Only half of your users have provided consent for analytics purposes, and you’re worried about reporting accuracy. What analytics feature can help you fill in the gaps and gain useful insights on the user journey from Analytics reports while respecting your users’ privacy?

Behavioral modeling is the correct answer because it uses Google's machine learning to estimate the activity of users who decline analytics cookies by analyzing the patterns of similar users who have provided consent. When a user denies consent, Google Analytics 4 (GA4) cannot use persistent identifiers to link their sessions or actions; however, if Consent Mode is implemented, the system receives "cookieless pings" containing non-identifying signals like device type, country, and event time. By training models on the observed data from consenting users and validating them against held-back data, behavioral modeling can fill in critical reporting gaps for metrics such as daily active users and session-to-conversion journeys. This allows marketers to maintain a comprehensive view of their site performance and user behavior while fully adhering to privacy regulations and respecting individual user choices.