OTELLERE DAİR MÜŞTERİ TATMİNİNİN TAHMİNLENMESİ: MAKİNE ÖĞRENMESİ TEKNİKLERİ İLE BİR UYGULAMA

Year 2024, Volume: 17 Issue: 3, 673 - 702, 26.09.2024

Atilla Suncak , Fatma Selin Sak , Hilal Öztay Çağan

Abstract

Yapay zekâ algoritmalarının pek çok alanda kullanımının yaygınlaşmasıyla birlikte, pazarlama çalışmalarındaki kullanımı da gündeme gelmeye başlamıştır. Bu çalışmanın amacı bir yapay zekâ uygulaması olan makine öğrenmesi tekniklerinin pazarlamada kullanımına dair bir örnek sunmaktır. Bu amaçla müşteri tatmininin makine öğrenme teknikleri ile tahminlenmesinin hangi başarı düzeyi ile mümkün olduğu araştırılmıştır. Araştırmada, dünyadaki en büyük seyahat platformu olma özelliğini taşıyan Tripadvisor web sitesinden elde edilen veriler kullanılmıştır. 993 otele ait özellikler ve bu otellerin tatmin skorları kayıt altına alınmıştır. İşletme olanak sayısı, odalardaki özellik sayısı, oda türü sayısı, yer skoru, temizlik skoru, değer skoru, yorum sayısı ile tatmin arasında orta düzeyde ilişki olduğu, hizmet skoru ile de yüksek düzeyde ilişki olduğu sonuçları elde edilmiştir. % 70 eğitim verisi, % 30 test verisi olacak şekilde ikiye ayrılan veri seti ile önce modellerin eğitilmesi sağlanmış, sonra da model başarıları ortaya konmuştur. İlgili değişkenlere dayalı olarak en düşük % 71, en yüksek % 81 başarı oranıyla otellere dair tahminlemenin yapılmasının mümkün olduğu sonuçları elde edilmiştir.

Keywords

Müşteri tatmini, Müşteri değerlendirmeleri, Yapay zekâ, Makine öğrenmesi

Ethical Statement

Bu çalışmada etik kurul onayı gerekmemektedir.

PREDICTING CUSTOMER SATISFACTION REGARDING HOTELS: AN APPLICATION USING MACHINE LEARNING TECHNIQUES

Abstract

With the increasing use of artificial intelligence algorithms in many areas, applying AI in marketing studies has also begun to emerge. The aim of this study is to present an example of the use of machine learning techniques, an artificial intelligence application, in marketing. For that purpose, the accuracy rate of predicting customer satisfaction with machine learning techniques was investigated. In this research, data obtained from the Tripadvisor website, which is the largest travel platform in the world, was used. The features of 993 hotels and the satisfaction scores of these hotels were recorded by the researchers. medium level of correlation was observed with the number of amenities, number of room features, number of room types, location score, cleanliness score, value score, and number of reviews. A high level of correlation was found with the service score. The dataset was divided into 70% training data and 30% test data. Models were trained with the training data and their performance was tested with the test data. Using these variables, it was found that it is possible to predict satisfaction with accuracy rates ranging from a minimum of 71% to a maximum of 81% through various machine learning methods.

Keywords

Customer satisfaction, Customer reviews, Artificial intelligence, Machine learning

References

• Acar, A., & Uğur, İ. (2021). Uluslararası zincir otellere yönelik Tripadvisor yorumlarının duygu analizi yöntemi ile değerlendirilmesi: Ankara örneği. Türk Turizm Araştırmaları Dergisi, 5(3), 1803-1814. doi:https://doi.org/10.26677/TR1010.2021.820
• Ahmetoğlu, H., & Daş, R. (2020). Türkçe Otel Yorumlarıyla Eğitilen Kelime Vektörü Modellerinin Duygu Analizi ile İncelenmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 24(2), 455-463. doi:https://doi.org/10.19113/sdufenbed.645579
• Ak, S., & Altunöz Sürücü. (2018). Termal otel işletmelerinin çevrimiçi tüketici değerlendirmeleri bağlamında incelenmesi: Tripadvisor örneği. 19. Ulusal Turizm Kongresi. Afyonkarahisar.
• Aktaş, M., & Çavuşoğlu, S. (2023). Pazarlamada yapay zekâ. E. S. Yılmaz içinde, Dijitalleşme ve pazarlama araştırmaları. Gaziantep: Özgür Yayınları.
• Akyol, M. (2021). Clustering Hotels and Analyzing the Importance of Their Features by Machine Learning Techniques. Journal of Computer Science and Technologies, 2(1), 16-23.
• Anderson, C. K. (2012). The impact of social media on lodging performance. Cornell Hospitality Report, 12(15), 6-12.
• Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77(1), 236-246. doi:https://doi.org/10.1016/j.eswa.2017.02.002
• Aria, M., Cuccurullo, C., & Gnasso, A. (2021). A comparison among interpretative proposals for Random Forests. Machine Learning with Applications, 6, 1-8. doi:https://doi.org/10.1016/j.mlwa.2021.100094
• Arıca, R., & Çorbacı, A. (2019). Turizm sektöründe müşterilerin bilgi üretici rolü: Adıyaman'daki turistik çekiciliklere yönelik Tripadvisor sitesinde yer alan yorumlar üzerine bir araştırma. Seyahat ve Otel İşletmeciliği Dergisi, 16(3), 437-455. doi:https://doi.org/10.24010/soid.655292
• Banerjee, S., & Chua, A. Y. (2016). In search of patterns among travellers' hotel ratings in TripAdvisor. Tourism management(53), 125-131. doi:https://doi.org/10.1016/j.tourman.2015.09.020
• Bayuk, M. N., & Demir, B. N. (2019). Endüstri 4.0 Kapsaminda Yapay Zekâ ve Pazarlamanin Geleceği. International Journal Of Social, Humanities and Administrative Sciences, 5(19), 781-799. doi: http://dx.doi.org/10.31589/JOSHAS.163
• Berezina, K., Bilgihan, A., Çobanoğlu, C., & Okumuş, F. (2016). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24. doi:https://doi.org/10.1080/19368623.2015.983631
• Bi, J.-W., Liu, Y., Fan, Z.-P., & Zhang, J. (2020). Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry. Tourism Management, 77(104006), https://doi.org/10.1016/j.tourman.2019.104006.
• Bouchriha, Z., Farid, S., & Ouiddad, S. (2023). Enhancing value co-creation behaviors through customer engagement In the moroccan hotel context: how does it influence customer satisfaction and brand image? Journal of Quality Assurance in Hospitality & Tourism , 1-26. https://doi.org/10.1080/1528008X.2023.2165595.
• Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. doi:https://doi.org/10.1023/A:1010933404324
• Breiman, L. (2017). Classification and regression tree. New York: Routledge.
• Büyüköztürk, Ş. (2015). Sosyal bilimler için veri analizi el kitabı (21. b.). Ankara: Pegem Akademi.
• Cabral, A., & Marques, J. (2023). How innovation can influence customer satisfaction– case study of the Saccharum hotel in Madeira. International Journal of Innovation Science, 15(1), 80-93. https://doi.org/10.1108/IJIS-03-2021-0061.
• Chan, I. C., Lam, L. W., Chow, C. W., Fong, L. H., & Law, R. (2017, Ekim). The effect of online reviews on hotel booking intention: The role of reader-reviewer similarity. International Journal of Hospitality Management, 66, 54-65. doi:https://doi.org/10.1016/j.ijhm.2017.06.007
• Chen, S., Ngai, E. W., Ku, Y., Xu, Z., Gou, X., & Zhang, C. (2023, July). Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction. Decision Support Systems, 170, 1-14. doi:https://doi.org/10.1016/j.dss.2023.113959
• Colledani, D., Anselmi, P., & Robusto, E. (2023). Machine learning-decision tree classifiers in psychiatric assessment: An application to the diagnosis of major depressive disorder. Psychiatry Research, 322, 1-7. doi:https://doi.org/10.1016/j.psychres.2023.115127
• Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016). Efficient KNN classification algorithm for big data. Neurocomputing, 195, 143-148. doi:https://doi.org/10.1016/j.neucom.2015.08.112
• Ensor, K. B., & Glynn, P. W. (1997). Stochastic optimization via grid search. Lectures in Applied Mathematics-American Mathematical Society, 33, 89-100.
• Ercan, F. (2020). Turizm pazarlamasında yapay zekâ teknolojilerinin kullanımı ve uygulama örnekleri. AHBVÜ Turizm Fakültesi Dergisi, 23(2), 394-410. doi:10.34189/tfd.23.02.009
• Erdem, B. (2020). Çevrimiçi yorumların davranışsal niyete etkisi: Google haritaları örneği. International Social Mentality and Researcher Thinkers Journal, 6(30), 357-364. doi:http://dx.doi.org/10.31576/smryj.477
• Ergüt, Ö. (2019). Otel yorumlarının metin madenciliği teknikleri ile incelenmesi. International Congress Of Management Economy And Policy 2019 Autumn Proceedıngs Book, (s. 103-114). İstanbul.
• Fayed, H. A., & Atiya, A. F. (2021). Decision boundary clustering for efficient local SVM. Applied Soft Computing Journal, 110. doi:https://doi.org/10.1016/j.asoc.2021.107628
• Firman, A., & Ilyas, G. (2021). The effect of kaizen strategy on customer satisfaction: empirical study on budget hotels in Makassar City. Point of View Research Management, 2(1), 1-9.
• Garla, V., Taylor, C., & Brandt, C. (2013). Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management. Journal of biomedical informatics, 46(5), 869-875. doi:https://doi.org/10.1016/j.jbi.2013.06.014
• Gretzel, U., & Yoo, K.-H. (2008). Use and Impact of Online Travel Reviews. Information and communication technologies in tourism 2008 (s. 35-46). Vienna: Springer. doi:DOI: 10.1007/978-3-211-77280-5_4 · Source: DBLP
• Han, S., & Anderson, C. (2022). The dynamic customer engagement behaviors in the customer satisfaction survey. Decision Support Systems, 154, https://doi.org/10.1016/j.dss.2021.113708.
• Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 35-52. doi:https://doi.org/10.1002/dir.10073
• Hyde, K. F. (2008). Information processing and touring planning theory. Annals of Tourism Research, 35(3), 712-731. doi:https://doi.org/10.1016/j.annals.2008.05.001
• İnan, H. E. (2019). Otel Yorumlarının Sınıflandırılmasında Makine Öğrenmesi Algoritmalarının Karşılaştırılması ve Eskişehir Otellerine Yönelik Yorumların Duygu Analizi. 20.Ulusal Turizm Kongresi Bildiriler Kitabı (s. 1106-1111). Eskişehir: Anadolu Üniversitesi Basımevi.
• İstikbal, D. (2022). Türkiye'nin turizm ekonomisi: Kazanımlar ve beklentiler. İstanbul: Seta Yayınları.
• Jacksen, Chandra, T., & Putra, R. (2021). Service quality and brand image on customer satisfaction and customer loyalty at Pesonna Hotel Pekanbaru. Journal of Applied Business and Technology (JABT), 2(2), 142-153.
• Jin, R., & Chen, K. (2021). Impact of value cocreation on customer satisfaction and loyalty of online car-hailing services. Journal of Theoretical and Applied Electronic Commerce Research, 16(3), 432-444. https://doi.org/10.3390/jtaer16030027.
• Kafa, N., & Demircioğlu, E. (2022). Gökçeada’daki konaklama işletmelerine yönelik çevrimiçi müşteri değerlendirmelerinin incelenmesi: Tripadvisor örneği. Journal of Recreation and Tourism Research /JRTR, 9(2), 52-69. doi:DOI: https://doi.org/10.5281/zenodo.6774476
• Kaludewa, N., & Abeywickrama, K. (2023). Customer Expectations and Satisfaction: An Analysis of Negative Customer Reviews on Tripadvisor for Classified Hotels in Western Province . 2nd International Research Symposium on Management (s. 466-469). Faculty of Management Studies, Rajarata University of Sri Lanka.
• Khairawati, S. (2020). Effect of customer loyalty program on customer satisfaction and its impact on customer loyalty. International Journal of Research in Business & Social Science, 9(1), 15-23. https://doi.org/10.20525/ijrbs.v9i1.603.
• Khan, R., Salamzadeh, Y., Iqbal, Q., & Yang, S. (2022). The impact of customer relationship management and company reputation on customer loyalty: the mediating role of customer satisfaction. Journal of Relationship Marketing, 21(1), 1-26. https://doi.org/10.1080/15332667.2020.1840904.
• Kim, C., & Chung, K. (2020). Measuring customer satisfaction and hotel efficiency analysis: an approach based on data envelopment analysis. Cornell Hospitality Quarterly, 63(2), 1-10. https://doi.org/10.1177/1938965520944914.
• Kim, Y.-J., & Kim , H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), https://doi.org/10.3390/su14020848.
• Liang, Y., & Zhao, P. (2019). A machine learning analysis based on big data for eagle ford shale formation. SPE Annual Technical Conference and Exhibition. Calgary, Alberta. doi:https://doi.org/10.2118/196158-MS
• Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computingpower to human insights. International Journal of Research in Marketing, 37(3), 481-504. doi:https://doi.org/10.1016/j.ijresmar.2020.04.005
• Michalski, R. S., Carbonel, J. G., & Mitchell, T. M. (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.
• Narangajavana, Y., Fiol, L. J., Tena, M. Á., Artola, R. M., & García, J. S. (2017). The influence of social media in creating expectations. An empirical study for a tourist destination. Annals of Tourism Research(65), 60-70. doi:https://doi.org/10.1016/j.annals.2017.05.002
• Nguyen, D., Pham, V., Tran, D., & Pham, D. (2020). Impact of service quality, customer satisfaction and switching costs on customer loyalty. Journal of Asian Finance, Economics and Business, 7(8), 395-405. https://doi.org.10.5267/j.msl.2019.12.034.
• Nguyen, N., Nguyen, T.-H., Nguyen, Y.-N., & Doan, D. (2023). Machine learning-based model for customer emotion detection in hotel booking services. Journal of Hospitality and Tourism Insights. doi:https://doi.org/10.1108/JHTI-03-2023-0166
• Nguyen, V.-H., & Ho, T. (2023). Analysing online customer experience in hotel sector using dynamictopic modelling and net promoter score. Journal of Hospitality and Tourism Technology, 14(2), 258-277. https://doi.org/10.1108/JHTT-04-2021-0116.
• Nunkoo, R., Teeroovengadum, V., Ringle, C., & Sunnassee, V. (2020). Service quality and customer satisfaction: The moderating effects of hotel star rating. International Journal of Hospitality Management, 91, https://doi.org/10.1016/j.ijhm.2019.102414.
• Pereira, L. N., & Cerqueira, V. (2022). Forecasting hotel demand for revenue management using machine learning regression methods. Current Issues in Tourism, 25(17), 2733-2750. doi:10.1080/13683500.2021.1999397
• Pontes, F. J., Amorim, G. F., Balestrassi, P. P., Paiva, A. P., & Ferreira, J. R. (2016). Design of experiments and focused grid search for neural network parameter optimization. Neurocomputing, 186, 22-34. doi:https://doi.org/10.1016/j.neucom.2015.12.061
• Prasath, S. V., Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. (2019). Effects of distance measure choice on KNN classifier performance - A review. ArXiv.
• Purba, J., Juliana, J., Budiono, S., Purwanto, A., Pramono, R., & Djakasaputra, A. (2022). The relationship between hotel service quality and customer satisfaction: an empirical study of spa hotels in Indonesia. International Journal of Entrepreneurship, 26(1), 1-11.
• Rahmah, N., Kara, M., Bakry, M., & Muin, R. (2021). Effect of service quality on customer loyalty with customer satisfaction as an intervening variable in Shariah Hotel (Study at Pesonna Hotel in Makassar, South Sulawesi). International Journal of Multicultural and Multireligious Understanding, http://dx.doi.org/10.18415/ijmmu.v8i4.2479.
• Sao Mai, D., & Tri Cuong, D. (2021). Relationships between service quality, brand image, customer satisfaction, and customer loyalty. The Journal of Asian Finance, Economics and Business, 8(3), 585-593. https://doi.org/10.13106/jafeb.2021.vol8.no3.0585.
• Sharma, D., & Singh, B. (2021). Understanding the relationship between customer satisfaction, customer engagement and repeat purchase behaviour. Vision: The Journal of Business Perspective, 27(4), 449-457. https://doi.org/10.1177/0972262921992593.
• Shu, Z., Torralba, M., Carrasco, R., & López, M. (2023). Assessing customer satisfaction of London luxury hotels with the AHP method and the SERVPERF scale: a case study of customer reviews on TripAdvisor. Procedia Computer Science, 221, 73-80. https://doi.org/10.1016/j.procs.2023.07.011.
• Singgalen, Y. A. (2024). Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm. Journal of Computer System and Informatics, 5(2), 442-453. doi:https://doi.org/10.47065/josyc.v5i2.4924
• Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310-1323. doi:https://doi.org/10.1016/j.tourman.2010.12.011
• Sun, A., Lim, E.-P., & Liu, Y. (2009). On strategies for imbalanced text classification using SVM: A comparative study. Decision Support Systems, 48(1), 191-201. doi:https://doi.org/10.1016/j.dss.2009.07.011
• Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9, 293–300. doi: https://doi.org/10.1023/A:1018628609742
• Tabelessy, W., Sitaniapessy, R., & Lekatompessy, D. (2023). Establishing customer satisfaction to increase customer loyalty in collin beach hotel. Indonesian Journal of Contemporary Multidisciplinary Research, 2(2), 165-176. https://doi.org/10.55927/modern.v2i2.3431.
• Tien, N., Anh, D., Dung, H., On, P., Anh, V., Dat, N., & Tam, B. (2021). Factors impacting customer satisfaction at Vietcombank in Vietnam. Himalayan Economics and Business Management, 2(4), 44-51.
• Truong, N., Dang-Pham, D., McClelland, R., & Nkhoma, M. (2020). Exploring the impact of innovativeness of hospitality service operation on customer satisfaction. Operations and Supply Chain Management, 13(3), 307-319. http://doi.org/10.31387/oscm0420272.
• Ünal, A. (2019). Otel müşterilerinin e-şikâyetlerinin sınıflandırılmasına yönelik bir çalışma. International Journal of Management Economics & Business, 15(2), 561-581. doi:http://dx.doi.org/10.17130/ijmeb.2019252111
• Vapnik, V. (1998). The Support Vector Method of Function Estimation. J. A. Suykens, & J. Vandewalle (Dü) içinde, Nonlinear Modeling (s. 55-85). Boston, MA.: Springer. doi:https://doi.org/10.1007/978-1-4615-5703-6_3
• Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions On Neural Networks, 10(5), 988-999. doi:10.1109/72.788640
• Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Record, 31(1), 76-77. doi:https://doi.org/10.1145/507338.507355
• Wulandari, D. (2022). Customer satisfaction as a priority in excellent banking services. Jurnal Manajemen Organisasi dan Industri, 1(1), 27-34.
• www.tripadvisor.com. (2024, Mart 30.03.2024). Tripadvisor: https://tripadvisor.mediaroom.com/tr-about-us adresinden alındı
• Xia, S.-y., Xiong, Z.-y., Luo, Y.-g., & Dong, L.-m. (2015). A method to improve support vector machine based on distance to hyperplane. Optik, 126(20), 2405-2410. doi:https://doi.org/10.1016/j.ijleo.2015.06.010
• Yi, H.-T., Yeo, C., Amenuvor, F., & Boateng, H. (2021). Examining the relationship between customer bonding, customer participation, and customer satisfaction. Journal of Retailing and Consumer Services, 62(102598), https://doi.org/10.1016/j.jretconser.2021.102598.
• Yılmaz, E. (2020). The effects on consumer behavior of hotel related comments on the tripadvisor website: An Istanbul case. Advances in Hospitality and Tourism Research (AHTR), 8(1), 1-29. https://doi.org/10.30519/ahtr.536303.
• Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017a). Learning k for KNN classification. ACM Transactions on Intelligent Systems and Technology, 8(3), 1-19. doi:https://doi.org/10.1145/2990508
• Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017b). Efficient KNN classification with different numbers of nearest neighbors. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1774-1785. doi:10.1109/TNNLS.2017.2673241
• Zhang, X., & Kim, H.-S. (2021). Customer experience and satisfaction of disneyland hotel through big data analysis of online customer reviews. Sustainability, 13, 1-17. https://doi.org/10.3390/su132212699.
• Zhao, Y., Xu, X., & Wang, M. (2019). Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. International Journal of Hospitality Management, 76, 111-121. doi:https://doi.org/10.1016/j.ijhm.2018.03.017
• Zhu, X., Suk, H.-I., & Shen, D. (2014). A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage, 100, 91-105. doi:https://doi.org/10.1016/j.neuroimage.2014.05.078
• Zlatanov, S., & Popesku, J. (2019). Current applications of artificial intelligence in tourism and hospitality. International Scientific Conference on Information Technology and Data Related Research., (s. 84-90). doi:https://doi.org/10.15308/Sinteza-2019-84-90