Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis
Pinar Karagoz (),
Ismail Hakki Toroslu and
Ferhat Yucel ()
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Merve Mitik: Middle East Technical University
Ozan Korkmaz: Middle East Technical University
Pinar Karagoz: Middle East Technical University
Ismail Hakki Toroslu: Middle East Technical University
Ferhat Yucel: Intertech Bilgi Islem Ve Pazarlama Ticaret A.S.
The Review of Socionetwork Strategies, 2017, vol. 11, issue 1, 17-31
Abstract Nowadays, many businesses, such as banks, use direct marketing methods to reach customers to minimize the campaigning cost and maximize the return rate. To achieve this, huge customer data should be analyzed to determine the most appropriate product offer for each customer and the most effective channel to reach her/him. However, since only a very small amount of responses collected from the customers are positive to the offers, the dataset is very imbalanced. This decreases sensitivity ratio of prediction results and makes it difficult to make a successful product and channel selection for the offer. In this paper, we propose a hybrid system, which first classifies the customers to decide if s/he is interested in the offered product, and then clusters them for product and channel suggestions. Experiments with real life banking data show very promising accuracy results for predicting the proper product and channel for the customers. Moreover, cost-profit analysis is also added to this problem. Our experiment results show that the proposed method decreases a fraction of the total profit, but since the decrease in the total cost is very large, there is a huge increase in the overall profit/cost ratio.
Keywords: Bank marketing; Direct marketing; Data mining; Naïve Bayes; C4.5 decision tree; CRM (search for similar items in EconPapers)
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