Research on Application of Data Mining Algorithm Based on RFM in Customer Segmentation of Logistics Enterprises
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Xueyuan Wang, Wenyao Qu, Jiajun Dang
Market competition is becoming increasingly fierce, bringing serious challenges to the survival and development of logistics enterprises. Today, enterprises are unable to win only by the products in the market competition. Moreover, the enterprise focuses on the customers. To give differentiated service strategies based on classification of customers is an important part of implementing customer relationship management. Traditionally, to classify customers in accordance with the amount of consumption ignores two important factors between customers and business, including trading time and trading frequency, thus the accuracy rate is lower. In this paper, the RFM model is introduced into the process of classifying customers, and each attribute is given a certain weight. After the data are normalized by statistical knowledge, the RFM value of each customer is calculated. K-means method is used for classification. Then according to the clustering results, some different services and marketing strategies are adopted to different types of customers. Thereby the core competitiveness of enterprises is developed.
Logistics enterprises, K-means algorithm, customer segmentation.