In today's competitive markets for a business success it is essential to fully understand customers, to strive to maximally satisfy their desires and preferences, and on this basis build a solid, long-term and fruitful relationship with customers. This is the core of customer relationship management. Good customer understanding is the basis for increase of customer lifetime value, which encompasses customer segmentation. The goal of customer segmentation is to group customers by common characteristics in the way that created segments are profitable and growing which will enable companies to target each segment with specific offerings. This cannot be done without utilization of intelligent methods and techniques for data analysis. The focus of this research is on business strategy driven customer segmentation, in attempt to maximize customer potentials which is the most important resource in business, with the focus on credit users' segmentation task in banking industry. Presented case study illustrates usage of multilayer feed forward neural network to segment bank customers into two groups: customers who have and who have not problems with payments.
['Zita Bosnjak', 'Olivera Grljevic']
Credit users segmentation for improved customer relationship management in banking