Optimizing customer journey through advanced analytics techniques over Google Analytics 4 data in Google BigQuery

Abstract

In a highly competitive, data-driven marketplace, optimizing customer journeys is essential for businesses. This paper examines the combination of advanced analytics techniques with Google BigQuery’s data warehousing capabilities, utilizing data from Google Analytics 4 (GA4). GA4 provides a comprehensive view of user interactions across platforms, but extracting actionable insights requires a robust data infrastructure. Google BigQuery’s scalable architecture supports real-time analysis of massive datasets, offering valuable insights into customer behavior. This research explores methodologies such as sequence analysis, network analysis, and clustering to analyze customer journeys and enhance marketing strategies. Our technical contributions include the development of a scalable ELT pipeline using Dataform for processing GA4 data, the implementation of optimized star schema design for enhanced query performance in BigQuery, and the integration of advanced analytics techniques, such as sequence, cluster, and network analysis, to drive actionable insights and improve decision-making accuracy. Through practical implementations and real-world examples, the study demonstrates the effectiveness of this integration. Key findings show sequence analysis improves purchase flow, network analysis identifies product relationships, and clustering analysis enables customer segmentation for targeted marketing. The paper concludes with recommendations for businesses to fully leverage GA4 data, improving user experiences and fostering sustainable growth.