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Otel Rezervasyon İptal Tahmin Modelinin Veri Madenciliği Algoritmaları ile Uygulanması

Year 2022, Volume: 1 Issue: 2, 15 - 24, 31.10.2022

Kevser Şahinbaş Ozge Doguc

Abstract

Otel rezervasyon iptalleri, oda rezervasyon sistemleri üzerindeki etkileri nedeniyle gelir yönetiminde kritik olarak kabul edildiği için otel ve konaklama sektörünün önemli bir problemidir. Gelir yöneticilerinin bakış açısından, satılan her otel odası, uçak koltuğu vb. ek kar sağlamaktadır. Bu nedenle, satılmayan her oda ya da koltuğun potansiyel bir kaybı temsil ettiği sonucu çıkmaktadır. Müşterileri iptal etmeye sevk eden sebepler veya bunun nasıl önlenebileceği hakkında bilgi sahibi olmak oldukça önem taşımaktadır. Bu çalışmanın amacı bireysel otel iptallerinin tahminine odaklanmak ve iptal üzerinde en çok hangi parametrelerin olduğunu ortaya çıkarmaktır. Bu çalışmada Veri Madenciliği tekniklerinden Karar Ağaçları ve Rastgele Orman algoritmaları uygulanmıştır. Elde edilen sonuçlara göre %86.7 oranında doğruluk oranı ile Rastgele Orman algoritması daha iyi sonuç vermiştir. Depozito tipi ve müşterinin daha önce rezervasyon iptali yapıp yapmadığı parametrelerinin sınıflandırma üzerinde en fazla etkiye sahip oldukları gözlemlenmiştir. Bu modeli benimseyen kuruluşlar turist varışlarıyla ilgili eylem protokollerini güçlendirebilir, rezervasyon yönetim sistemleri ve iptal politikalarını optimize edilebilirler.

Keywords

otel rezervasyonları iptaller gelir yönetimi veri yönetimi tahminleme

References

  • Antonio N, de Almeida A, Nunes L. (2017). Predicting hotel booking cancellations to decrease uncertainty and increase revenue, Tour. Manag. Stud., 13(2).
  • Breiman L. (2001). Random forests. Mach. Learn., 45(1), 5– 32.
  • Brijain, M., Patel, R., Kushik, M. R., & Rana, K. (2014). A survey on decision tree algorithm for classification. IJEDR .
  • Chen, C. C., Schwartz, Z., & Vargas, P. (2011). The search for the best deal: How hotel cancellation policies affect the search and booking decisions of deal-seeking customers. International Journal of Hospitality Management, 30(1), 129-135.
  • Han, J., ve Kamber, M. (2006). Data mining: concepts and techniques. USA: Morgan Kaufmann.
  • https://www.statista.com/statistics/186743/international-tourist-arrivals-worldwide-by-region-since-2010/ Erişim Tarihi: 10.05.2022
  • Liu, Y. (2014). Random forest algorithm in big data environment. Computer modelling & new technologies, 18(12A), 147-151.
  • Li, X., & Claramunt, C. (2006). A spatial entropy‐based decision tree for classification of geographical information. Transactions in GIS, 10(3), 451-467.
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22. Lin, W., Wu, Z., Lin, L., Wen, A., & Li, J. (2017). An ensemble random forest algorithm for insurance big data analysis. Ieee access, 5, 16568-16575.
  • Mehrotra R, Ruttley J. (2006). Revenue management. (2nd ed.). Washington, DC, American Hotel and Lodging Association.
  • Pereira, L. N. (2016). An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management, 58, 13-23.
  • Sánchez-Medina, A. J., & Eleazar, C. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546.
  • Sierag, D. D., Koole, G. M., van der Mei, R. D., Van der Rest, J. I., & Zwart, B. (2015). Revenue management under customer choice behaviour with cancellations and overbooking. European journal of operational research, 246(1), 170-185.
  • Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3), 660-674. Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29.
  • Sánchez, E. C., Sánchez-Medina, A. J., & Pellejero, M. (2020). Identifying critical hotel cancellations using artificial intelligence. Tourism Management Perspectives, 35, 100718.
  • Yüksel, S. (2005). An integrated forecasting approach for hotels. In International Symposium on the Analytic Hierarchy Process (ISAHP) (Vol. 10).
  • Wu, D. C., Song, H., & Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management.

Year 2022, Volume: 1 Issue: 2, 15 - 24, 31.10.2022

Kevser Şahinbaş Ozge Doguc

Abstract

References

  • Antonio N, de Almeida A, Nunes L. (2017). Predicting hotel booking cancellations to decrease uncertainty and increase revenue, Tour. Manag. Stud., 13(2).
  • Breiman L. (2001). Random forests. Mach. Learn., 45(1), 5– 32.
  • Brijain, M., Patel, R., Kushik, M. R., & Rana, K. (2014). A survey on decision tree algorithm for classification. IJEDR .
  • Chen, C. C., Schwartz, Z., & Vargas, P. (2011). The search for the best deal: How hotel cancellation policies affect the search and booking decisions of deal-seeking customers. International Journal of Hospitality Management, 30(1), 129-135.
  • Han, J., ve Kamber, M. (2006). Data mining: concepts and techniques. USA: Morgan Kaufmann.
  • https://www.statista.com/statistics/186743/international-tourist-arrivals-worldwide-by-region-since-2010/ Erişim Tarihi: 10.05.2022
  • Liu, Y. (2014). Random forest algorithm in big data environment. Computer modelling & new technologies, 18(12A), 147-151.
  • Li, X., & Claramunt, C. (2006). A spatial entropy‐based decision tree for classification of geographical information. Transactions in GIS, 10(3), 451-467.
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22. Lin, W., Wu, Z., Lin, L., Wen, A., & Li, J. (2017). An ensemble random forest algorithm for insurance big data analysis. Ieee access, 5, 16568-16575.
  • Mehrotra R, Ruttley J. (2006). Revenue management. (2nd ed.). Washington, DC, American Hotel and Lodging Association.
  • Pereira, L. N. (2016). An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management, 58, 13-23.
  • Sánchez-Medina, A. J., & Eleazar, C. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546.
  • Sierag, D. D., Koole, G. M., van der Mei, R. D., Van der Rest, J. I., & Zwart, B. (2015). Revenue management under customer choice behaviour with cancellations and overbooking. European journal of operational research, 246(1), 170-185.
  • Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3), 660-674. Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29.
  • Sánchez, E. C., Sánchez-Medina, A. J., & Pellejero, M. (2020). Identifying critical hotel cancellations using artificial intelligence. Tourism Management Perspectives, 35, 100718.
  • Yüksel, S. (2005). An integrated forecasting approach for hotels. In International Symposium on the Analytic Hierarchy Process (ISAHP) (Vol. 10).
  • Wu, D. C., Song, H., & Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management.
There are 17 citations in total.

Details

Primary Language Turkish
Subjects Tourism (Other)
Journal Section Research Articles
Authors

Kevser Şahinbaş 0000-0002-8076-3678

Ozge Doguc 0000-0002-5971-9218

Early Pub Date August 24, 2022
Publication Date October 31, 2022
Submission Date August 20, 2022
Published in Issue Year 2022 Volume: 1 Issue: 2

Cite

APA Şahinbaş, K., & Doguc, O. (2022). Otel Rezervasyon İptal Tahmin Modelinin Veri Madenciliği Algoritmaları ile Uygulanması. Selçuk Turizm Ve Bilişim Araştırmaları Dergisi, 1(2), 15-24.
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