Click and Sales Prediction for Digital Advertisements: Real World Application for OTAs

Abstract

Online travel agencies (OTAs) aim to use digital media advertisements within the most efficient way for increasing their market share. One of the most commonly used digital media environments by OTAs are the metasearch bidding engines. In metasearch bidding engines, many OTAs offer daily bids per click for each hotel to get reservations. Therefore, the management of bidding strategies is crucial to minimize the cost and maximize the revenue for OTAs. In this paper, we aim to predict both the impression count and Click-Through-Rate (CTR) metrics of hotel advertisements for an OTA and then use these values to obtain the number of clicks the OTA will take for each hotel. After that using these predictions, we aim to 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 click and sales prediction. In this study, the data which is generated by one of the biggest OTA in Turkey and we provide a real-world application for OTA’s. The results both for Impression, CTR and sales prediction 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.