. Hung SY, Yen DC, Wang HY (2006) Applying data mining to telecom churn management. Expert Syst Appl 31(3):515–524

    Article  Google Scholar 

  42. Lee J, Lee J (2006) Customer churn prediction by hybrid model. Adv Data Min Appl 4091:959–966

    Article  Google Scholar 

  43. Tsai CF, Lu YH (2009) Customer churn prediction by hybrid neural networks. Expert Syst Appl 36(10):12547–12553

    Article  Google Scholar 

  44. Zhang Y, Qi J, Shu H, Cao J (2007) A hybrid KNN-LR classifier and its application in customer churn prediction. In: IEEE International conference on systems, man and cybernetics, 2007, ISIC. IEEE, pp 3265–3269

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    E. Sivasankar & J. Vijaya

Authors
  1. E. Sivasankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Vijaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Vijaya.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sivasankar, E., Vijaya, J. Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network. Neural Comput & Applic 31, 7181–7200 (2019). https://doi.org/10.1007/s00521-018-3548-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3548-4

Keywords