Best buy: what does it mean in corporate travel?

Corporate travel departments manage travel costs prescribing best buy decisions. With reference to flights, the crucial choice concerns the fare. As the difference in price can be great, it might seem convenient to always purchase the cheapest ticket, neglecting that the more expensive a fare, the lower the cost of change or cancellation. We define “best buy” minimizing the expected global cost of flights, whence estimating the probability distribution of travellers’ behaviour is necessary. Variables useful to this aim are detected. This statistical approach can improve the efficiency of corporate policies, leading to diversified purchasing strategies.     

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 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.