# A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
###### Abstract  
In the airline industry, customer satisfaction occurs when passengers' expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social services. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively. airline servicescape; customer churn risk prediction; customer satisfaction prediction; data analysis; deep learning; machine learning +
Footnote †: journal: Medical  
examined the influence of physical and social servicescapes on airline customer propensities. However, while most of the studies that dealt with airline servicescapes so far have limited the human service to only the cabin crew, the ultimate purpose of this study is to connect airline customer propensities with brand loyalty by extending the human service to the viewpoint of passengers. Moreover, most existing studies only use traditional statistical methods, such as frequency, factor, and regression analyses, to analyze airline customer data. Therefore, there is a limitation in that the derived results are limited to the linear relationship, which makes it difficult to visually identify the magnitude of various factors affecting each other in airline customer data. To improve upon this problem, in this paper, we propose finding the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data using machine learning and deep learning models. Machine learning and deep learning-based data analysis enables us to discover hidden correlations and valuable insights from complex multi-dimensional data [17]. Research that utilizes machine learning and deep learning is now widespread in various fields. In particular, machine learning-based methods are actively used for customer analysis and marketing, such as forecasting customer purchase in the travel industry [18], predicting customer churn risk in the telecommunication and banking industries [19; 20], and improving sales and marketing efficiency [21]. Furthermore, machine learning models have been applied to the fields of transportation for predicting car accidents [22], agriculture industry for predicting demand and the price of products [23], healthcare and smart home for detecting falls [24], smart manufacturing applications for predicting tool wear [25], entertainment industry for correcting postures of piano players [26], and many others. A few studies applied machine learning to analyze airline customer propensities. For example, [PERSON] and [PERSON] [27] used a well-known machine learning model, decision tree, to determine the essential factors in the evaluation of customer satisfaction in British Airways. [PERSON] et al. [28] used a combination of k-Nearest Neighbor (kNN) and ensemble regression models to predict airline customer satisfaction. [PERSON] et al.