Over the last few years, we have witnessed the rapid development of information and web technologies. There is an increase in the use of the terms »information society« and »information economy« where data is valued more than the products and services themselves. Companies collect huge amounts of data which they process by means of data mining, and from which they obtain helpful information used to plan business and marketing strategies. In the thesis, we discussed the areas of application of data mining in electronic commerce. We found that e-commerce, due to its features, especially online presence, represents an effective domain for successful data mining. We focused mainly on customer segmentation in e-commerce which is the core of the CRM customer relationship management strategy. We presented the advantages of data mining in the segmentation process which relate mainly to process automation and the possibility of personalization, as well as described the most frequently used techniques and methods of data mining for customer segmentation. In the empirical part, we made a presentation of online store customer segmentation using the k-means clustering method and the LRFM model, which is considered to be one of the most effective behaviour-based customer segmentation models.
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