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

Author

Listed:
  • Andrea Guizzardi

    (Alma Mater Studiorum University of Bologna)

  • Luca Vincenzo Ballestra

    (Alma Mater Studiorum University of Bologna)

  • Enzo D’Innocenzo

    (Alma Mater Studiorum University of Bologna)

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.

Suggested Citation

  • Andrea Guizzardi & Luca Vincenzo Ballestra & Enzo D’Innocenzo, 2024. "Reverse engineering the last-minute on-line pricing practices: an application to hotels," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 943-971, July.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:3:d:10.1007_s10260-024-00751-3
    DOI: 10.1007/s10260-024-00751-3
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    References listed on IDEAS

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