Forecasting Hotel Room Sales within Online Travel Agencies by Combining Multiple Feature Sets

Hotel Room Sales prediction using previous booking data is a prominent research topic for the online travel agency (OTA) sector. Various approaches have been proposed to predict hotel room sales for different prediction horizons, such as yearly demand or daily number of reservations. An OTA website includes offers of many companies for the same hotel, and the position of the company's offer in OTA website depends on the bid amount given for each click by the company. Therefore, the accurate prediction of the sales amount for a given bid is a crucial need in revenue and cost management for the companies in the sector. In this paper, we forecast the next day's sales amount in order to provide an estimate of daily revenue generated per hotel. An important contribution of our study is to use an enriched dataset constructed by combining the most informative features proposed in various related studies for hotel sales prediction. Moreover, we enrich this dataset with a set of OTA specific features that possess information about the relative position of the company's offers to that of its competitors in a travel metasearch engine website. We provide a real application on the hotel room sales data of a large OTA in Turkey. The comparative results show that enrichment of the input representation with the OTA-specific additional features increases the generalization ability of the prediction models, and tree-based boosting algorithms perform the best results on this task.