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A data mining approach for bank telemarketing using the rminer package and r tool

Sérgio Moro (), Paulo Cortez and Raul Laureano ()

No 13-06, Working Papers Series 2 from ISCTE-IUL, Business Research Unit (BRU-IUL)

Abstract: Due to the global financial crisis, credit on international markets became more restricted for banks, turning attention to internal clients and their deposits to gather funds. This driver led to a demand for knowledge about client’s behavior towards deposits and especially their response to telemarketing campaigns. This work describes a data mining approach to extract valuable knowledge from recent Portuguese bank telemarketing campaign data. Such approach was guided by the CRISP-DM methodology and the data analysis was conducted using the rminer package and R tool. Three classification models were tested (i.e., Decision Trees, Naïve Bayes and Support Vector Machines) and compared using two relevant criteria: ROC and Lift curve analysis. Overall, the Support Vector Machine obtained the best results and a sensitive analysis was applied to extract useful knowledge from this model, such as the best months for contacts and the influence of the last campaign result and having or not a mortgage credit on a successful deposit subscription.

Keywords: Telemarketing; Direct Marketing; Long-Term Deposits; Data Mining; CRISP-DM; Classification Problem; Banking; R. (search for similar items in EconPapers)
JEL-codes: C38 C88 M31 (search for similar items in EconPapers)
Date: 2013-09-06
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