Report
Unlock high-value users with machine learning.
A practical guide for brand marketers to rapidly turn first-party GA4 data into high-value, personalized audiences using iBQML.
The key to better audience targeting is already within your Google Analytics data.
In today’s competitive landscape, poor audience targeting drains marketing resources and erodes trust. Personalization—delivering timely, relevant messages based on real-time behaviors—has become essential. The challenge is activating your valuable first-party data to create tailored experiences that align with users' real-time behaviors without requiring deep technical investment.
In this article, Brianna Mersey, VP of Data for North America, demonstrates how brands can operationalize first-party data with Google’s Instant BigQuery Machine Learning (iBQML) to identify high-value segments and tailor messaging at scale. Built to be accessible to marketers and analysts with foundational SQL, iBQML sits inside BigQuery and uses GA4 data to train simple propensity models, outputting scores that segment users into low, medium, and high propensity for a chosen KPI. While not a full ML stack, it provides a practical gateway to ML-powered personalization, with clear trade-offs around model variety, tuning, and cost at scale.
Discover how this powerful, accessible framework allows marketing and analytics teams to deploy propensity models that predict high-value customer actions, such as the likelihood to purchase or churn. Mersey provides a roadmap for moving beyond manual segmentation to an automated, ML-driven approach that refines targeting, improves conversion efficiency and fosters long-term loyalty through personalized campaigns at scale.
She also discusses the technology’s limitations, helping teams should plan for governance, cost and a phased rollout. The payoff is a faster, more targeted path to personalized experiences, with Lookalike and remarketing opportunities enabled by the high-propensity segment.
Ultimately, iBQML lowers the barrier to ML-enabled targeting for teams without deep data science capacity, helping brands unlock more relevant interactions, reduce waste, and improve marketing ROI.
Read the full article to see the framework in action and to understand whether it is the right entry point for your organization's journey into advanced, data-driven marketing.
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This article was originally published in the leading journal Applied Marketing Analytics by Henry Stewart Publications.
