Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data

Abstract

This study uses the web traffic volume data of a destination marketing organization (DMO) to predict hotel demand for the destination. The results show a significant improvement in the error reduction of ARMAX models, compared with their ARMA counterparts, for short-run forecasts of room nights sold by incorporating web traffic data as an explanatory variable.These empirical results demonstrate the significant value of website traffic data in predicting demand for hotel rooms at a destination, and potentially even local businesses’ future revenue and performance. The implications for future research on using big data for forecasting hotel demand is also discussed.

Keywords

tourism demand forecasting time series online data hotel occupancy big data website traffic

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