تحلیل داده‌محور رضایت مشتری در صنعت هتلداری: بهره‌گیری از یادگیری ماشین برای شناسایی محرک‌های کلیدی

نوع مقاله : مقاله پژوهشی

نویسنده

گروه مدیریت - دانشکده علوم انسانی - دانشگاه علم و هنر - یزد - ایران

چکیده
این پژوهش به تحلیل رضایت مشتری در صنعت هتلداری با استفاده از نظرات آنلاین تریپ‌ادوایزر می‌پردازد. هدف آن، کشف الگوهای پنهان در بازخوردهای مشتریان و ارائه راهکارهای مبتنی بر شواهد برای بهبود تجربه مهمانان است. این مطالعه با استفاده از تکنیک وب‌اسکرپینگ، داده‌های متنی و عددی را جمع‌آوری کرد. سپس با استفاده از مدل رگرسیون جنگل تصادفی، اهمیت ویژگی‌های مختلف را شناسایی نمود. تحلیل داده‌های ۲۴۱۱ نظر نشان داد که کیفیت اتاق و خدمات کارکنان مهم‌ترین عوامل تأثیرگذار بر رضایت مشتریان هستند. نتایج پژوهش همچنین نشان داد که عواملی مانند موقعیت مکانی و قیمت نیز تأثیر قابل‌توجهی دارند. این مطالعه یک چارچوب جامع برای درک محرک‌های رضایت مشتری ارائه می‌دهد و بر ضرورت تمرکز بر کیفیت اتاق و خدمات کارکنان برای بهینه‌سازی تجربه مهمان تأکید می‌کند. این رویکرد داده‌محور، به مدیران هتل‌ها کمک می‌کند تا منابع خود را به طور مؤثر تخصیص داده و مزیت رقابتی خود را تقویت کنند.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Data-Driven Customer Satisfaction Analysis in the Hospitality Industry: Leveraging Machine Learning to Identify Key Drivers

نویسنده English

Hamed Fallah Tafti
Management Department, Humanity Faculty, Science and Arts university, Yazd, Iran
چکیده English

This research analyzes customer satisfaction in the hospitality industry using online reviews from TripAdvisor. Its goal is to uncover hidden patterns in customer feedback and provide evidence-based solutions to improve the guest experience. The study collected textual and numerical data using a web scraping technique. Then, it used a random forest regression model to identify the importance of various features. An analysis of 2,411 reviews showed that room quality and staff service are the most significant factors influencing customer satisfaction. The research also revealed that factors such as location and price have a notable impact. This study provides a comprehensive framework for understanding the drivers of customer satisfaction and emphasizes the need to focus on room quality and staff service to optimize the guest experience. This data-driven approach helps hotel managers effectively allocate resources and strengthen their competitive advantage.

کلیدواژه‌ها English

Customer satisfaction
machine learning
online reviews
random forest
hospitality industry
1.      Adam, I. (2021). Negative tourist-to-tourist interactions, value destruction, satisfaction, and post consumption behavioral intention. Journal of Destination Marketing & Management, 20, Article 100557. https://doi.org/10.1016/j.jdmm.2021.100557
2.      Alsayat, A. (2023). Customer decision-making analysis based on big social data using machine learning: A case study of hotels in Mecca. Neural Computing and Applications, 35(6), 4701–4722. https://doi.org/10.1007/s00521-022-07992-x
3.      Amat-Lefort, N., Barravecchia, F., & Mastrogiacomo, L. (2023). Quality 4.0: Big data analytics to explore service quality attributes and their relation to user sentiment in Airbnb reviews. International Journal of Quality & Reliability Management, 40(4), 990–1008. https://doi.org/10.1108/IJQRM-01-2022-0024
4.      Ansari, A., & Asadi, A. (2016). Assessing tourist loyalty to destinations using a data mining approach: Domestic tourists in Isfahan. Tourism Management Studies Quarterly, 11(35), 85–106. [In Persian]
5.      Barzizza, E., Campbell, S., Ceccato, R., Dobosz, A., Haag, M., Martins, R., & Salmaso, L. (2024). A data-driven approach to understanding customer satisfaction. Journal of Machine Intelligence and Data Science, 5. https://doi.org/10.11159/jmids.2024.001
6.      Benaddi, L., Ouaddi, C., Jakimi, A., & Ouchao, B. (2024). A systematic review of chatbots: Classification, development, and their impact on tourism. IEEE Access, 12, 78799–78810. https://doi.org/10.1109/ACCESS.2024.3408108
7.      Bhuiyan, M., Rahman, M. A., Hoque, A. B., Ashrafuzzaman, M., & Rahman, A. (2023). Advanced analytics and machine learning for revenue optimization in the hospitality industry: A comprehensive review of frameworks. American Journal of Scholarly Research and Innovation, 2(2), 52–74. https://doi.org/10.63125/8xbkma40
8.      Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., & DeNisi, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524
9.      Cutler, S. Q., & Carmichael, B. A. (2010). The dimensions of the tourist experience. In M. Morgan, P. Lugosi, & J. R. B. Ritchie (Eds.), The tourism and leisure experience (pp. 3–26). Multilingual Matters. https://doi.org/10.21832/9781845411503-004
10.  Diwan, S. A. (2025). Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT. Alexandria Engineering Journal, 116, 535–547. https://doi.org/10.1016/j.aej.2024.11.051
11.  Elhabyb, K., Baina, A., Bellafkih, M., & Deifalla, A. F. (2024). Machine learning algorithms for predicting energy consumption in educational buildings. International Journal of Energy Research, 2024(1), Article 6812425. https://doi.org/10.1155/2024/6812425
12.  Fallah Tafti, H. (2020). Predicting the number of tourists based on Google Trends data records using machine learning methods: A case study of tourists in Yazd. Journal of Tourism and Development, 10(2), 67–79. [In Persian]
13.  Fouad, A. M., Salem, I. E., & Fathy, E. A. (2024). Generative AI insights in tourism and hospitality: A comprehensive review and strategic research roadmap. Tourism and Hospitality Research. Advance online publication. https://doi.org/10.1177/14673584241293125
14.  Garner, B., Thornton, C., Luo Pawluk, A., Mora Cortez, R., Johnston, W., & Ayala, C. (2022). Utilizing text-mining to explore consumer happiness within tourism destinations. Journal of Business Research, 139, 1366–1377. https://doi.org/10.1016/j.jbusres.2021.08.025
15.  Iloranta, R., & Komppula, R. (2022). Service providers' perspective on the luxury tourist experience as a product. Scandinavian Journal of Hospitality and Tourism, 22(1), 39–57. https://doi.org/10.1080/15022250.2021.1946845
16.  Lundberg, E. (2017). The importance of tourism impacts for different local resident groups: A case study of a Swedish seaside destination. Journal of Destination Marketing & Management, 6(1), 46–55. https://doi.org/10.1016/j.jdmm.2016.02.002
17.  Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
18.  Mehra, P. (2023). Unexpected surprise: Emotion analysis and aspect-based sentiment analysis (ABSA) of user generated comments to study behavioral intentions of tourists. Tourism Management Perspectives, 45, Article 101063. https://doi.org/10.1016/j.tmp.2022.101063
19.  Mirthipati, T. (2024). Enhancing airline customer satisfaction: A machine learning and causal analysis approach (arXiv preprint arXiv:2405.09076). https://doi.org/10.48550/arXiv.2405.09076
20.  Nam, S., Yoon, S., Raghavan, N., & Park, H. (2021). Identifying service opportunities based on outcome-driven innovation framework and deep learning: A case study of hotel service. Sustainability, 13(1), Article 391. https://doi.org/10.3390/su13010391
21.  Prayag, G., Hosany, S., & Odeh, K. (2013). The role of tourists' emotional experiences and satisfaction in understanding behavioral intentions. Journal of Destination Marketing & Management, 2(2), 118–127. https://doi.org/10.1016/j.jdmm.2013.05.001
22.  Ren, H., Pang, B., Bai, P., Zhao, G., Liu, S., Liu, Y., & Li, M. (2024). Flood susceptibility assessment with random sampling strategy in ensemble learning (RF and XGBoost). Remote Sensing, 16(2), Article 320. https://doi.org/10.3390/rs16020320
23.  Sahebi, A. G., Kordheydari, R., & Aghaei, M. (2022). A new approach in marketing research: Identifying the customer expected value through machine learning and big data analysis in the tourism industry. Asia-Pacific Journal of Management and Technology, 2(3). https://doi.org/10.46977/apjmt.2022v02i03.004
24.  Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/BJML/2024/007
25.  Sharma, R., Kumar, A., & Chuah, C. (2021). Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer. International Journal of Information Management Data Insights, 1(2), Article 100050. https://doi.org/10.1016/j.jjimei.2021.100050
26.  Shrestha, D., Wenan, T., Shrestha, D., Rajkarnikar, N., & Jeong, S.-R. (2024). Personalized tourist recommender system: A data-driven and machine-learning approach. Computation, 12(3), Article 59. https://doi.org/10.3390/computation12030059
27.  Sun, H., Kim, M., Kim, S., & Choi, L. (2025). A methodological exploration of generative artificial intelligence (AI) for efficient qualitative analysis on hotel guests' delightful experiences. International Journal of Hospitality Management, 124, Article 103974. https://doi.org/10.1016/j.ijhm.2024.103974
28.  Tan, J., Zhao, Z., Ma, W., Liu, Y., & Zhao, H. (2025). Price fairness perception on online food service platforms: A data-driven approach using fsQCA and machine learning. International Journal of Hospitality Management, 125, Article 104014. https://doi.org/10.1016/j.ijhm.2024.104014
29.  Van Leeuwen, R., & Koole, G. (2022). Data-driven market segmentation in hospitality using unsupervised machine learning. Machine Learning with Applications, 10, Article 100414. https://doi.org/10.1016/j.mlwa.2022.100414
30.  Veréb, V., & Azevedo, A. (2019). A quasi-experiment to map innovation perception and pinpoint innovation opportunities along the tourism experience journey. Journal of Hospitality and Tourism Management, 41, 208–218. https://doi.org/10.1016/j.jhtm.2019.10.003
31.  Wang, J., Zhao, Z., Liu, Y., & Guo, Y. (2021). Research on the role of influencing factors on hotel customer satisfaction based on BP neural network and text mining. Information, 12(3), Article 99. https://doi.org/10.3390/info12030099
32.  Wang, Y., Tang, T., Zhang, W., Sun, Z., & Xiong, Q. (2021). The Achilles tendon of dynamic pricing: The effect of consumers' fairness preferences on platform's dynamic pricing strategies. Journal of Internet and Digital Economics, 1(1), 15–35.
33.  Wu, W., & Riantama, D. (2022). Determining the factors affecting customer satisfaction using an extraction-based feature selection approach. PeerJ Computer Science, 8, Article e850. https://doi.org/10.7717/peerj-cs.850
34.  Wüst, K., & Bremser, K. (2025). Artificial intelligence in tourism through chatbot support in the booking process—An experimental investigation. Tourism and Hospitality, 6(1), Article 36. https://doi.org/10.3390/tourhosp6010036
35.  Zhang, X., & Kim, H.-S. (2021). Customer experience and satisfaction of Disneyland hotel through big data analysis of online customer reviews. Sustainability, 13(22), Article 12699. https://doi.org/10.3390/su132212699