Research on Optimization of Customer Value Segmentation Based on Improved K-Means Clustering Algorithm

摘要

In order to solve the problem of customer value, telecom enterprises generally classified them into the RFM model index, according to telecom customer analysis on the lack of forward-looking, so put forward FTCA customer segmentation model, industry characteristics, reflect the value of customers at the same time fusion and applies the model index to improve the peak density clustering algorithm. Because the clustering algorithm clustering effect is associated with the choice of truncation distance, so this paper proposes an adaptive density peak algorithm based on gini coefficient. In this article, through clustering algorithm evaluation index analysis and visualization analysis experiment, the results show that the model and algorithm of the classification of customers are more effectively and fully reflect customer value.