Abstract
This work aims to show a product recommender construction approach within the banking industry. Such a model costruction should respect several methodological and business constraints. In particular, analysis’ outcome should be a model which must be easily interpretable when shown to business people. We start from a Customer Relationship Management data set collected in Banking industry. Formerly, data is prepared by managing missing values and keeping only the most relevant variables. Latterly, we apply some algorithms and evaluate them using diagnostic tools.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Considered data is referred to a subset of a leading Bank’s clients. More detailed informations and exploratory analysis are not here reported due to Compliance issues.
References
Agarwal, D.K., Chen, B.C.: Statistical Methods for Recommender Systems. Cambridge University Press, Cambridge (2015)
Baser, N.C., Thakar, D.G.: A literature review on customer relationship management in banks. Int. J. Customer Relat. Mark. Manage. (IJCRMM) 6(4), 43–56 (2015)
Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225–2236 (2010)
Hardle, W., Simar, L.: Applied Multivariate Statistical Analysis. Springer, Heidelberg (2007)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning. Springer, Heidelberg (2013)
Kevork, E.K., Vrechopoulos, A.P.: CRM literature: conceptual and functional insights by keyword analysis. Mark. Intell. Plann. 27(1), 48–85 (2009)
McCullagh, P., Nelder, J.A.: Generalized Linear Models, vol. 37. CRC Press, Boca Raton (1989)
Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36, 2592–2602 (2009)
Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39, 10059–10072 (2012)
Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinf. 9(1), 307 (2008)
Welling, S.H., Refsgaard, H.H.F., Brockhoff, P.B., Clemmensen, L.H.: Forest floor visualizations of random forests. arXiv preprint. arXiv:1605.09196 (2016)
Zineldin, M., Vasicheva, V.: Banking and financial sector in the cloud: knowledge, quality and innovation management. In: Cloud Systems in Supply Chains, pp. 178–194 (2015)
Acknowledgments
We want to acknowledge Raffaele Brevetti, Luca Cilumbriello, Federica Perugini, Nicolò Russo and Dr. Enrico Tonini for their contribute in this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Danesi, I.L., Rea, C. (2016). A Customer Relationship Management Case Study Based on Banking Data. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-51469-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51468-0
Online ISBN: 978-3-319-51469-7
eBook Packages: Computer ScienceComputer Science (R0)