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Cross-selling models for telecommunication services

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Języki publikacji
EN
Abstrakty
EN
Cross-selling is a strategy of selling new products to a customer who has made other purchases earlier. Except for the obvious profit from extra products sold, it also increases the dependence of the customer on the vendor and therefore reduces churn. This is especially important in the area of telecommunications, characterized by high volatility and low customer loyalty. The paper presents two cross-selling approaches: one based on classifiers and another one based on Bayesian networks constructed based on interesting association rules. Effectiveness of the methods is validated on synthetic test data.
Rocznik
Tom
Strony
52--59
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
Bibliografia
  • [1] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions”, IEEE Trans. Knowl. Data Eng. (TKDE), vol. 17, no. 6, pp. 734–749, 2005.
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  • [6] D. Heckerman, “A tutorial on learning with Bayesian networks”, Tech. Rep. MSR-TR-95-06, Microsoft Research, Redmond, 1995.
  • [7] S. Jaroszewicz and T. Scheffer, “Fast discovery of unexpected patterns in data, relative to a Bayesian network”, in 11th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. KDD 2005, Chicago, USA, 2005, pp. 118–127.
  • [8] S. Jaroszewicz and D. Simovici, “Interestingness of frequent itemsets using Bayesian networks as background knowledge”, in 10th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. KDD 2004, Seattle, USA, 2004, pp. 178–186.
  • [9] F. V. Jensen, Bayesian Networks and Decision Graphs. New York: Springer, 2001.
  • [10] W. Kamakura, M. Wedel, F. de Rosa, and J. Mazzon, “Cross-selling through database marketing: a mixed data factor analyzer for data augmentation and prediction”, Int. J. Res. Market., vol. 20, pp. 45–65, 2003.
  • [11] T. Mitchell, Machine Learning. New York: McGraw Hill, 1997. [12] J. Pearl, Probabilistic Reasoning in Intelligent Systems. Los Altos: Morgan Kaufmann, 1998.
  • [13] J. Quinlan, C4.5: Programs for Machine Learning. San Mateo: Morgan Kaufmann, 1993.
  • [14] “Sas cross-sell and up-sell for telecommunications”, http://www.sas.com/industry/telco/sell/brochure.pdf
  • [15] E. Suh, S. Lim, H. Hwang, and S. Kim, “A prediction model for the purchase probability of anonymous customers to support real time marketing: a case study”, Expert Syst. Appl., vol. 27, pp. 245–255, 2004.
  • [16] V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
  • [17] I. H.Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. San Mateo: Morgan Kaufmann, 2005.
  • [18] R. C.-W. Wong and A. W.-C. Fu, “ISM: item selection for marketing with cross-selling considerations”, in Eights Pacific-Asia Conf. Knowl. Discov. Data Min. PAKDD, Sydney, Australia, 2004, pp. 431–440.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BATA-0001-0039
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