Airline Customer Satisfaction Analysis Using Machine Leaning Methods

Öz

The airline industry, a major player in global transportation, operates in a fiercely competitive market where ensuring and maintaining customer satisfaction are of essential importance. This study focuses on Turkish Airlines, one of the leading airline companies in the world, aiming to assess its customer satisfaction relative to competitors by analyzing customer feedback on social media. The primary objective is to identify the key factors significantly impacting customer satisfaction in the service-oriented airline industry and propose strategies for improvement. Some well-known machine learning methods are employed to analyze customer data and feedback on social medias. The findings highlight that in-flight entertainment is a crucial factor influencing customer satisfaction for this airline. Notably, customers who highly rate in-flight entertainment tend to be in the 35-55 age range, prefer business class, and demonstrate loyalty. Consequently, it can be inferred that loyal customers within the 35-55 age range, opting for business class, generally express high satisfaction with airline services. This analysis not only underscores the importance of in-flight entertainment but also provides insights into the demographic characteristics and preferences of satisfied customers. Based on these findings, Turkish Airlines can strategically customize its services to better cater to the specific needs of its loyal clientele within the 35-55 age group, particularly those who prefer business class. This paves the way for targeted enhancements and initiatives that align with the identified customer segment, fostering enduring satisfaction in the competitive landscape of the airline industry.

Anahtar Kelimeler

• Customer satisfaction
• Airline industries
• Deep learning techniques
• Data analysis

Makine Öğrenme Yöntemleri Kullanarak Havayolu Müşteri Memnuniyeti Analizi

Öz

Havayolu endüstrisi, küresel taşımacılığın önemli bir aktörü olarak, müşteri memnuniyetini sağlamak ve sürdürmek açısından büyük öneme sahip olan son derece rekabetçi bir pazarda faaliyet göstermektedir. Bu çalışma, dünyanın önde gelen havayolu şirketlerinden biri olan Türk Hava Yolları'na odaklanarak, sosyal medyada paylaşılan müşteri geri bildirimlerini analiz ederek rakiplerine kıyasla müşteri memnuniyetini değerlendirmeyi amaçlamaktadır. Çalışmanın temel amacı, hizmet odaklı havayolu endüstrisinde müşteri memnuniyetini önemli ölçüde etkileyen temel faktörleri belirlemek ve iyileştirme stratejileri önermektir. Müşteri verilerini ve sosyal medya üzerindeki geri bildirimleri analiz etmek için bazı yaygın makine öğrenmesi yöntemleri kullanılmıştır. Bulgular, uçak içi eğlence sisteminin bu havayolu için müşteri memnuniyetini etkileyen kritik bir faktör olduğunu ortaya koymaktadır. Özellikle, uçak içi eğlence sistemine yüksek puan veren müşterilerin genellikle 35-55 yaş aralığında olduğu, business class tercih ettikleri ve havayoluna sadakat gösterdikleri tespit edilmiştir. Bu doğrultuda, 35-55 yaş aralığında bulunan ve business class’ı tercih eden sadık müşterilerin genel olarak havayolu hizmetlerinden yüksek memnuniyet duyduğu sonucuna varılabilir. Bu analiz, uçak içi eğlence sisteminin önemini vurgulamanın yanı sıra, memnun müşterilerin demografik özellikleri ve tercihlerine dair önemli iç görüler sunmaktadır. Elde edilen bulgular doğrultusunda, Türk Hava Yolları, özellikle 35-55 yaş grubundaki ve business class’ı tercih eden sadık müşterilerine yönelik hizmetlerini stratejik olarak özelleştirebilir. Bu da belirlenen müşteri segmentine uygun hedeflenmiş iyileştirmeler ve girişimlerin yolunu açarak, havayolu endüstrisinin rekabetçi ortamında kalıcı müşteri memnuniyetinin sağlanmasına katkıda bulunacaktır.

Anahtar Kelimeler

• Müşteri memnuniyeti
• Havayolu endüstrileri
• Derin öğrenme teknikleri
• Veri analizi

Kaynakça

1. Aktepe, A., Ersöz, S., & Toklu, B. (2015). Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling. Computers & industrial engineering, 86, 95-106. https://doi.org/10.1016/j.cie.2014.09.031
2. Al-Mashraie, M., Chung, S. H., & Jeon, H. W. (2020). Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach. Computers & Industrial Engineering, 144, 106476. https://doi.org/10.1016/j.cie.2020.106476
3. Bockhorst, J., Yu, S., Polania, L., & Fung, G. (2017). Predicting self-reported customer satisfaction of interactions with a corporate call center. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, 179-190. 10.1007/978-3-319-71273-4_15
4. Chow, C. K. W. (2014). Customer satisfaction and service quality in the Chinese airline industry. Journal of air transport management, 35, 102-107. https://doi.org/10.1016/j.jairtraman.2013.11.013
5. Gao, K., Yang, Y., & Qu, X. (2021). Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction. Transportation Research Part D: Transport and Environment, 97, 102957. https://doi.org/10.1016/j.trd.2021.102957
6. García, V., Florencia-Juárez, J. P., Sánchez-Solís., G. Rivera-Zarate., R. Contreras-Masse. (2019). Predicting airline customer satisfaction using k-nn ensemble regression models. Instituto de Ingeniería y Tecnología, 1870- 4069. :10.13053/rcs-148-6-15
7. Gou, J., Qiu, W., Yi, Z., Shen, X., Zhan, Y., & Ou, W. (2019). Locality constrained representation-based K-nearest neighbor classification. Knowledge-Based Systems, 167, 38-52. 10.1016/j.knosys.2019.01.016
8. Gures, N., Arslan, S., & Tun, S. Y. (2014). Customer expectation, satisfaction and loyalty relationship in Turkish airline industry. International Journal of Marketing Studies, 6(1), 66. 10.5539/ijms.v6n1p66

9. Hayadi, B. H., Kim, J. M., Hulliyah, K., & Sukmana, H. T. (2021). Predicting Airline Passenger Satisfaction with Classification Algorithms. International Journal of Informatics and Information Systems, 4(1), 82-94. https://doi.org/10.47738/ijiis.v4i1.80
10. Hong, A. C. Y., Khaw, K. W., Chew, X., & Yeong, W. C. (2023). Prediction of US airline passenger satisfaction using machine learning algorithms. Data Analytics and Applied Mathematics (DAAM), 7-22. https://doi.org/10.15282/daam.v4i1.9071
11. Hussain, R., Al Nasser, A., & Hussain, Y. K. (2015). Service quality and customer satisfaction of a UAE-based airline: An empirical investigation. Journal of Air Transport Management, 42, 167-175. https://doi.org/10.1016/j.jairtraman.2014.10.001
12. Hwang, S., Kim, J., Park, E., & Kwon, S. J. (2020). Who will be your next customer: A machine learning approach to customer return visits in airline services. Journal of Business Research, 121, 121-126.
13. Kaggle.(2020). Airline Passenger Satisfaction Dataset. Available at: https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction
14. Krishnan, C. G., Robinson, Y. H., & Chilamkurti, N. (2020). Machine learning techniques for speech recognition using the magnitude. Journal of Multimedia Information System, 7(1), 33- 40. https://doi.org/10.33851/JMIS.2020.7.1.33
15. Kumar, S., & Zymbler, M. (2019). A machine learning approach to analyze customer satisfaction from airline tweets. Journal of Big Data, 6(1), 1-16. :10.1186/s40537-019-0224-1
16. Leong, L. Y., Hew, T. S., Lee, V. H., & Ooi, K. B. (2015). An SEM–artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline. Expert systems with applications, 42(19), 6620-6634. 10.1016/j.eswa.2015.04.043
17. Magsi, H., Sodhro, A. H., Al-Rakhami, M. S., Zahid, N., Pirbhulal, S., & Wang, L. (2021). A novel adaptive battery-aware algorithm for data transmission in IoT-based healthcare applications. Electronics, 10(4), 367. https://doi.org/10.3390/electronics10040367
18. Nourbakhsh, A., & Rezaei Chelkasari, M. (2023). Airline passenger’s sentiment analysis for improving the quality of airline services by using a deep learning approach. International Journal of Applied Operational Research-An Open Access Journal, 11(2), 77-97. https://doi.org/10.71885/ijorlu-2023-1-638
19. Nurdina, A., & Puspita, A. B. I. (2023). Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis. Journal of Information System Exploration and Research, 1(2). https://doi.org/10.52465/joiser.v1i2.167
20. Park, E., Jang, Y., Kim, J., Jeong, N. J., Bae, K., & Del Pobil, A. P. (2019). Determinants of customer satisfaction with airline services: An analysis of customer feedback big data. Journal of Retailing and Consumer Services, 51, 186-190. https://doi.org/10.1016/j.jretconser.2019.06.009
21. Park, S. H., Kim, M. Y., Kim, Y. J., & Park, Y. H. (2022). A deep learning approach to analyze airline customer propensities: the case of South Korea. Applied Sciences, 12(4), 1916. https://doi.org/10.3390/app12041916
22. Suprapto, D. S., & Oetama, R. (2023). Analysis of Airline Passenger Satisfaction Using Decision Tree and Naïve Bayes Algorithms. Jurnal Informatika Ekonomi Bisnis, 1493-1500. https://doi.org/10.37034/infeb.v5i4.728
23. Taliah, A. R. B., & Zervopoulos, P. D. (2023). Using a Bayesian meta-frontier approach to evaluate airline performance: The nexus between airline efficiency and customer satisfaction. Heliyon, 9(10). 10.1016/j.heliyon.2023.e20391
24. Tayaba, M., Ayon, E. H., Mia, M. T., Sarkar, M., Ray, R. K., Chowdhury, M. S., ... & Puja, A. R. (2023). Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining. Journal of Computer Science and Technology Studies, 5(4), 194-202. https://doi.org/10.32996/jcsts.2023.5.4.20
25. Thakur, N., & Han, C. Y. (2021). A study of fall detection in assisted living: Identifying and improving the optimal machine learning method. Journal of sensor and actuator networks, 10(3), 39. https://doi.org/10.3390/jsan10030039
26. Ouf, S. (2023). An optimized deep learning approach for improving airline services. Comput. Mater. Contin, 75(1), 1213-1233. https://doi.org/10.32604/cmc.2023.034399
27. Xu, Z., Zhu, G., Metawa, N., & Zhou, Q. (2022). Machine learning based customer meta-combination brand equity analysis for marketing behavior evaluation. Information Processing & Management, 59(1), 102800. 10.1016/j.ipm.2021.102800