Booker Prediction From Requests For Quotation Via Machine Learning Techniques
Purpose – Many incoming requests for quotation usually compete for the attention of accommodation service provider staff on a daily basis, while some of them might deserve more priority than others. Design – This research is therefore based on the correspondence history of a large booking manage...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of tourism and hospitality management
2023-01-01
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| Series: | Tourism and Hospitality Management |
| Subjects: | |
| Online Access: | https://thm.fthm.hr/images/issues/vol2no1/3_Runggaldier_Sottocornola_Janes_Stella_Zanker |
| _version_ | 1797829260141920256 |
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| author | Samuel Runggaldier Gabriele Sottocornola Andrea Janes Fabio Stella Markus Zanker |
| author_facet | Samuel Runggaldier Gabriele Sottocornola Andrea Janes Fabio Stella Markus Zanker |
| author_sort | Samuel Runggaldier |
| collection | DOAJ |
| description | Purpose – Many incoming requests for quotation usually compete for the attention of
accommodation service provider staff on a daily basis, while some of them might deserve
more priority than others.
Design – This research is therefore based on the correspondence history of a large booking
management system that examines the features of quotation requests from aspiring guests in
order to learn and predict their actual booking behavior.
Approach – In particular, we investigate the effectiveness of various machine learning
techniques for predicting whether a request will turn into a booking by using features such as
the length of stay, the number and type of guests, and their country of origin. Furthermore, a
deeper analysis of the features involved is performed to quantify their impact on the prediction
task.
Findings – We based our experimental evaluation on a large dataset of correspondence data
collected from 2014 to 2019 from a 4-star hotel in the South Tyrol region of Italy. Numerical
experiments were conducted to compare the performance of different classification models
against the dataset. The results show a potential business advantage in prioritizing requests
for proposals based on our approach. Moreover, it becomes clear that it is necessary to solve
the class imbalance problem and develop a proper understanding of the domain-specific
features to achieve higher precision/recall for the booking class. The investigation on feature
importance also exhibits a ranking of informative features, such as the duration of the stay, the
number of days prior to the request, and the source/country of the request, for making accurate
booking predictions.
Originality of the research – To the best of our knowledge, this is one of the first attempts to
apply and systematically harness machine learning techniques to request for quotation data in
order to predict whether the request will end up in a booking. |
| first_indexed | 2024-04-09T13:17:36Z |
| format | Article |
| id | doaj.art-2e72d07615f8445aba284f3f6ac81da5 |
| institution | Directory Open Access Journal |
| issn | 1330-7533 1847-3377 |
| language | English |
| last_indexed | 2024-04-09T13:17:36Z |
| publishDate | 2023-01-01 |
| publisher | Faculty of tourism and hospitality management |
| record_format | Article |
| series | Tourism and Hospitality Management |
| spelling | doaj.art-2e72d07615f8445aba284f3f6ac81da52023-05-11T15:55:02ZengFaculty of tourism and hospitality managementTourism and Hospitality Management1330-75331847-33772023-01-01291254310.20867/thm.29.1.3Booker Prediction From Requests For Quotation Via Machine Learning TechniquesSamuel Runggaldier0Gabriele Sottocornola1https://orcid.org/0000-0001-9983-2330Andrea Janes2Fabio Stella3Markus Zanker4Free University of Bozen-Bolzano, Faculty of Computer Science Piazza Domenicani, 3, Bolzano, ItalyResearch Assistant (Corresponding Author) Free University of Bozen-Bolzano, Faculty of Computer Science Piazza Domenicani, 3, Bolzano, ItalyFree University of Bozen-Bolzano, Faculty of Computer Science Piazza Domenicani, 3, Bolzano, ItalyUniversity of Milano-Bicocca, Department of Informatics, Systems and Communication Viale Sarca, 336, Milano, ItalyFree University of Bozen-Bolzano, Faculty of Computer Science Piazza Domenicani, 3, Bolzano, Italy E-mail: Markus.Zanker@unibz.it University of Klagenfurt Faculty of Technical Sciences Universitätsstrasse 65, Klagenfurt, AustriaPurpose – Many incoming requests for quotation usually compete for the attention of accommodation service provider staff on a daily basis, while some of them might deserve more priority than others. Design – This research is therefore based on the correspondence history of a large booking management system that examines the features of quotation requests from aspiring guests in order to learn and predict their actual booking behavior. Approach – In particular, we investigate the effectiveness of various machine learning techniques for predicting whether a request will turn into a booking by using features such as the length of stay, the number and type of guests, and their country of origin. Furthermore, a deeper analysis of the features involved is performed to quantify their impact on the prediction task. Findings – We based our experimental evaluation on a large dataset of correspondence data collected from 2014 to 2019 from a 4-star hotel in the South Tyrol region of Italy. Numerical experiments were conducted to compare the performance of different classification models against the dataset. The results show a potential business advantage in prioritizing requests for proposals based on our approach. Moreover, it becomes clear that it is necessary to solve the class imbalance problem and develop a proper understanding of the domain-specific features to achieve higher precision/recall for the booking class. The investigation on feature importance also exhibits a ranking of informative features, such as the duration of the stay, the number of days prior to the request, and the source/country of the request, for making accurate booking predictions. Originality of the research – To the best of our knowledge, this is one of the first attempts to apply and systematically harness machine learning techniques to request for quotation data in order to predict whether the request will end up in a booking.https://thm.fthm.hr/images/issues/vol2no1/3_Runggaldier_Sottocornola_Janes_Stella_Zanker booking predictionrequest for quotationmachine learningclass imbalance problemfeature importance analysis |
| spellingShingle | Samuel Runggaldier Gabriele Sottocornola Andrea Janes Fabio Stella Markus Zanker Booker Prediction From Requests For Quotation Via Machine Learning Techniques Tourism and Hospitality Management booking prediction request for quotation machine learning class imbalance problem feature importance analysis |
| title | Booker Prediction From Requests For Quotation Via Machine Learning Techniques |
| title_full | Booker Prediction From Requests For Quotation Via Machine Learning Techniques |
| title_fullStr | Booker Prediction From Requests For Quotation Via Machine Learning Techniques |
| title_full_unstemmed | Booker Prediction From Requests For Quotation Via Machine Learning Techniques |
| title_short | Booker Prediction From Requests For Quotation Via Machine Learning Techniques |
| title_sort | booker prediction from requests for quotation via machine learning techniques |
| topic | booking prediction request for quotation machine learning class imbalance problem feature importance analysis |
| url | https://thm.fthm.hr/images/issues/vol2no1/3_Runggaldier_Sottocornola_Janes_Stella_Zanker |
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