Search pattern analytics as a source of marketing trend forecasting

Анотація

The rapid pace of change in consumer interests in the digital economy outpaces the analytical capabilities of traditional systems, necessitating a transition from a reactive to a proactive model of marketing management. Using search patterns as a data source enables the detection of early signals of demand formation, creating new opportunities for building adaptive brand strategies, enhancing the efficiency of media planning, and predicting consumer behavior. The purpose of the article is to determine the essence, potential and practical directions of applying search pattern analytics to predict marketing trends in the digital economy, as well as to develop a conceptual model for integrating this data into the strategic marketing management system. The research methodology is based on a combination of systemic, synergistic, and cognitive-analytical approaches, which provide a comprehensive study of the relationship between user information behavior and market processes. Results. The properties of search data as behavioral predictors of changes in consumer interests are investigated. It was found that the inclusion of search indicators in forecasting models increases the accuracy of short-term demand estimates. It was found that the integration of analytical tools Google Trends, Google Analytics 4, SEMrush and Power BI forms a closed loop of «observation – analysis –forecast – correction», which provides a significant reduction in management response time and increases the accuracy of marketing decisions. It is proven that the systematic use of search analytics increases the efficiency of budgeting and the accuracy of targeting in digital marketing. Conclusions. It was found that search pattern analytics is an effective mechanism for increasing the accuracy of marketing forecasts and the adaptability of decisions. Key problems with its implementation were identified, including the incomplete representativeness of the data, the lack of standardized procedures for normalization, and ethical risks associated with the processing of behavioral data. Prospects for further research include the creation of hybrid intelligent models that combine search, social, and transactional analytics, as well as the development of a scientifically based Search Analytics Governance Framework aimed at ensuring the reliability, transparency, and responsible use of digital data in marketing activities.