Using Class Membership based Approach to Improve Predictive Classification in Customer Relationship Management Systems
DOI:
https://doi.org/10.47839/ijc.21.2.2593Keywords:
CRM, Targeting, Machine learning, Predictive classification, Predictive analysis, Data mining, Classification, CMBAbstract
Recently, the diversity of data collected on both social networks and digital interfaces is extremely increased. This diversity of data raises the problem of heterogeneous variables that are not favourable to classification algorithms. Although machine learning and predictive analysis have significantly improved the efficiency of the classification in customer relationship management (CRM) systems, their performance remains very limited by heterogeneous data processing. In this paper, we propose a new predictive classification approach well adapted for targeting actual CRM systems. Our approach consists of preprocessing each type of feature and constructing a reduced array. From this reduced array, the class membership computations become very faster and perform the predictive targeting of a new instance great accurately. The results of the experiments carried out on four types of data from the CRMs showed that the proposed algorithm is a good tool for strengthening these systems not only to optimize their loyalty actions but also to efficiently acquire new customers.
References
E. W. T. Ngai, L. Xiu, and D. C. K. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Syst. Appl., vol. 36, no. 2 PART 2, pp. 2592–2602, 2009. https://doi.org/10.1016/j.eswa.2008.02.021.
B. T. Femina and E. M. Sudheep, “An efficient CRM-data mining framework for the prediction of customer behaviour,” Procedia Comput. Sci., vol. 46, no. Icict 2014, pp. 725–731, 2015. https://doi.org/10.1016/j.procs.2015.02.136.
J. Ranjan, V. Bhatnagar, “Critical success factors for implementing CRM using data mining,” J. Knowl. Manag. Pract., vol. 3, no. 9, pp. 18–25, 2008.
A. S. Dick, “Customer Loyalty: Toward an Integrated Conceptual Framework,” J. Acad. Mark. Sci., vol. 22, no. 2, pp. 99–113, 1978. https://doi.org/10.1177/0092070394222001.
M. S. Garver, “Using data mining for customer satisfaction research,” International Journal of Intelligent Systems and Applications in Engineering IJISAE, vol. 4, pp. 63–66, 2016. https://doi.org/10.18201/ijisae.266801.
B. Oralhan, K. Uyar, and Z. Oralhan, “Customer satisfaction using data mining approach,” Int. J. Intell. Syst. Appl. Eng., vol. 4, no. Special Issue, pp. 63–66, 2016. https://doi.org/10.18201/ijisae.266801.
Z. Zhang, H. Lin, K. Liu, D. Wu, G. Zhang, and J. Lu, “A hybrid fuzzy-based personalized recommender system for telecom products/services,” Inf. Sci. (Ny)., vol. 235, pp. 117–129, 2013. https://doi.org/10.1016/j.ins.2013.01.025.
J. Díez, D. Martínez-Rego, A. Alonso-Betanzos, O. Luaces, and A. Bahamonde, “Optimizing novelty and diversity in recommendations,” Prog. Artif. Intell., vol. 8, no. 1, pp. 101–109, 2019. https://doi.org/10.1007/s13748-018-0158-4.
W.-H. Au, K. C. C. Chan, and X. Yao, “A novel evolutionary data mining algorithm with applications to churn prediction,” IEEE Trans. Evol. Comput., vol. 7, no. 6, pp. 532–545, 2003. https://doi.org/10.1109/TEVC.2003.819264.
C.-P. Wei, I.-T. Chiu, “Turning telecommunications call details to churn prediction: a data mining approach,” Expert Syst. Appl., vol. 23, no. 2, pp. 103–112, 2002. https://doi.org/10.1016/S0957-4174(02)00030-1.
W. Verbeke, D. Martens, B. Baesens, “Social network analysis for customer churn prediction,” Appl. Soft Comput., vol. 14, pp. 431–446, 2014. https://doi.org/10.1016/j.asoc.2013.09.017.
T. Vafeiadis, K. Diamantaras, T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis, and K. Ch Chatzisavvas, “A comparison of machine learning techniques for customer churn prediction,” Simul. Model. Pract. Theory, vol. 55, pp. 1–9, 2015. https://doi.org/10.1016/j.simpat.2015.03.003.
M. Cioca, G. Andrada-Iulia, C. Lucian-Ionel, G. Daniela, and L. Blaga, “Machine learning and creative methods used to classify customers in a CRM systems,” Appl. Mech. Mater. Trans Tech Publ., vol. 371, pp. 769–773, 2013. https://doi.org/10.4028/www.scientific.net/AMM.371.769.
S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decis. Support Syst., vol. 50, no. 3, pp. 602–613, 2011. https://doi.org/10.1016/j.dss.2010.08.008.
B. Krawczyk, “Learning from imbalanced data: open challenges and future directions,” Progress in Artificial Intelligence, vol. 5, no. 4. Springer Verlag, pp. 221–232, 2016. https://doi.org/10.1007/s13748-016-0094-0.
S. Fletcher and Z. Islam, “An anonymization technique using intersected decision trees,” J. King Saud Univ. – Comput. Inf. Sci., vol. 27, no. 3, pp. 297–304, 2015. https://doi.org/10.1016/j.jksuci.2014.06.015.
H. Elmandili, H. Toulni, and B. Nsiri, “Optimizing road traffic of emergency vehicles,” Proceedings of the 2013 Int. Conf. Adv. Logist. Transp. ICALT 2013, 2013, pp. 59–62. https://doi.org/10.1109/ICAdLT.2013.6568435.
M. Boudhane, B. Nsiri, and H. Toulni, “Optical fish classification using statistics of parts,” Int. J. Math. Comput. Simul., vol. 10, no. 1, pp. 18–22, 2016.
S.-Y. Hung, D. C. Yen, and H.-Y. Wang, “Applying data mining to telecom churn management,” Expert Syst. Appl., vol. 31, pp. 515–524, 2006. https://doi.org/10.1016/j.eswa.2005.09.080.
C.-L. Huang, M.-C. Chen, and C.-J. Wang, “Credit scoring with a data mining approach based on support vector machines,” Expert Syst. Appl., vol. 33, pp. 847–856, 2007. https://doi.org/10.1016/j.eswa.2006.07.007.
X. Huang, X. Liu, and Y. Ren, “Enterprise credit risk evaluation based on neural network algorithm,” Cogn. Syst. Res., vol. 52, pp. 317–324, 2018. https://doi.org/10.1016/j.cogsys.2018.07.023.
S. F. Crone, S. Lessmann, and R. Stahlbock, “The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing,” Eur. J. Oper. Res., vol. 173, no. 3, pp. 781–800, 2006. https://doi.org/10.1016/j.ejor.2005.07.023.
A. Alphy and S. Prabakaran, “A dynamic recommender system for improved web usage mining and CRM using swarm intelligence,” Sci. World J., vol. 2015, no. Article ID 193631, pp. 1–16, 2015. https://doi.org/10.1155/2015/193631.
Y. L. Chen, C. L. Hsu, and S. C. Chou, “Constructing a multi-valued and multi-labeled decision tree,” Expert Syst. Appl., vol. 25, no. 2, pp. 199–209, 2003. https://doi.org/10.1016/S0957-4174(03)00047-2.
K. K. Lai, L. Yu, S. Wang, and W. Huang, “An intelligent CRM system for identifying high-risk customers: An ensemble data mining approach,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, vol. 4488 LNCS, no. PART 2, pp. 486–489. https://doi.org/10.1007/978-3-540-72586-2_70.
M. A. H. Farquad, V. Ravi, and S. B. Raju, “Analytical CRM in banking and finance using SVM: A modified active learning-based rule extraction approach,” Int. J. Electron. Cust. Relatsh. Manag., vol. 6, no. 1, pp. 48–73, 2012. https://doi.org/10.1504/IJECRM.2012.046470.
E. Arbi et al., “Improvement in automated diagnosis of soft tissues tumors using machine learning,” Big Data Min. Anal., vol. 4, no. 1, pp. 33–46, 2021. https://doi.org/10.26599/BDMA.2020.9020023.
E. Arbi, A. Alaoui, S. Cedric, and K. Tekouabou, “Intelligent management of bike sharing in smart cities using machine learning and Internet of Things,” Sustain. Cities Soc., vol. 67, no. April 2020, p. 102702, 2021. https://doi.org/10.1016/j.scs.2020.102702.
S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decis. Support Syst., vol. 62, pp. 22–31, 2014. https://doi.org/10.1016/j.dss.2014.03.001.
C. S. T. Koumetio, W. Cherif, and S. Hassan, “Optimizing the prediction of telemarketing target calls by a classification technique,” Proceedings of the 2018 International Conference on Wireless Networks and Mobile Communications, WINCOM 2018, 2019. https://doi.org/10.1109/WINCOM.2018.8629675.
E. Diaz-Aviles et al., “Towards real-time customer experience prediction for telecommunication operators,” Proceedings of the IEEE International Conference on Big Data, 2015, pp. 1063–1072. https://doi.org/10.1109/BigData.2015.7363860.
H. Xu, L. Wang, and W. Gan, “Application of improved decision tree method based on rough set in building smart medical analysis CRM system,” Int. J. Smart Home, vol. 10, no. 1, pp. 251–266, 2016. https://doi.org/10.14257/ijsh.2016.10.1.23.
C. S. T. Koumetio, W. Cherif, and S. Hassan, “Optimizing the prediction of telemarketing target calls by a classification technique,” Proceedings of the 2018 International Conference on Wireless Networks and Mobile Communications, WINCOM 2018, 2019, pp. 1–6. https://doi.org/10.1109/WINCOM.2018.8629675.
S. C. Koumetio, “A data modeling approach for classification problems : application to bank telemarketing prediction,” Proceedings of the NISS19, March 27–29, 2019, Rabat, Morocco, pp. 1–7.
B. Martin-Barragan, R. Lillo, and J. Romo, “Interpretable support vector machines for functional data,” Eur. J. Oper. Res., vol. 232, pp. 146–155, 2014. https://doi.org/10.1016/j.ejor.2012.08.017.
D. V. den P. Michel Ballings, “Evaluating multiple classifiers for stock price direction prediction,” Eur. J. Oper. Res., vol. 244, no. 1, pp. 248–260, 2015.
M. Ballings, D. Van den Poel, “CRM in social media: Predicting increases in Facebook usage frequency,” Eur. J. Oper. Res., vol. 244, no. 1, pp. 248–260, 2015. https://doi.org/10.1016/j.ejor.2015.01.001.
A. Ben-Hur and J. Weston, “A user’s guide to support vector machines,” Methods Mol. Biol., vol. 609, pp. 223–239, 2010. https://doi.org/10.1007/978-1-60327-241-4_13.
J. Yang and S. Olafsson, “Optimization-based feature selection with adaptive instance sampling,” Comput. Oper. Res., vol. 33, no. 11, pp. 3088–3106, 2006. https://doi.org/10.1016/j.cor.2005.01.021.
L. Yu and H. Liu, “Feature selection for high-dimensional data: A fast correlation-based filter solution,” Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003, pp. 856–863.
K. Lakshminarayan, S. A. Harp, and T. Samad, “Imputation of missing data in industrial databases,” Applied Intelligence, vol. 11, pp. 259–260, 1999. https://doi.org/10.1023/A:1008334909089.
D. Tripathi, D. R. Edla, V. Kuppili, A. Bablani, R. Dharavath, “Credit scoring model based on weighted voting and cluster based feature selection,” Procedia Comput. Sci., vol. 132, pp. 22–31, 2018. https://doi.org/10.1016/j.procs.2018.05.055.
A. C. Bahnsen, D. Aouada, and B. Ottersten, “Example-dependent cost-sensitive decision trees,” Expert Syst. Appl., vol. 42, no. 19, pp. 6609–6619, 2015. https://doi.org/10.1016/j.eswa.2015.04.042.
T. Verbraken, W. Verbeke, and B. Baesens, “A novel profit maximizing metric for measuring classification performance of customer churn prediction models,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 5, pp. 961–973, 2013. https://doi.org/10.1109/TKDE.2012.50.
S. Moro, P. Cortez, and P. Rita, “Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns,” Neural Comput. Appl., vol. 26, no. 1, pp. 131–139, 2014. https://doi.org/10.1007/s00521-014-1703-0.
G. Marinakos and S. Daskalaki, “Imbalanced customer classification for bank direct marketing,” J. Mark. Anal., vol. 5, no. 1, pp. 14–30, 2017. https://doi.org/10.1057/s41270-017-0013-7.
N. Ghatasheh, H. Faris, I. Altaharwa, Y. Harb, A. Harb, “Business analytics in telemarketing: Cost-sensitive analysis of bank campaigns using artificial neural networks,” Appl. Sci., vol. 10, pp. 8–13, 2020. https://doi.org/10.3390/app10072581.
C. Yan, M. Li, and W. Liu, “Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network,” Appl. Soft Comput. J., vol. 92, p. 106259, 2020. https://doi.org/10.1016/j.asoc.2020.106259.
M. Selma, “Predicting the success of bank telemarketing using artificial neural network,” Int. J. Econ. Manag. Eng., vol. 14, no. 1, pp. 1–4, 2020.
I. V Pustokhina, D. A. Pustokhin, P. Thanh, N. Mohamed, and K. Shankar, “Multi ‑ objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector,” Complex & Intell. Syst., 2021. https://doi.org/10.1007/s40747-021-00353-6.
T. Xu, Y. Ma, and K. Kim, “Telecom churn prediction system based on ensemble learning using feature grouping,” Appl. Sci., vol. 11, no. May, p. 4742, 2021. ttps://doi.org/10.3390/app11114742.
E. Stripling and B. Baesens, “PT US CR,” Eur. J. Oper. Res., vol. 284, no. 3, pp. 920–933, 2018.
S. K. Trivedi, “A study on credit scoring modeling with different feature selection and machine learning approaches,” Technol. Soc., vol. 63, no. September, p. 101413, 2020. https://doi.org/10.1016/j.techsoc.2020.101413.
P. Pławiak, M. Abdar, J. Pławiak, and V. Makarenkov, “DGHNL : A new deep genetic hierarchical network of learners for prediction of credit scoring,” Inf. Sci. (Ny)., vol. 516, pp. 401–418, 2020. https://doi.org/10.1016/j.ins.2019.12.045.
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