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.

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