{"title":"Customer Churn Prediction: A Cognitive Approach","authors":"Damith Senanayake, Lakmal Muthugama, Laksheen Mendis, Tiroshan Madushanka","volume":99,"journal":"International Journal of Computer and Information Engineering","pagesStart":767,"pagesEnd":774,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10000977","abstract":"
Customer churn prediction is one of the most useful
\r\nareas of study in customer analytics. Due to the enormous amount
\r\nof data available for such predictions, machine learning and data
\r\nmining have been heavily used in this domain. There exist many
\r\nmachine learning algorithms directly applicable for the problem of
\r\ncustomer churn prediction, and here, we attempt to experiment on
\r\na novel approach by using a cognitive learning based technique in
\r\nan attempt to improve the results obtained by using a combination
\r\nof supervised learning methods, with cognitive unsupervised learning
\r\nmethods.<\/p>\r\n","references":"[1] K. Tsiptsis and A. Chorianopoulos, Data mining techniques in CRM:\r\ninside customer segmentation. John Wiley & Sons, 2011.\r\n[2] V. L. Migu\u00b4eis, D. Van den Poel, A. S. Camanho, and J. Falc\u02dcao e Cunha,\r\n\u201cModeling partial customer churn: On the value of first product-category\r\npurchase sequences,\u201d Expert systems with applications, vol. 39, no. 12,\r\npp. 11 250\u201311 256, 2012.\r\n[3] E. W. Ngai, L. Xiu, and D. C. Chau, \u201cApplication of data mining\r\ntechniques in customer relationship management: A literature review\r\nand classification,\u201d Expert systems with applications, vol. 36, no. 2, pp.\r\n2592\u20132602, 2009.\r\n[4] B. Larivi`ere and D. Van den Poel, \u201cPredicting customer retention and\r\nprofitability by using random forests and regression forests techniques,\u201d\r\nExpert Systems with Applications, vol. 29, no. 2, pp. 472\u2013484, 2005.\r\n[5] B.-H. Chu, M.-S. Tsai, and C.-S. Ho, \u201cToward a hybrid data mining\r\nmodel for customer retention,\u201d Knowledge-Based Systems, vol. 20, no. 8,\r\npp. 703\u2013718, 2007.\r\n[6] . T. C. Hung, C., \u201cSegmentation based on hierarchical self-organizing\r\nmap for markets of multimedia on demand,\u201d 2008.\r\n[7] . L. G. S. Berry, M. J. A., \u201cData mining techniques: For marketing,\r\nsales, and customer support.\u201d 2003.\r\n[8] T. Kohonen, \u201cThe self-organizing map,\u201d Proceedings of the IEEE,\r\nvol. 78, no. 9, pp. 1464\u20131480, 1990.\r\n[9] P. Rakic, \u201cSpecification of cerebral cortical areas,\u201d Science, vol. 241,\r\nno. 4862, pp. 170\u2013176, 1988.\r\n[10] R. M. Gray, \u201cVector quantization,\u201d ASSP Magazine, IEEE, vol. 1, no. 2,\r\npp. 4\u201329, 1984.\r\n[11] D. Alahakoon, S. Halgamuge, and B. Srinivasan, \u201cDynamic\r\nself-organizing maps with controlled growth for knowledge discovery,\u201d\r\nNeural Networks, IEEE Transactions on, vol. 11, no. 3, pp. 601\u2013614,\r\n2000.\r\n[12] M. B. T. Graepel and K. Obermayer, \u201cSelf-organizing maps:\r\ngeneralizations and new optimiza-tion techniques,\u201d Neurocomputing,\r\n1998.\r\n[13] P. Andras, \u201cKernel-kohonen networks,\u201d International Journal of Neural\r\nSystems, 2002.\r\n[14] C. F. D. Mac Donald, \u201cThe kernel self organ-ising map,\u201d in Proceedings\r\nof 4th International Con-ference on knowledge-based intelligence\r\nengineering systems and applied technologies, 2000.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 99, 2015"}