K-LRFMD: Method of Customer Value Segmentation in Shared Transportation Filed Based on Improved K-means Algorithm

Abstract

The advent of information age has transformed the focus of enterprise marketing from product-centric to customer-centric, and customer relationship management becomes the core problem of enterprises. Accurate customer value classification results are an important basis for enterprises to optimize marketing resources allocation, and customer value classification is becoming one of the key issues that need to be solved urgently in customer relationship management. In the face of the fierce market competition of the vehicle-sharing industries, each shared transportation company has introduced more preferential marketing methods to attract more customers. In this paper, with the aid of the vehicle-sharing platform in a domestic university campus, we established a reasonable customer value evaluation model called K-LRFMD. K-LRFMD did some clustering analysis with the customers based on specific feature engineering and improved K-means algorithm. In this paper, we compare different customer value derived from K-LRFMD. The analysis can formulate the corresponding marketing strategy to provide personalized customer service for different customers.