The thesis deals with customer segmentation with the help of data mining and identifies relevant methods and data that are important for segmentation in the banking sector. The period of rapid development of technology and advanced analytical tools requires constant adjustment of the offer to consumers and analysis of their needs and habits. In addition to the many benefits we receive from the advancement of technology, we also face many challenges. In banking, this is especially evident in the frequent abuses and frauds that banks try to avoid and prevent on a daily basis. A large number of customers cannot be addressed individually, so segmentation in banking is extremely important and allows banks to adjust their offer to customers based on their common characteristics. In the empirical part of the thesis, I presented an example of customer segmentation based on demographic characteristics. I used the k-means clustering method, which is one of the most commonly used methods for customer segmentation. I described the obtained clusters and defined the key characteristics of individuals who belong to a certain cluster and what services the bank can offer to individuals within these clusters.
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