Analysis of the passengers’ loyalty and satisfaction of Airasia passengers using classification / Ee Jian Pei ... [et al.]

Ee, Jian Pei and Chong, Pui Lin and Mohd Radzuan, Nabilah Filzah (2021) Analysis of the passengers’ loyalty and satisfaction of Airasia passengers using classification / Ee Jian Pei ... [et al.]. In: e-Proceedings of the 5th International Conference on Computing, Mathematics and Statistics (iCMS 2021), 4-5 August 2021. (Submitted)

Abstract

“Business rules” of airline is to earn profits by providing air transportation services and flights to the travel passenger. Passengers purchase airline tickets base on their different requirement to choose a satisfied flight from different airlines, and become a loyal passenger to the airline due to satisfaction. The airline can establish a long-term win-win relationship with the loyal passengers which is long term purchase company’s flights as the passenger can have a satisfied flight, while the airline can earn the long-term profit. This research proposes the dashboard system in finding the passengers' purchase behavior of loyalty and satisfaction from the hidden data in order to make a better business strategy in stand out from other competitors, and visualize the report. The classification method will be used included random forest, logistic regression and lightgbm. The result will identify the various possibilities of information, and contribute prediction of passenger loyalty and satisfaction.

Metadata

Item Type: Conference or Workshop Item (Paper)
Creators:
Creators
Email / ID Num.
Ee, Jian Pei
i17012981@student.newinti.edu.my
Chong, Pui Lin
puilin.chong@newinti.edu.my
Mohd Radzuan, Nabilah Filzah
nabilahfilzah@ump.edu.my
Subjects: H Social Sciences > HF Commerce > Consumer satisfaction
H Social Sciences > HF Commerce > Customer services. Customer relations
Divisions: Universiti Teknologi MARA, Kedah > Sg Petani Campus
Event Title: e-Proceedings of the 5th International Conference on Computing, Mathematics and Statistics (iCMS 2021)
Event Dates: 4-5 August 2021
Page Range: pp. 291-298
Keywords: Analytic, classification, random forest, logistic, airline
Date: 2021
URI: https://ir.uitm.edu.my/id/eprint/56224
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