Today’s highly competitive and dynamic market structure pressures marketing professionals to monitor consumer trends promptly. However, identifying trends lying in the depths of conventional media sources is an ambiguous task, and by using qualitative methods, trendspotters cannot detect main trends until they become large-scale epidemics. However, a decade of outspread in the coverage of information technology, especially broadband and mobile internet services, furnished trendspotters with an extraordinary measurable tool, the “internet searches”. As more and more individuals have gained access to the internet community, “the buyer’s decision process”. migrates to the web domain. Despite early studies’ interests only in forecasting or nowcasting performance of search trends, in this paper, our approach focuses on the power of Google Trends time series to define common latent trend clusters and the availability of these trend clusters in the early determination of emerging trends in consumer behaviour. These potential trendsetters prefer to use internet search queries in information gathering and evaluating alternative products at the stages of consumer decision-making. Firstly, we derived common consumer behaviour trends from Turkish internet search data. Then, we contribute to a concept by employing time series methods such as univariate trend analysis (unit root and time series decomposition) to state endogenous trend properties of common factors precisely. Using Dynamic Factor Analysis, I derived trend segments related to various Supermarket brands. Consequently, we turn to multivariate. Cointegration methods test the long-term and short-term validity of the relationship between market activity and captured trends. My intention in this two- Stage time series analysis was used to distinguish short-term fads in common consumer behaviour from emerging trends that have the prospect of becoming mainstream. Online Credit Card Transaction data published by the Central Bank of Türkiye is employed as a proxy for market activity. Finally, we fail to prove that sales for online search trends are significantly related to search trends.