Within the most productive route, online travel agencies (OTAs) intend to use advanced digital media ads to expand their piece of the industry as a whole. The metasearch engine platforms are among the most consistently used digital media environments by OTAs. Most OTAs offer day by day deals in metasearch engine platforms that are paying per click for each hotel to get reservations. The administration of offering methodologies is critical along these lines to reduce costs and increase revenue for online travel agencies. In this study, we tried to predict both the number of impressions and the regular Click-Through-Rate (CTR) level of hotel advertising for each hotel and the daily sales amount. A significant commitment of our research is to use an extended dataset generated by integrating the most informative features implemented in various related studies as the rolling average for a different amount of day and shifted values for use in the proposed test stage for CTR, impression and sales prediction. The data is created in this study by one of Turkey's largest OTA, and we are giving OTA's a genuine application. The results at each prediction stage show that enriching the training data with the OTA-specific additional features, which are the most insightful and sliding window techniques, improves the prediction models' generalization capability, and tree-based boosting algorithms carry out the greatest results on this problem. Clustering the dataset according to its specifications also improves the results of the predictions.