Airline Customers Strategic Decision Analysis and Formulation
DOI:
https://doi.org/10.54691/bcpbm.v24i.1430Keywords:
Airline, Customer values, Decision making, Regression, RFM, K-means cluster.Abstract
In recent years, estimating customer value for airline companies has attracted much scholarly attention. Many researchers have tried to classify customer rankings based on different commercial models [1-2] (e.g., Huang & Liu, 2016; Liu & Du, 2010). However, few studies have quantified specific value rankings and designed tailored strategies. This project aims to fractionate customer value rankings and make corresponding strategies depending on their features. The whole project can be divided into two parts. First, customer values are quantified and six value rankings (Rank A to F) are developed by applying factor analysis method and regression models. Second, researchers use K-means algorithm to find out customer features for each value ranking. Customers are classified into two or three feature groups within each value ranking. Budgets were allocated based on value rankings and tailored strategies were made according to feature groups. The results of this project can assist the airline company in allocating resources and attracting customers.
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