# A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
## 5 Conclusions and Discussion  
In this paper, we have proposed a deep learning approach to analyze airline customer propensities in South Korea. For this, we have first collected data from the users who have used airplanes at least once within the last five years. We then applied several preprocessing techniques to consolidate the collected data, clean invalid data, and select essential features. Lastly, we have evaluated the performance of various machine learning and deep learning models for predicting customer churn risk and satisfaction from airline customer data. Specifically, we selected well-known machine learning models, such as kNN and decision tree, ensemble learning models, such as RF and XGBoost, and deep learning models, such as CNN and CNN-LSTM. There are several implications of this study. From the theoretical perspective, unlike existing work, our work not only found various relationships between factors influencing airline customer propensities but also included the comparison of the performance of different machine learning and deep learning approaches. We demonstrated through experiments that deep learning models could predict the customer churn risk and satisfaction with accuracy values of 94% and 90%, respectively. Specifically, the deep learning model, CNN-LSTM, outperformed the machine learning models by approximately 11% and 7% on average (in terms of accuracy) in predicting customer churn risk and customer satisfaction, respectively. The experiment results proved our hypothesis that deep learning models are generally more accurate in predicting airline customer propensities compared with machine learning models. The high accuracy of deep learning models and their flexibility in handling a large amount of diverse data indicate that we can apply the proposed methodology to analyze customer prosperities in various fields. For example, we can use the proposed methodology in the banking sphere to analyze the customer satisfaction with the service provider, or we can also explore the customer retention strategies in the telecommunication industry and customer churn risks in various e-commerce applications. From a practical perspective, while most of these studies that dealt with airline services have limited human services to only the cabin crew, we have connected airline customer propensities with brand loyalty by extending the human service to the viewpoint of passengers. In order to understand the effect of the physical and social servicescapes of the airline customer propensities, we analyze the relationship with the aircraft cabin and passenger viewpoints. Specifically, we proved through experiments that considering social servicescapes factors in addition to physical servicescap factors can increase the accuracy of deep learning models by approximately 6% on average and machine learning models by 5% on average in predicting the customer churn risk and customer satisfaction. On the other hand, by analyzing this relationship, we can obtain meaningful insights related to the cabin crew and passengers and all factors that directly or indirectly affect service experience. From the data analysis in this paper, we could also observe several findings. For example, we observed that the survey participants indicated that they frequently take Korean Air and Asiana Airlines over other foreign airlines. This is explainable as these are the two largest airlines in South Korea. We could also observe that despite the higher prices, more survey participants preferred FSC over LCC. This is also explainable as most survey participants took medium and long-distance flights, whereas FSC would be more comfortable to travel due to plenty of legroom in the cabin and in-flight meal. On the other hand, the result of the correlation matrix also revealed that in-flight entertainment items, such as in-flight music, movies, games, and readings, are less correlated with customer churn risk and customer satisfaction than other factors.