Research On The Customer Classification Of High-Speed Railway Based On Customer Loyalty

With the rapid development of China's social undertakings and market economy,the living standards of residents are increasing.The travel demand of passengers is no longer limited to fast and safe,but the pursuit of a better travel experience,which has led to increasing competition pressure in China's passenger transport market.At present,China's"four vertical and four horizontal" high-speed railway structure network has been completed,and the "eight vertical and eight horizontal" high-speed railway network is also planned.High-speed railway enterprises urgently need to establish a good customer relationship management model and establish a good corporate reputation to enhance the market competitiveness of high-speed rail transportation.In this paper,the high-speed railway customer classification method and model are deeply studied,which helps high-speed railway enterprises to design differentiated marketing strategies for different travel preferences of passengers,improve passenger travel experience and satisfaction.A good customer relationship management solution helps high-speed railway enterprises improve train resource utilization and optimize revenue management.Firstly,this paper summarizes the research status on customer classification of domestic and foreign experts and scholars,compares and analyzes the advantages and disadvantages of common customer classification methods and customer classification models,and expounds the characteristics of frequent flyer program operations of different enterprises.Then,by improving the meaning of RFM indicators,the customer psychological loyalty evaluation index is introduced,and the high-speed railway customer loyalty evaluation system was constructed.Combining with BP neural network classification method,a high-speed railway customer classification model based on customer loyalty was constructed.Finally,taking the passengers of Beijing-Shanghai high-speed railway as the research object,the empirical research on the classification model of high-speed railway customers is carried out.Based on the customer loyalty,the classification results of passengers on Beijing-Shanghai high-speed railway are obtained.The K-means clustering and decision tree classification are used to verify the accuracy and scientific of the customer classification results,and the customer classification results are used as the reference basis for designing the Beijing-Shanghai high-speed railway frequent flyer program.The innovation of this paper is to define the meaning and measurement dimension of high-speed railway customer loyalty.Combining the customer evaluation index of RFM model and the customer classification process of CLV model,the high-speed railway customer classification model based on customer loyalty is constructed.BP neural network training process is used to calculate the weight of evaluation index.According to customer loyalty,Beijing-Shanghai high-speed railway passengers are divided into gold medal passengers,silver medal passengers,frequen,t flyers and ordinary passengers.Based on the analysis of travel characteristics,loyalty and satisfaction of different types of passengers,the Beijing-Shanghai high-speed railway frequent flyer program was studied and designed.