Setting up a Mechanism for Predicting Automobile Customer Defection at SAHAM Insurance (Cameroon)

  • Rhode Ghislaine Nguewo Ngassam
  • Jean Robert Kala Kamdjoug
  • Samuel Fosso Wamba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

As markets become more competitive, companies have realized the need to manage the loss of customers (Churn) especially in terms of its prediction. To achieve this, in datamining framework, the main challenge is the selection of variables and the technique adapted to the studied context. This article examines the case of SAHAM insurance and uses ANOVA, chi-square test and Pearson correlations table for variable selection. To make an objective decision on selection of a technique among others, the multi criteria decision aid method PROMETHEE-GAIA has been used. With the aim to improve the initial model, which results was mitigated; the data set has been separated in two groups: individual customers and corporations. Then, with computation of the new one, we observe that, in general, performance is better on the group of individual customers than on previous global model and on corporations.

Keywords

Churn Data mining Decision tree PROMETHEE GAIA  ANOVA Chi square Pearson correlations 

References

  1. 1.
    Coussement, K., Benoit, D.F., Van den Poel, D.: Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert Syst. Appl. 37(3), 2132–2143 (2010)CrossRefGoogle Scholar
  2. 2.
    Reichheld, F.F., Sasser, W.E.: Zero defections: quality comes to services. Harvard Bus. Rev. 68, 105–111 (1990)Google Scholar
  3. 3.
    Gremler, D.D., Brown, S.W.: The loyalty ripple effect: appreciating the full value of customers. Int. J. Serv. Ind. Manag. 10(3), 271–293 (1999)CrossRefGoogle Scholar
  4. 4.
    Yabas, U., Cankaya, H.C.: Churn prediction in subscriber management for mobile and wireless communications services. In: 2013 IEEE Globecom Workshops (GC Wkshps). IEEE (2013)Google Scholar
  5. 5.
    Zhao, Y., et al.: Customer churn prediction using improved one-class support vector machine. In: International Conference on Advanced Data Mining and Applications. Springer (2005)CrossRefGoogle Scholar
  6. 6.
    Hadden, J., et al.: Computer assisted customer churn management: state-of-the-art and future trends. Comput. Oper. Res. 34(10), 2902–2917 (2007)CrossRefGoogle Scholar
  7. 7.
    Hu, X.: A data mining approach for retailing bank customer attrition analysis. Appl. Intell. 22(1), 47–60 (2005)CrossRefGoogle Scholar
  8. 8.
    Huang, B., Kechadi, M.T., Buckley, B.: Customer churn prediction in telecommunications. Expert Syst. Appl. 39(1), 1414–1425 (2012)CrossRefGoogle Scholar
  9. 9.
    Song, H.S., Kim, J.K., Kim, S.H.: Mining the change of customer behavior in an internet shopping mall. Expert Syst. Appl. 21(3), 157–168 (2001)CrossRefGoogle Scholar
  10. 10.
    Pannetier Lebeuf, S.: Prédiction de l’attrition en date de renouvellement en assurance automobile avec processus gaussiens (2011)Google Scholar
  11. 11.
    Huigevoort, C.W.J.M.: Customer churn prediction for an insurance company, Master of Science: Information System, p. 99. Eindhoven University of Technology, Eindhoven (2015)Google Scholar
  12. 12.
    ASAC: Assurance et Sécurité: le marché camerounais de l’assurance. Magazine de L’ASAC, No. 39, pp. 18–34 (2017)Google Scholar
  13. 13.
    ASAC: Assurance et Sécurité. Magazine de l’Association des Sociétés d’assurances au Cameroun, 2016. No. 38, Décembre 2016Google Scholar
  14. 14.
    ASAC: Rapport sur le marché camerounais des assurances: Exercice 2015. Statistiques, pp. 7–8 (2015)Google Scholar
  15. 15.
    Neslin, S.A., et al.: Defection detection: measuring and understanding the predictive accuracy of customer churn models. J. Mark. Res. 43(2), 204–211 (2006)CrossRefGoogle Scholar
  16. 16.
    Ngai, E.W., Xiu, L., Chau, D.C.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)CrossRefGoogle Scholar
  17. 17.
    Wei, C.-P., Chiu, I.-T.: Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst. Appl. 23, 103–112 (2002)CrossRefGoogle Scholar
  18. 18.
    Alsultanny, Y.A.: Database preprocessing and comparison between data mining methods. Int. J. New Comput. Archit. Appl. (IJNCAA) 1(1), 61–73 (2011)Google Scholar
  19. 19.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1), 245–271 (1997)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)CrossRefGoogle Scholar
  21. 21.
    Farquad, M.A.H., Ravi, V., Raju, S.B.: Churn prediction using comprehensible support vector machine: an analytical CRM application. Appl. Soft Comput. 19, 31–40 (2014)CrossRefGoogle Scholar
  22. 22.
    Tsai, C.-F., Chen, M.-Y.: Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Syst. Appl. 37(3), 2006–2015 (2010)CrossRefGoogle Scholar
  23. 23.
    Gordini, N., Veglio, V.: Architectures: customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Ind. Mark. Manag. 62(Supplement C), 100–107 (2017)CrossRefGoogle Scholar
  24. 24.
    Baccini, A., et al.: Pour l’analyse statistique de données transcriptomiques. Journal de la société française de statistique 146(1–2), 5–44 (2005)Google Scholar
  25. 25.
    Wu, H.-L., Zhang, W.-W., Zhang, Y.-Y.: An empirical study of customer churn in e-commerce based on data mining. In: 2010 International Conference on Management and Service Science (MASS). IEEE (2010)Google Scholar
  26. 26.
    Ali, Ö.G., Arıtürk, U.: Dynamic churn prediction framework with more effective use of rare event data: the case of private banking. Expert Syst. Appl. 41(17), 7889–7903 (2014)CrossRefGoogle Scholar
  27. 27.
    He, Y., He, Z., Zhang, D.: A study on prediction of customer churn in fixed communication network based on data mining. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009. IEEE (2009)Google Scholar
  28. 28.
    Risselada, H., Verhoef, P.C., Bijmolt, T.H.: Staying power of churn prediction models. J. Interact. Mark. 24(3), 198–208 (2010)CrossRefGoogle Scholar
  29. 29.
    Bin, L., Peiji, S., Juan, L.: Customer churn prediction based on the decision tree in personal handyphone system service. In: 2007 International Conference on Service Systems and Service Management. IEEE (2007)Google Scholar
  30. 30.
    Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data Mining and Knowledge Discovery Handbook, pp. 875–886. Springer (2009)CrossRefGoogle Scholar
  31. 31.
    Brans, J.-P., Mareschal, B.: PROMETHEE methods. In: Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 163–186 (2005)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rhode Ghislaine Nguewo Ngassam
    • 1
  • Jean Robert Kala Kamdjoug
    • 1
  • Samuel Fosso Wamba
    • 2
    • 3
  1. 1.Université Catholique d’Afrique Centrale, FSSG, GRIAGESYaoundeCameroun
  2. 2.Toulouse Business SchoolToulouseFrance
  3. 3.Université Fédérale de Toulouse Midi-PyrénéesToulouseFrance

Personalised recommendations