Russian Federation
Analysis of search queries, which come to the site visitors from search engines is quite relevant. Because understanding the intents of users can predict the number of visits to the site. It is possible to predict the conversion and the number of contracts by applying the theory of probability. The main essence of the study is the clustering of search queries by the Ward method for the UK and US. The queries in the clusters contain a common intent of users of the Internet. As a result, were formed two clusters of UK and three of US.
Google Trends, Internet Marketing, Digital Economy, Search Engine Marketing, Google Trends
1. Chekushin A. Azbuka klasterizacii [Elektronnyy resurs] searchengines.ru/azbuka_klasterizats.html
2. Daniel Tunkelang. Question Answering [Elektronnyy resurs] https://queryunderstanding.com/question-answering-94984185c203
3. Hongping Hu, Li Tang, Shuhua Zhang, Haiyan Wang, Predicting the direction of stock markets using optimized neural networks with Google Trends // Neurocomputing, Volume 285. 2018. S.188-195. https://doi.org/10.1016/j.neucom.2018.01.038.
4. Maria José Palma Lampreia Dos Santos, Nowcasting and forecasting aquaponics by Google Trends in European countries // Technological Forecasting and Social Change, Volume 134. 2018. - S. 178-185. https://doi.org/10.1016/j.techfore.2018.06.002
5. Lean Yu, Yaqing Zhao, Ling Tang, Zebin Yang, Online big data-driven oil consumption forecasting with Google trends // International Journal of Forecasting. 2018. https://doi.org/10.1016/j.ijforecast.2017.11.005
6. Seung-Pyo Jun, Hyoung Sun Yoo, San Choi, Ten years of research change using Google Trends: From the perspective of big data utilizations and applications // Technological Forecasting and Social Change, Volume 130. 2018. S. 69-87. https://doi.org/10.1016/j.techfore.2017.11.009.
7. Sidhartha S. Padhi, Rupesh K. Pati, Quantifying potential tourist behavior in choice of destination using Google Trends // Tourism Management Perspectives, Volume 24. 2017. S.34-47. https://doi.org/10.1016/j.tmp.2017.07.001
8. Varun Dewan, Hartej Sur, Using google trends to assess for seasonal variation in knee injuries // Journal of Arthroscopy and Joint Surgery. 2018. https://doi.org/10.1016/j.jajs.2018.02.002
