The Application of K-Means Clustering Algorithm in the Profit of Building Material Rental Customers

K-means clustering algorithm is a common Unsupervised learning algorithm. It divides samples into multiple clusters by calculating their similarity, and each cluster represents a class of samples. In the profit analysis of building materials leasing customers, different customers can be classified and analyzed based on their profit levels using K-means clustering algorithm to find the high and low points and characteristics of profits. Specifically, for different customers, relevant financial data, business data, and other related data can be collected and integrated, such as the type of leased equipment, lease term, etc. Based on this data, K-means clustering algorithm can be used to establish a customer profit model, dividing customers into different clusters according to their profit level. Customers within the cluster have similar financial and leasing characteristics, which can better reflect the actual situation of the customer group. By analyzing different clusters, we can discover the profit level and characteristics of each cluster, and further grasp the leasing situation, leasing time, equipment type, and other information of customers in different clusters. These analysis results can serve as the basis for customer clustering management. By analyzing the characteristics and predictions of customer needs, a fast and accurate leasing plan can be formulated to improve customer satisfaction. In addition, building materials leasing companies can develop personalized marketing strategies based on cluster analysis results for customers in different clusters, and launch different preferential measures and service plans for customers with different profit levels to achieve differentiated competition. In summary, the K-means clustering algorithm can be well applied to the profit analysis of building materials leasing customers. Through cluster analysis, customers can be classified based on factors such as their profit level and leasing characteristics, providing more accurate and practical market analysis and management decision support for enterprises. In practical applications, it is necessary to select appropriate data and algorithms based on the actual situation, and continuously adjust and optimize them to ensure the accuracy and stability of the model.