A Customer Relationship Management Case Study Based on Banking Data
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.
KeywordsCustomer Relationship Management Machine learning Missing values Variables selection
We want to acknowledge Raffaele Brevetti, Luca Cilumbriello, Federica Perugini, Nicolò Russo and Dr. Enrico Tonini for their contribute in this work.
- 1.Agarwal, D.K., Chen, B.C.: Statistical Methods for Recommender Systems. Cambridge University Press, Cambridge (2015)Google Scholar
- 11.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)
- 12.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)Google Scholar