Research on Customer Segmentation of Net business User Data using K-means

Abstract

It is difficult to grasp the purchasing behavior, purchase consciousness and decision making of customers in previous studies on customer relationship management. In this research, the RF-PACV method has been proposed by increasing the number of purchases (R), cumulative purchase number (F), number of clicks (P), and number of items (A), item type (C), and number of favorite (V) based on the RFM model and the features of net business data. The Calinski-Hadabasz (CH) criterion is lead into the K-means clustering analysis method in this paper. As a result, the improved K-means clustering algorithmthe and the RF-PACV model are able to obtain five customer data, and the RF-PACV analysis model can analyze the characteristics of each customer group,and distinguish the different consumer habits and preferences of customers.