<![CDATA[In a competitive business environment, understanding customer behaviour and improving retention strategies are critical to a company's success. Many companies struggle to identify valuable customers, understand their needs, and develop effective marketing strategies. One method that has proven effective is Recency, Frequency, and Monetary (RFM) analysis, which measures customer value based on three dimensions: when the customer last made a purchase, how often they transact, and how much money they spend. This research focuses on applying the RFM method with Python for customer segmentation in a retail company. By analysing customer transaction data, this research shows how RFM analysis can provide deep insights into customer behaviour and assist in the development of more targeted marketing strategies. The ultimate goal is to improve customer retention and maximise the return on investment (ROI) of marketing activities. This research offers practical solutions to common challenges in customer relationship management and contributes to the development of more efficient data-driven marketing methods.]]>
Copyrights © 2024
