![]() ![]() When opting into either Signals or Ad Personalisation, we recommend that you consider which privacy regulations your company must adhere to. Be careful from a privacy perspective here. ![]() Data Sharing Between Google Products – You can share GA4 data with other tools in the Google ecosystem, in particular Google Signals and Ad Personalisation. ![]() If a 14-month data retention period is too limited for the types of long-term comparison analysis you run, you can always save the data for longer in tools such as BigQuery. The limited retention timeframes in effect force your GA4 setup to retain data for less time, pushing you toward compliance with GDPR and other data privacy policy laws focused on ensuring you only retain user data for as long as you are making use of it. GA4 simplifies this with only two options: two months or 14 months. Data Retention – In Universal Analytics you could configure data retention to a series of timeframes from 14 months minimum to a “do not automatically expire” maximum.Starting a new Google Analytics 4 implementation offers you the opportunity to configure your GA4 tags from the start using Consent Mode to ensure your tracking responds accurately to users’ opt-in/out decisions. Consent Mode – Announced in 2020, the Consent Mode feature in Google Tag Manager allows you to configure your Google tags (Analytics and Ads) to respect users’ consent choices.This is a privacy-friendly update compared to Universal Analytics that tracked IP addresses by default. Anonymising IP – By default, GA4 anonymises IP addresses of all users.Below are a few changes in GA4 taking into consideration new privacy legal requirements: Although first-party cookies are still used for tracking, companies can use their own User IDs and integration with Google Signals for user identification. GA4 was designed to work in the future with or without cookies. In practice, this means that GA4 features support easier compliance with data privacy laws (Source). Google has built out GA4 based on the “data privacy by design” approach. If anomalies are found, Analytics Intelligence may identify granular user segments that demonstrate these anomalies. Analytics Intelligence regularly scans your data for anomalies in metrics. Contribution Analysis: This is a statistical technique used by Analytics Intelligence to identify user segments contributing to anomalies.User and conversion modelling: Analytics Intelligence powers Smart Goals, Smart Lists, Session Quality and Conversion Probability, which use machine learning to model conversions and can be used in building audiences.You can create up to 50 custom insights per property. When the conditions are triggered, you see the insights on the Insights dashboard, and you can optionally receive email alerts. Custom insights: You create conditions that detect changes in your data that are important to you.Automatic Insights: Emerging trends and unusual changes in your data will be detected and you will be notified automatically within the Anaytlics platform, on the insights dashboard. ![]() Analytics Intelligence provides two types of insights. For example, it can point out that a certain landing page is performing better than normal.
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