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

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Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 746))

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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.

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References

  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)

    Article  Google Scholar 

  2. Reichheld, F.F., Sasser, W.E.: Zero defections: quality comes to services. Harvard Bus. Rev. 68, 105–111 (1990)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  7. Hu, X.: A data mining approach for retailing bank customer attrition analysis. Appl. Intell. 22(1), 47–60 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Pannetier Lebeuf, S.: Prédiction de l’attrition en date de renouvellement en assurance automobile avec processus gaussiens (2011)

    Google Scholar 

  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. ASAC: Assurance et Sécurité: le marché camerounais de l’assurance. Magazine de L’ASAC, No. 39, pp. 18–34 (2017)

    Google Scholar 

  13. ASAC: Assurance et Sécurité. Magazine de l’Association des Sociétés d’assurances au Cameroun, 2016. No. 38, Décembre 2016

    Google Scholar 

  14. ASAC: Rapport sur le marché camerounais des assurances: Exercice 2015. Statistiques, pp. 7–8 (2015)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1), 245–271 (1997)

    Article  MathSciNet  Google Scholar 

  20. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Risselada, H., Verhoef, P.C., Bijmolt, T.H.: Staying power of churn prediction models. J. Interact. Mark. 24(3), 198–208 (2010)

    Article  Google Scholar 

  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. Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data Mining and Knowledge Discovery Handbook, pp. 875–886. Springer (2009)

    Chapter  Google Scholar 

  31. Brans, J.-P., Mareschal, B.: PROMETHEE methods. In: Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 163–186 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Université Catholique d’Afrique Centrale, FSSG, GRIAGES, Yaounde, Cameroun

    Rhode Ghislaine Nguewo Ngassam & Jean Robert Kala Kamdjoug

  2. Toulouse Business School, Toulouse, France

    Samuel Fosso Wamba

  3. Université Fédérale de Toulouse Midi-Pyrénées, 20 Boulevard Lascrosses, 31068, Toulouse, France

    Samuel Fosso Wamba

Authors
  1. Rhode Ghislaine Nguewo Ngassam
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  2. Jean Robert Kala Kamdjoug
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  3. Samuel Fosso Wamba
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Corresponding author

Correspondence to Jean Robert Kala Kamdjoug .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  3. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  4. DIMES, Universita della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Ngassam, R.G.N., Kamdjoug, J.R.K., Wamba, S.F. (2018). Setting up a Mechanism for Predicting Automobile Customer Defection at SAHAM Insurance (Cameroon). In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_83

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  • DOI: https://doi.org/10.1007/978-3-319-77712-2_83

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77711-5

  • Online ISBN: 978-3-319-77712-2

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