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

Baysal E., Bayılmış C., Baykal Baysal D.

Sakarya University Journal of Computer and Information Sciences (Online), cilt.9, ss.609-617, 2026 (Scopus, TRDizin)

Özet

In today’s technological era, the pervasive presence of technology has led to exponential growth in data gen-

eration. 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 predict-

ing 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