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 servicescapes. 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.

An Optimized Deep Learning Approach for Improving Airline Services

“…Park et al [40] applied machine learning and deep learning techniques to discover the relationship between different features affecting passengers' churn risk and satisfaction. They applied these techniques to survey data gathered from passengers (340 Korean adults) who have used Korean airplanes.…”

Clasificación de quejas de los clientes empleando procesamiento de lenguaje natural: revisión sistemática de la literatura

“…Se entiende por satisfacción del cliente al estado de agrado o decepción en comparación entre la percepción y la expectativa por un producto o servicio [14]; esta métrica se obtiene de variados y complejos factores tales como precio, servicio, calidad [15]. La satisfacción del cliente puede traer la lealtad del cliente beneficiando a la empresa con recompras o recomendaciones además de reducir los costos de atraer nuevos clientes [16]; por el contrario un cliente insatisfecho podría no regresar o sentirse motivado en difundir comentarios negativos dañado la imagen de la empresa [17].…”

Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

“…Particularly well-explored are the contractual business settings in the B2C domain, such as those commonly encountered in the telecommunications [19][20][21], banking [22,23], and insurance [24,25] sectors, where customers at risk of terminating or not renewing their contracts are identified and targeted with retention campaigns in efforts to persuade them otherwise. Non-contractual settings have also often been studied, where efforts have been put towards predicting which retail customers are least likely to make a purchase in the future [26,27], which users are at most risk to stop playing mobile games [6], or which passengers are not planning to use a particular airline for their future flights [28]. The B2B domain, on the other hand, has received less attention so far.…”

“…In binary classification problems, the performance of a model can be evaluated by using different metrics, the suitability of which will vary depending on the model's ultimate purpose and the potential cost of misclassification. Even though accuracy, precision, recall, and F1 measures are often used with binary classifiers [19,26,28], given the purpose for which we devise our models for (i.e., churn prediction), we opt for measures that are well established within the domain and which capture the overall model performance and allow for their assessment from a decision-making standpoint: AUC and TDL [18,42].…”