Applications of Data Mining on Airlines Customer Clustering

• Main Author: 李怡
• Other Authors: 洪叔民
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/uvxmm5

碩士 === 國立政治大學 === 企業管理研究所(MBA學位學程) === 106 === The Airline industry is in a dynamic change and was influenced by multiple factors such as the market environment, the interest of traveling, and regional developments. Airline companies adopt these rapid changes by reconsidering and rescheduling its routes and flights, in order to avoid possible losses by accidentally missing the market trends or forecast. The fastest way to access these possible information is by collecting its members’ information, such as flight records, specific demands and personal preferences, along with their periodically or annually changes. This research was conducted by using the Data Mining method. It collected airline companies’ members’ information within a specific period and a specific route that operates between Taipei and Shanghai, with segmentations according to their flying records. This research also considered the demographic variables and flight frequencies in the analysis to provide strong evidences with a brief understanding that explains the differences between segments. The analyzed members were divided into five segments, or groups including general passengers, mid-class passengers, travelers, frequent flyers, and high-end passengers. With these segmentations, it provides a better understanding of the behaviors between different members of ages, gender, membership tiers, and nationalities. By cross analyzing the changes in different periods and years, this information can be an essential factor that airline companies’ can consider while making important decisions.