Predicting Tourist Arrivals: A Google Trends-Based Model for Destination Management
Predicting Tourist Arrivals: A Google Trends-Based Model for Destination Management

Summary/Abstract: In this paper, a review of the scientific literature on the importance and knowledge of the tourist destination for the formation of a correct strategy for its development and promotion is made. The possibility of managing the tourist destination by modeling with Google Trends data is represented. A review of studies using the mentioned tool in the field of tourism is conducted. The potential benefits of using Google Trends data on searches for a city as a keyword in combination with archival data are summarized. The possibilities for modeling the visits of tourists using data from Google Trends are investigated, as a result of which a model is built by applying polynomial regression to predict the overnight stays of foreign citizens in the city of Veliko Tarnovo based on archival data from the National Statistical Institute and Google Trends data. Through data analytics with Google Trends, it is concluded that key value is created for the external and internal environment, influencing the destination. The data creates conditions for the development of management strategies for attracting tourists based on primary and secondary data for analysis. The application of the received data sets through data analysis are also important for tourism organizations and institutional bodies in tourism (macro- and meso-level). The resulting analysis affects issues related to the management and marketing of the destination, and on the other hand, it directly affects the segmentation of the market and the distribution of products and services to the end user (micro level).

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