Big Data–Driven Cost-Per-Click Prediction for Hotels

Abstract

In today’s technological era, the pervasive presence of technology has led to exponential growth in data generation. The tourism industry, a major contributor to this data flood, generates large volumes of data, including comments, photos, and location-sharing on social media. Online tourism agencies collect metadata, including hotel views, clicks, and visitor comments. This metadata enables these agencies to predict click estimates and cost-per-click (CPC) for hotels, aiding in the development of effective bid strategies. This study presents a model for estimating CPC using big data analytics, leveraging metadata from online tourism agency dashboards. The key findings show that the gradient-boosted tree algorithm outperforms the Random Forest algorithm in predicting CPC with greater accuracy. The proposed model improves bid strategies and offers a significant advantage by leveraging extensive, diverse data. This research contributes to the field by demonstrating how advanced machine learning techniques can optimize marketing strategies within the tourism industry.

Keywords

• Big data
• Click cost prediction
• Machine learning
• Tourism

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