The Application of K-Means Clustering Algorithm in the Ranking of Customer Groups of Financial Leasing Companies in the Process of Optimizing the Management of Right to Use Assets
Description
This study aims to enhance the effectiveness of asset management of customers' right to use by analyzing the customer groups of financial leasing firms. Therefore, the primary technique used in this study is the K-means clustering algorithm, which is combined with the customer dataset of financial leasing organizations for analysis. Firstly, the customer information of a representative financial leasing company is collected, including historical transaction data and customer characteristics. Then, some customers are selected as the experimental group, and these customers are clustered by K-means algorithm. The customer groups are divided into different clusters, and the customers are ranked according to their actual performance. Meanwhile, a control group is set up, and the clustering algorithm is not used for ranking. The results show that the customers of financial leasing companies are successfully divided into different clusters by applying K-means clustering algorithm, and the customer groups are ranked. Among them, the average transaction amount of cluster 1, cluster 2 and cluster 3 in the experimental group is $100,000, $75,000, and $50,000, respectively. It is better than the average transaction amount of cluster 1, cluster 2 and cluster 3 in the control group ($95,000, $69,000, $43,000, respectively). In addition, the repayment rate of customers in the experimental cluster 1 is the highest, which is 90%, and the repayment rates of clusters 2 and 3 are 85% and 80% respectively. These rates are also better than those in the control group (76%, 73% and 69% respectively). This shows that through the clustering and ranking of customer groups, companies can better understand the characteristics and performance of different clusters and adopt corresponding strategies to improve the management effect of right-to-use assets. This study offers the financial leasing industry a useful approach to maximize customer management and asset allocation decisions.