Research on customer segmentation and precision marketing strategy based on LRFMC model and K-means algorithm

Faced with fierce competition in the enterprise sector and the need to optimize customer resources, the traditional RFM model demonstrates insufficient applicability due to the limitations of the consumption amount indicator in certain scenarios. This study introduces the LRFMC model, which provides a more effective evaluation of customer value, and combines it with the K-means clustering algorithm to segment customers, aiming to achieve precise identification of customer value and formulation of marketing strategies. The experiment demonstrates that after data cleaning, attribute reduction, and standardization preprocessing, customers are aggregated into five groups; model performance evaluation shows that its silhouette coefficient reaches 0.72, and the overall accuracy of customer identification is 91.2%, an improvement of 6.9 percentage points compared to the traditional RFM model. It can effectively identify key groups such as important retention customers (with an identification accuracy of 92.5%) and important development customers. The results indicate that the customer segmentation method based on LRFMC and K-means not only significantly enhances the accuracy and robustness of customer value discrimination but also provides a reliable basis for the differentiated allocation of enterprise marketing resources, helping to optimize customer relationship management efficiency and maximize corporate profits.