Despite the lack of a unified definition customer relationship management (CRM) is defined as an integrated approach to acquiring and retaining customers, based on the processing of customer data and information technology. The key stages of CRM include identifying, acquiring, retaining, and developing customers. In this work, we define key concepts and described models in the first part and later focus on the use of data mining methods to support CRM. We performed customer segmentation using two models: RFM (Recency, Frequency, Monetary) and the K-means clustering method. Finally, we predicted customer lifetime value (CLV) using the BG/NBD and Gamma-Gamma models, where the former predicts the number of future customer transactions, and the latter predicts their monetary value. These analyses help the company to better understand its customers, adjust marketing strategies, and increase its competitiveness. Through the analysis, we confirmed the hypotheses that implementing a CRM strategy contributes to a greater competitive advantage for the company and enables better customer segmentation, which in turn contributes to optimisation of the marketing strategy and increased profitability for the company.
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