UNDERSTANDING HOTEL BOOKING BEHAVIOUR THROUGH RESERVATION PATTERNS AND CUSTOMER CHARACTERISTICS


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Authors

  • Rishi Kumar Singh Management/ Birla Institute Of Technology Mesra Ranchi Birla Institute Of Technology Mesra Ranchi Birla Institute Of Technology Mesra Ranchi, City, State, Country - RANCHI , JHARKHAND, INDIA

DOI:

https://doi.org/10.53555/th.v1i2.2594

Keywords:

hotel booking behaviour, booking cancellation, reservation patterns, customer characteristics

Abstract

The cancellations of hotel bookings pose significant operational and revenue management problems as the bookings recorded in the systems do not necessarily translate to actual bookings. This study investigated the hotel booking behaviour in order to determine the factors affecting the cancellation of the booking. After cleaning the data, a quantitative secondary data design was used for 118,564 valid hotel reservations. The analysis of booking volume, seasonality, customer categories, lead time, duration of stay, deposit type, market segment, previous booking history, and special requests was performed in descriptive statistics, chi-square test, group comparison, correlation analysis and binary logistic regression. The general cancellation rate was 37.3%, with City Hotel reservations having a higher cancellation rate than Resort Hotel reservations. Repeat customers showed more consistent booking behaviour, while transient customers and new customers were more likely to cancel. The lead time for cancelled reservations was significantly longer than for non-cancelled reservations. The strongest categorical association was with deposit type, and previous cancellations were a significant risk factor for cancellation. On the other hand, frequent guests and extra special requests were strong factors in decreasing the chance of cancellation. Seasonal differences were also seen for the arrival month. The results presented herein illustrate how reservation timing, booking trends, customer characteristics, conditions of deposit, hotel classification, and engagement cues can be used to help forecast customer cancellations, communicate with customers, and manage hotel revenue by risk.

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Published

2026-06-25