Decision Support System For A Customer Relationship Management Case Study

Özge Kart, Alp Kut, Vladimir Radevski

Abstract


Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesian classification. The accuracy rate of the model is determined by doing cross validation. The results demonstrated the applicability and effectiveness of the proposed model. Naive Bayesian classifier reported high accuracy. So the classification rules can be used to support decision making in CRM field. The aim of this study is to apply the data mining model to the banking sector as example case study. This work also contains an example data set related with customers to predict if the client will subscribe a term deposit. The results of the implementation are available on a mobile platform.

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DOI: http://doi.org/10.11591/ijict.v3i2.pp88-96

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