Business Client Segmentation in Banking Using Self-Organizing Maps

• Authors: Mirjana Pejić Bach , Sandro Juković , Ksenija Dumičić , Nataša Šarlija
• Languages of publication: EN

Abstracts

EN

Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of 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 client 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 analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

Keywords

EN

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

• Publisher: Sciendo
• Journal: South East European Journal of Economics and Business
• Year: 2014
• Volume: 8
• Issue: 2
• Pages: 32-41

Dates

published

2014-11-01

online

2014-11-20

Contributors

author

Mirjana Pejić Bach

• Faculty of Economics & Business, University of Zagreb

author

Sandro Juković

• IT Solutions Manager Erste&Steiermärkische Bank

author

Ksenija Dumičić

• Full professor Faculty of Economics & Business, University of Zagreb

author

Nataša Šarlija

• Faculty of Economics in Osijek, University of Osijek

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Identifiers

DOI

10.2478/jeb-2013-0007