Using machine learning and big data for efficient forecasting of hotel booking cancellations [Summary]
| dc.date.accessioned | 2020-10-19T17:42:41Z | en |
| dc.date.available | 2020-10-19T17:42:41Z | en |
| dc.date.issued | 2020-10-18 | en |
| dc.description.abstract | Through this research, it was possible to add the academic literature about the hotel and lodging industry regarding forecasting hotel booking cancellations using artificial intelligence. Theoretically, it has contributed as well in terms of using PNR data for forecasting hotel cancellations with a high level of accuracy. Also, it is also significant that the research only used a reduced number of independent 13 variables compare to previous research which used at least 37. These variables are common data for hotels to acquire, and it makes it possible for hotels to accumulate to train the model enough to anticipate the booking cancellation. From a managerial perspective, it is implied that customer’s historical records are critical assets for the hospitality industry to forecast the cancellation and cope with to avoid revenue loss. Since the research results that we can identify which customer is likely to cancel, hoteliers can take proactive actions to prevent cancel from them. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/100618 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | booking cancellation | en |
| dc.subject | AI | en |
| dc.subject | Machine learning | en |
| dc.title | Using machine learning and big data for efficient forecasting of hotel booking cancellations [Summary] | en |
| dc.type | Summary | en |
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