Segmentation bank customers using self-organizing maps

The demands of the customers are not uniform. Meeting all of these demands is not possible due to the variety of demand. It is important for firms to understand the customers and develop marketing strategies according to this insight. Today it is possible to get detailed data about the customers, owing to the developments in computer technologies. These data can be processed with data mining techniques in order to transform them to guiding information. One such data mining technique is self-organizing maps (SOM).The most important feature of self-organizing maps is to reduce the dimension of data by visualization. This visualization enables bank managers to easily understand customer profile. A data set which contains various data records about the customers was gathered from a bank whose name was kept in secret because of privacy issues. This data set is used to cluster the customers and visualize these clusters through self-organizing maps. As a result of the analysis dataset was clustered into 5 segments. Maps that are the result of the analysis reflect the characteristics of the commercial credit-user bank customers.