Research On The Methods In Customer Satisfaction And Marketing Segmentation For Airline Companies In China

In recent years, airline industry in China steps into rapid development. Since then, Airline industry in China continues its renovation and improves its transportation, which strengthens its power and raises its status all over the world, especially in the tenth five-year program. At the same time, airline industry in China is confronted with the huge sum of deficit. At the same time, airline companies in China are still operation at a loss. As statistical figures released by CAAC manifest, in the past six years, airline industry in China deficit for 4 years, gains a profit of for 2 years. In all airline industry lost 1,860,000,000 yuan. It is not only because of the increase in wages, welfares but also because of customer loss and poor service attitude.This paper uses customer satisfaction theory, combines the status quo of China airline industry, and utilizes statistics methods to instruct China airline industry. In the first two chapters, this paper analyzes the research background and meaning of customer satisfaction theory and models in home and abroad, and then discusses why and how this research is conducted. Analyses and Discussions help construct China airline customer satisfaction models.The most important part of this paper is Chapter 3 and Chapter 4. In Chapter 3, this paper constructed CSI model of China Airline companies, and computed CSI of X Airlines. And then, this paper identifies 9 quality variables that affect customers'Perceived Quality and determines how satisfied customers are towards the 9 quality variables and how much the 9 quality variables affect Perceived Quality.In Chapter 4, this paper uses every customer's Customer Satisfaction(CS) and Customer Loyalty(CL) as marketing segmentation variables and uses K-means Clustering as marketing segmentation methods to clusters customers into 4 segments. The 4 segments are segment with high CS and high CL, segment with high CS and low CL, segment with low CS and high CL, and segment with low CS and low CL. Then, this paper utilized Correspondence Analysis to describe the characteristics of segments and utilized Discriminant Analysis to anticipate which segment a certain customer belongs to.In Chapter 5, this paper summarizes the conclusion of this research, the significance of this research and contribution to academia, and the shortcoming of this research.