RETRACTED ARTICLE: A study on e-commerce customer segmentation management based on improved K-means algorithm

Abstract

With the continuous popularization of the network, the customer resources have to be valued if enterprises want to occupy a certain share in the field of e-commerce. However, the traditional clustering analysis method has obvious lag for the segmentation of e-commerce customers. Therefore, accurate and efficient customer segmentation management should be carried out for the large and complex data information of current e-commerce enterprises, so as to realize customer retention and potential customer mining and promote the efficient development of enterprises. On the basis of customer segmentation theory, for the shortcomings of traditional K-means algorithm, a new SAPK + K-means algorithm based on semi-supervised Affinity Propagation combined with classic K-means algorithm is proposed in combination with AP algorithm, which is applied to e-commerce customers for segmentation management. The results show that when the SAPK + K-means algorithm clusters the iris dataset and the ionosphere dataset, the clustering time is longer than the K-means algorithm and the AP algorithm, but the algorithm error rate in the standard data is significantly reduced and the correct number of clusters can be obtained. The main steps of SAPK + K-means algorithm applied to customer segmentation management including data acquisition, cluster analysis and analysis and evaluation of clustering results. The SAPK + K-means algorithm clusters the data information of an e-commerce customer to obtain four different customer types and proposes corresponding strategies for each type of customer. It is concluded that the SAPK + k-means algorithm can significantly improve the clustering quality of customer data information and improve the effectiveness of activities of e-commerce enterprises.