HC-means clustering algorithm for precision marketing on e-commerce platforms

With the rapid development of e-commerce industry, precision marketing has become a key means for enterprises to enhance competitiveness and profitability. However, traditional marketing methods often cannot accurately identify the characteristics of customers, leading to the waste of e-commerce resources. In this context, e-commerce enterprises urgently need a more accurate and efficient marketing method to meet the growing business needs. To this end, this study attempts to optimize the traditional K-means algorithm, and fundamentally improve the clustering effect in precision marketing by optimizing the selection of initial clustering centers and similarity measurement methods. Based on this, the research constructs an e-commerce marketing system based on HC-means algorithm to more accurately divide customer groups, identify high-value customers, potential customers and lost customers, and formulate differentiated marketing strategies for different groups. Experiments show that the average accuracy of HC-means algorithm in Glass database is 93.71, which is 15.48–15.79 higher than the highest accuracy of other two kinds of algorithms in the same kind of database. When the cluster number is 8, the Mahalanobis distance of HC-Means is reduced by 2.1 and 1.2 respectively compared with K-means and DBSCAN, which indicates that the clustering results are more reasonable in data distribution. When the cluster number is 3, more than half of the customers' consumption interval days are mainly concentrated between 8–12 days, and about 10 % of the customers make purchases every 2 days. These accurate customer behavior insights provide a strong basis for marketing strategy development. To sum up, the HC-Means system constructed by the research has achieved remarkable results in e-commerce precision marketing, greatly improving user satisfaction, and providing a valuable reference scheme for e-commerce enterprises to optimize marketing mode and achieve sustainable development.