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Credit Card Customer Segmentation and Target Marketing Based on Data Mining

Wei Li · Xuemei Wu · Yayun Sun · Quanju Zhang

29 citations0/0 files storedInternational Conference on Computational Intelligence and Security (CIS)

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Customer churn and segmentation Data Mining and Machine Learning Applications Financial Distress and Bankruptcy Prediction

Abstract

Based on the real data of a Chinese commercial bank's credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit cards holders. Conclusively, we obtain some useful information of decision tree regulation by the best model among the four. The information is not only helpful for the bank to understand related characteristics of different customers, but also marketing representatives to find potential customers and to implement target marketing.

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References

2000Marketing management: analysis. planning. implementation and control 2004Mining the customer credit using classification and regression tree and multivariate adaptive regression splines 1996Neural network for predicting the performance of credit card accounts

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2015Mining and Exploration of Credit Cards Data in UAE 2016A survey - data mining frameworks in credit card processing 2011An Empirical Study on Credit Scoring Model for Credit Card by Using Data Mining Technology 2024Optimizing Loan Approval Decisions: Harnessing Ensemble Learning for Credit Scoring 2011Credit users segmentation for improved customer relationship management in banking 2023Customer Segmentation Using Credit Card Data Analysis 2011Customer Segmentation of Bank based on Data Mining – Security Value based Heuristic Approach as a Replacement to K-means Segmentation 2014Comparative study of ensemble classifiers for direct marketing

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