A Predictive Analysis of Tourism Recovery Using Digital Marketing Metrics

This study examines how digital marketing metrics can help predict tourism recovery, especially after COVID-19. By analyzing data from various marketing channels, such as click-through rates (CTR), conversion rates (CVR), and Google's Search Volume Index (SVI), we aimed to uncover patterns in consumer behavior that signal recovery trends. Using advanced machine learning models, like the Voting and Stacking Classifiers, we found that the Voting Classifier achieved impressive results, with an accuracy of 93.4% and a weighted F1-score of 0.94. These metrics help tourism providers understand customer interest and engagement, allowing them to tailor marketing efforts to boost conversions. The findings underscore the importance of metrics like Click-Through Rate (CTR) and Search Volume Index (SVI), which are closely linked to consumer intent and provide timely insights for tourism stakeholders. We recommend that tourism organizations adopt a multi-channel marketing strategy, integrating various platforms to reach broader audiences effectively. Additionally, regularly updating predictive models can help organizations adapt to evolving market conditions and shifts in consumer behavior. Future research could build on these insights by incorporating social and environmental variables, offering a more comprehensive view of tourism recovery. This study provides practical strategies and tools to support a data-driven, resilient, and sustainable recovery for the tourism industry, enhancing its long-term growth and adaptability.