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

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 str...

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Bibliographic Details
Published in:International journal of forecasting 2021-07, Vol.37 (3), p.1049-1060
Main Authors: Guizzardi, Andrea, Pons, Flavio Maria Emanuele, Angelini, Giovanni, Ranieri, Ercolino
Format: Article
Language:English
Subjects:
Advance booking
Analysis
Big data
Continental interfaces, environment
Daily forecasting
Dynamic pricing
Hotelier’s expectations about tourism demand
Leading indicator
Ocean, Atmosphere
Price indexes
Regional forecasting
Sciences of the Universe
Travel industry
Web sites
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Summary: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.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2020.11.006