Bayesian Analysis of Hotel Booking Cancellations: A Hierarchical Modeling Approach

TL;DR

This paper employs Bayesian hierarchical modeling to analyze hotel booking cancellations, revealing how lead time, special requests, and parking influence cancellation probabilities and how these effects vary by hotel type, offering valuable insights for revenue management.

Contribution

It introduces a Bayesian hierarchical modeling framework for hotel cancellations, incorporating interactions with hotel type, and compares model complexities to identify the best predictive model.

Findings

Longer lead times increase cancellation probability.

Special requests and parking reduce cancellation risk.

Full interaction model outperforms simpler models in prediction.

Abstract

This study presents a comprehensive Bayesian analysis of hotel booking cancellations using PyMC, comparing three model specifications of increasing complexity. We investigate how lead time, special requests, and parking requirements affect cancellation probability, and explore interaction effects with hotel type. Using MCMC sampling (NUTS algorithm) on 5,000 booking records, we find strong evidence that longer lead times increase cancellation probability (posterior mean: 0.600, 95\% HDI: [0.532, 0.661]), while special requests (posterior mean: -0.642) and parking (posterior mean: -3.879) significantly reduce cancellation risk. Model comparison via WAIC reveals that the full interaction model provides the best predictive performance, suggesting that the effects of booking characteristics vary systematically between city and resort hotels. This Bayesian approach enables full uncertainty quantification and provides actionable insights for revenue management.