政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/116175

政大典藏 > College of Commerce > MBA Program > Theses > Item 140.119/116175

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/116175

Title:	資料探勘應用於航空公司之顧客分群研究 Applications of Data Mining on Airlines Customer Clustering
Authors:	李怡
Contributors:	洪叔民李怡
Keywords:	航空業集群分析顧客分群資料探勘 The airline industry Cluster analysis Customer segmentations Data Mining
Date:	2018
Issue Date:	2018-03-02 12:06:34 (UTC+8)
Abstract:	航空業隨市場環境、產業變遷、旅遊觀光、區域發展等眾多複雜因素影響，航空公司對於航線的佈置、航班數量配置，都需快速因應市場環境之變化，才能避免因錯估市場形勢導致的鉅額損失。而最快速的市場信息可透過航空公司自身擁有的會員資料、搭乘記錄等去分析其會員之需求特性及行為偏好，以及跨年、跨期間的行為變化。本研究擬應用資料採礦技術於航空業旅客資料，目標鎖定於特定期間內搭乘台灣－上海航線之航空會員，並透過分群技術將目標會員以搭乘行為進行分群，並透過人口變相描述統計、搭乘數據分析等，強化對分群群體之瞭解與解釋。透過分群，可瞭解代表不同搭乘行為之群各自之組成人數多寡、年齡組成、性別組成、會員卡籍組成、國籍組成，並可透過期間內跨年數據分析觀察各群組成員之連續時間行為變化，透過資料採礦可把航空公司原本之會員資料及搭乘記錄轉換為可做商業決策參考之資訊。本研究按照搭乘行為將目標會員共分成五群，五群分別描述如下：一般大眾客、中端大眾客、一般常旅客、高頻常旅客、高端消費客，代表上海航線會員之旅客類型，並透過各項數據數據加深對各群旅客之認識以及行為解釋度。 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.
Reference:	1. 中華航空（民106），華夏哩程酬賓計劃。民106年12月5日取自http://www.china-airlines.com 2. 天合聯盟(民106)，天合聯盟航空公司聯盟介紹。民106年12月20日取自：https://www.skyteam.com/zh-TW/ 3. 交通部民用航空局(民106)，民航統計年報。民106年12月18日取自:http://www.caa.gov.tw/BIG5/content/index.asp?sno=870 4. 吳旭智與賴叔貞（譯）（民90），資料採礦理論與實務（原作者：Michael J.A. Berry, Gordon Linoff）。台北市：維科。 5. 李昭平（民100），兩岸直航對台灣航空業營運之影響以中華航空公司為例（碩士論文），國立政治大學。 6. 吳毓娟（民93），航空業進行分析型顧客關係管理之研究（碩士論文），中原大學。 7. 長榮航空(民106)，各卡籍優惠待遇。民106年12月13日取自：http://www.evaair.com/zh-tw/infinity-mileagelands/membership-benefits/tiers-and-privileges/ 8. 星空聯盟(民106)，星空聯盟成員航空公司。民106年12月20日取自：http://www.staralliance.com/zh_TW/member-airlines 9. 施雅月與賴錦慧（譯）（民97），資料探勘（原作者：Pang-Ning Tan, Michael Steinbach, Vipin Kumar）。台北市：臺灣培生教育。 10. 張云濤與龔玲（民96），資料探勘原理與技術 =Data mining、AI、Algorithm。台北市：五南。 11. 盧世銘（民93），資料採礦技術之商業應用研究 : 以航空公司會員系統為例（碩士論文），國立政治大學。英文文獻 1. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th ed. Uppersaddle River: Pearson Prentice Hall. 2. Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons. 3. Michalski, R. S. (1980). Pattern recognition as rule-guided inductive inference. IEEE Transactions on Pattern Analysis and Machine Intelligence. 4. Steinbach, M., Tan, P. N., Kumar, V., Klooster, S., & Potter, C. (2003, August). Discovery of climate indices using clustering. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 5. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Description:	碩士國立政治大學企業管理研究所(MBA學位學程) 105363042
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0105363042
Data Type:	thesis
Appears in Collections:	[MBA Program] Theses

Files in This Item:

There are no files associated with this item.

社群 sharing

Loading...