Primena otkrivanja zakonitosti u podacima kod direktnog marketinga u bankarskom sektoru
Sažetak
Ključ uspešnog poslovanja leži u dobroj komunikaciji sa klijentima, tako da kompanije sve više pažnje posvećuju upravljanju odnosima sa klijentima. Jedna od strategija UOK je da anlizira i razume ponašanje i katakteristike potrošača, i na osnovu sprovođenja direktnih marketinških kampanja dolazi do potrebnih odgovora. Cilj rada je da identifikuje faktore koji će ukazati na klijente koji su spremni da prilože svoj deposit u banku. Dobijeni rezulatati izdvajaju grupu klijenata koja je zadovoljna sa poslovanjem banke i spremna za saradnju u marketinškim kampanjama. Upoređivanjem metode koje su korišćene u istraživanju, metoda klasifikacije se pokazala pouzdanijom. Ova analiza daje rezultate kroz upotrebu algoritma otkrivanja zakonitosti u podacima tj. stabla odlučivanja. Nedostatak ove metode je tačnost podataka dostavljenih od strane klijenta.
Reference
Athanassopoulos, A.D. (2010). Client satisfaction cues to support market segmentation and explain switching behavior.Journal of Business Research, 47(3), 191-207.
Barwise, P., & Farley, J.U. (2005). The state of interactive marketing in seven countries: Interactive marketing comes of age. Journal of Interactive Marketing, 19(3), 67-80.
Bult, J.R. (1993). Semiparametric versus Parametric Classification Models: An Application to Direct Marketing. Journal of Marketing Research, 30(3), 380-390. doi:10.2307/3172889
Bult, J.R., van der Scheer, H., & Wansbeek, T. (1997). Interaction between target and mailing characteristics in direct marketing, with an application to health care fund raising. International Journal of Research in Marketing, 14(4), 301-308.
Choachang, C. (2002). A cased based client classification approach for direct marketing. Expert System with applications,2, 163-168.
Cortez, P., Laureano, M.S.R., & Moro, S. (2010). Using data mining for banking direct marketing: An application of the crisp-dm methodology. Portugal: Institute University of Lisbon.
Coussement, K., Benoit, D., & van de Poel, D. (2009). Improve marketing decision making in a client churn prediction context using generalized additive models. Expert Systems with Applications, 38, 120-128.
Coussement, K., & Buckinx, W. (2011). A likelihood-mapping algorithm for calibrating the posterior probabilities: A direct marketing application. European Journal of Operational Research, 214, 732-738.
Crone, S., Lessmann, S., & Stahlbock, R. (2006). The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research, 781-800.
Glady, N., Baesens, B., & Croux, C. (2009). Modeling churn using client lifetime value. European Journal of Operational Research, 197, 402-411.
Gustafsson, A., Johnson, M.D., & Roos, I. (2005). The effects of client satisfaction, relationship commitment dimensions, and triggers on client retention. Journal of Marketing, 69(4), 210-218.
Hsiang-Hsi, L., & Chorng-Shyong, O. (2008). Variable selection in clustering for marketing segmentation using genetic algorithms. Expert Systems with Applications, 34, 502-510.
Hu, X. (2005). A Data Mining Approach for Retailing Bank Customer Attrition Analysis. Applied Intelligence, 22(1), 47-60. doi:10.1023/B:APIN.0000047383.53680.b6
Johnson, P.A., & Frankel, A.B. (2005). US Direct Marketing Today: Economic Impact 2005. NY, USA: Direct Marketing Association. Retrieved from http://www.thedma.org/research/economicimpact2005ExecSummary.pdf
Kaefera, F., Heilmanb, C., & Ramenofskya, S. (2005). A neural network application to consumer classification to improve the timing of direct marketing activities. Computers & Operations Research, 32, 2595-2615.
Kweku, M., & Bryson, O. (2010). Towards supporting expert evaluation of clustering results using a data mining process model. Information Sciences, 180, 414-431.
Li, W., Wu, X., Sun, Y., & Zhang, Q. (2010). Credit Card client segmentation and target marketing based on data mining. In:Proceedings of international conference on computational intelligence and security. 73-76.
Ling, X., & Li, C. (1998). Data mining for direct marketing: Problems and solutions. In: Proceeding of the 4th KDD conference. AAAI Press.73-79.
Rao, V.R., & Steckel, J.H. (1995). Selecting, evaluating, and updating prospects in direct mail marketing. Journal of Direct Marketing, 9(2), 20-31. doi:10.1002/dir.4000090205
Reichheld, F.F., & Jr. Sasser, W.E. (1990). Zero defections: quality comes to service. Harvard Business Review, 68(5),
Reinartz, W.J., & Kumar, V. (2002). The mismanagement of client loyalty. Harvard Business Review, 80,
Reinartz, W.J., & Kumar, V. (2003). The impact of client relationship characteristics on profitable lifetime duration. Journal of Marketing, 67(1),
Sarčević, M., Mašović, S., & Kamberović, H. (2010). Tehnike Text Mining-a i njihova realizacija primenom objektno-orijentisane analize. In: 18. Telekomunikacioni forum TELFOR, Beograd.
Torkzadeh, G., Chang, J., & Hansen, G.W. (2006). Identifying issues in client relationship management at Merck-Medco.Decision Support Systems, 42(2),
Young, H.C. (2012). Monte Carlo analysis of estimation methods for the prediction of client response patterns in direct marketing. European Journal of Operational Research, 217, 673-678.