Reverse engineering the last-minute on-line pricing practices: an application to hotels

Andrea Guizzardi, Luca Vincenzo Ballestra and Enzo D’Innocenzo
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Enzo D’Innocenzo: Alma Mater Studiorum University of Bologna

Statistical Methods & Applications, 2024, vol. 33, issue 3, No 10, 943-971

Abstract: Abstract We suggest a nonlinear time series methodology to model the (last-minute) price adjustments that hotels active in the online market make to adapt their early-booking rates in response to unpredictable fluctuations in demand. We use this approach to reverse-engineer the pricing strategies of six hotels in Milan, Italy, each with different features and services. The results reveal that the hotels’ ability to align last-minute adjustments with early-booking decisions and account for stochastic demand seasonality varies depending on factors such as size, star rating, and brand affiliation. As a primary empirical finding, we show that the autocorrelations of the first four moments of the last-minute price adjustment can be used to gain crucial insights into the hoteliers’ pricing strategies. Scaling up this approach has the potential to equip policymakers in smart destinations with a reliable and transparent tool for the real-time monitoring of demand dynamics.

Keywords: Management strategy assessment; Web-based shared knowledge; Revenue managemenet; Last-minute price adjustment; Non-deterministic seasonality (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-024-00751-3

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