engleski

Business Client Segmentation in Banking Using Self-Organizing Maps

Segmentation in banking for the business clients market is traditionally based on the size measured in terms of income and the number or employees, and on statistical clustering methods (e.g. hierarchical clustering, k- means). The goal of the paper is to demonstrate that self- organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business clients market, with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross- selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analysed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments which differ with respect to the attributes of the foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, the views on the loan selection and the industry. Analyzed segments can be used by banks for deciding on the direction of further marketing activities.

self-organizing maps ; segmentation ; banking ; neural networks ; data mining

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