Designing Cancellation Intervention System with Sliding Lead Times

In this paper, we propose a new cancellation intervention system to minimize possible revenue loss of business entity in tourism sector from last-minute booking cancellation. The proposed system automatically sends e-reminders to travelers who are most likely to cancel their bookings using calibrated prediction models on the subsets of bookings with different lead times. In particular, cost-sensitive learning methods with varying class weights in machine learning community are adopted to overcome hurdles from imbalanced class distributions. Finally, this study introduces cumulative gain charts to provide general guidelines on how to maximize the expected benefits from the proposed system.