MINING IN CUSTOMER SEGMENTATION
The paper analyzes the possibilities of applying data mining techniques to improve customer segmentation. The aim of customer segmentation is to identify and profile lead, average and other company customers and to optimize and tailor future marketing actions so the right message can reach the right customer. Different techniques and models are applied in customer segmentation, like factor and cluster analysis in analyzing company customer data. In order to get required benefits from large data volumes stored in databases or data warehouses and to find hidden relationships between data, authors used cluster analysis, one of the data mining techniques. Data mining is a process of extracting previously unknown and potentially useful and hidden patterns from large databases. The main objective of the paper is to identify the high/medium/low-profit, high/medium/low-value and low/medium/high-risk customers by one of the data mining technique customer clustering. It presents results of empirical research related to data mining in customer segmentation made in a production company which produces and distributes products like dry fruits, nuts, seeds and cereals for the market of South-East Europe.