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460. Boosting Hotel Profits: The Power of Enhanced Cancellation Predictions
Invited abstract in session TB-59: Pricing and Capacity Management, stream Pricing and Revenue Management.
Tuesday, 10:30-12:00Room: S08 (building: 101)
Authors (first author is the speaker)
| 1. | Ryusuke Kimura
|
| Data Management & Planning Office, Recruit Co., Ltd. | |
| 2. | Kimitoshi Sato
|
| Kanagawa university |
Abstract
The hotel industry faces the uncertainty of reservations and cancellations. Although dynamic pricing provides appropriate discounts for last-minute cancellations, it is not always possible to fill vacancies unless there is sufficient demand. Therefore, it is important to know how the accuracy of cancellation forecasts affects the results of dynamic pricing in practice. In this study, we analyze the relationship between forecast accuracy and expected returns using actual data on room reservations and cancellations in a region in Japan. The results show that a 10% improvement in forecast accuracy results in a 2% improvement in revenue. We also show that this improvement simultaneously improves consumer surplus by lowering the average sales price and improving the load factor. Furthermore, the improvement is more effective during the peak season when the number of potential customers is higher.
Keywords
- Revenue Management and Pricing
- Analytics and Data Science
- Artificial Intelligence
Status: accepted
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