Big data from dynamic pricing: A smart approach to tourism demand forecasting

Andrea Guizzardi, Flavio Maria Emanuele Pons, Giovanni Angelini and Ercolino Ranieri

International Journal of Forecasting, 2021, vol. 37, issue 3, 1049-1060

Abstract: Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.

Keywords: Regional forecasting; Daily forecasting; Leading indicator; Advance booking; Dynamic pricing; Hotelier’s expectations about tourism demand (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:3:p:1049-1060

DOI: 10.1016/j.ijforecast.2020.11.006

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