Predicting hotel booking cancellations to decrease uncertainty and increase revenue
Booking cancellations have a substantial impact in demandmanagement decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels imple...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Algarve, ESGHT/CINTURS
2017-04-01
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| Series: | Tourism & Management Studies |
| Subjects: | |
| Online Access: | https://tmstudies.net/index.php/ectms/article/view/1000/pdf_51 |
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| _version_ | 1832568750158643200 |
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| author | Nuno Antonio Ana de Almeida Luis Nunes |
| author_facet | Nuno Antonio Ana de Almeida Luis Nunes |
| author_sort | Nuno Antonio |
| collection | DOAJ |
| description | Booking cancellations have a substantial impact in demandmanagement decisions in the hospitality industry. Cancellations limit
the production of accurate forecasts, a critical tool in terms of
revenue management performance. To circumvent the problems
caused by booking cancellations, hotels implement rigid cancellation
policies and overbooking strategies, which can also have a negative
influence on revenue and reputation.
Using data sets from four resort hotels and addressing booking
cancellation prediction as a classification problem in the scope of data
science, authors demonstrate that it is possible to build models for
predicting booking cancellations with accuracy results in excess of
90%. This demonstrates that despite what was assumed by Morales
and Wang (2010) it is possible to predict with high accuracy whether
a booking will be canceled.
Results allow hotel managers to accurately predict net demand and
build better forecasts, improve cancellation policies, define better
overbooking tactics and thus use more assertive pricing and
inventory allocation strategies. |
| format | Article |
| id | doaj-art-a5a169b214f54f3f8e4d78a2bc3d6186 |
| institution | Kabale University |
| issn | 2182-8466 |
| language | English |
| publishDate | 2017-04-01 |
| publisher | University of Algarve, ESGHT/CINTURS |
| record_format | Article |
| series | Tourism & Management Studies |
| spelling | doaj-art-a5a169b214f54f3f8e4d78a2bc3d61862025-02-03T00:37:27ZengUniversity of Algarve, ESGHT/CINTURSTourism & Management Studies2182-84662017-04-01132253910.18089/tms.2017.13203Predicting hotel booking cancellations to decrease uncertainty and increase revenueNuno Antonio0Ana de Almeida1Luis Nunes2ISCTE, Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisbon, PortugalISCTE Instituto Universitário de Lisboa, CISUC, Av. das Forças Armadas, 1649-026 Lisbon, PortugalISCTE, Instituto Universitário de Lisboa, Instituto de Telecomunicações, ISTAR, Av. das Forças Armadas, 1649-026 Lisbon, Portugal Booking cancellations have a substantial impact in demandmanagement decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation. Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.https://tmstudies.net/index.php/ectms/article/view/1000/pdf_51data sciencehospitality industrymachine learningpredictive modelingrevenue management |
| spellingShingle | Nuno Antonio Ana de Almeida Luis Nunes Predicting hotel booking cancellations to decrease uncertainty and increase revenue Tourism & Management Studies data science hospitality industry machine learning predictive modeling revenue management |
| title | Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
| title_full | Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
| title_fullStr | Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
| title_full_unstemmed | Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
| title_short | Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
| title_sort | predicting hotel booking cancellations to decrease uncertainty and increase revenue |
| topic | data science hospitality industry machine learning predictive modeling revenue management |
| url | https://tmstudies.net/index.php/ectms/article/view/1000/pdf_51 |
| work_keys_str_mv | AT nunoantonio predictinghotelbookingcancellationstodecreaseuncertaintyandincreaserevenue AT anadealmeida predictinghotelbookingcancellationstodecreaseuncertaintyandincreaserevenue AT luisnunes predictinghotelbookingcancellationstodecreaseuncertaintyandincreaserevenue |