INTERPRETATION OF GOOGLE ANALYTICS DATA FOR DEVELOPMENT OF MARKETING STRATEGIES

Authors

DOI:

https://doi.org/10.32689/2523-4536/80-18

Keywords:

web analytics, Google Analytics, GA4, data-driven marketing, marketing strategy, digital transformation, user engagement, conversion optimization, behavioral segmentation

Abstract

The article examines the role of Google Analytics 4 (GA4) as an essential analytical tool for developing data-driven marketing strategies in the digital economy. The growing digitalization of business processes and the increasing competition in online markets have made the ability to interpret analytical data a critical component of strategic decision-making. The purpose of the study is to identify and characterize the GA4 metrics that provide marketers with reliable insights into user behavior, engagement, and conversion performance. The research applies analytical, comparative, and synthesis methods to determine how event-based data from GA4 can be transformed into practical marketing strategies. The study emphasizes that GA4 represents a shift from session-based to event-based data collection, allowing for more accurate analysis of user interaction across websites and mobile applications. This approach helps businesses understand not only how many users visit their digital platforms but also how they behave, what content retains their attention, and what actions lead to conversions. The findings show that systematic interpretation of metrics such as Users, Engaged Sessions, Engagement Rate, Average Engagement Time, Events, Conversions, Event Count per User, and Traffic Sources enables marketers to assess campaign effectiveness, identify audience segments, and detect weaknesses in the conversion funnel. The interpretation of these indicators provides opportunities for optimizing content strategies, improving user experience (UX), and increasing retention rates through personalized communication. By integrating GA4 analytics into strategic planning, companies can allocate budgets more efficiently, enhance targeting accuracy, and create predictive models of consumer behavior. The practical significance of the article lies in establishing a conceptual framework for transforming quantitative web analytics data into actionable marketing insights. The research outcomes can be applied to design adaptive and evidence-based marketing strategies that enhance brand competitiveness, improve customer engagement, and support sustainable growth in a data-driven business environment.

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Published

2025-11-24