초록

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Abstract ▼ AI-Helper

Data mining has received a lot of attention from practitioners. That is partly because it allows company to extract a set of useful knowledge about customers from database, thereby retaining current customers and magneting potential customers. This logic is especially essential in the field of credi...

Data mining has received a lot of attention from practitioners. That is partly because it allows company to extract a set of useful knowledge about customers from database, thereby retaining current customers and magneting potential customers. This logic is especially essential in the field of credit card industry, where just 5% increase of number of customers is hewn to cause 120% increase in profit. The problem is how to retain current customers and even make them more loyal to company. However, previous studies lacked proposing extensive strategies of reducing the churning rate. In this sense, this study attempts to suggest such strategies by applying neural network, logistic regression, and C5.0 techniques to credit card data. We used a real data set of four years from 1997 to 2000, which were gathered from a credit card company. Experimental results revealed that our approach could yield robust strategies for retaining customers by reducing the churning rate.

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