Google Adds 2 New Metrics To GA4 Reports

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Google is bringing two brand-new metrics to GA4 homes that online marketers can utilize when developing customized reports.

Google Analytics is adding two brand-new metrics to GA4 residential or commercial properties that supply more insight into the number of pages visitors view and

the length of time they remain. Views per session and average session period are now available in Explorations and Reporting Personalization in

GA4. Views per session tracks the number of app screens or web pages people look at throughout a single visit, while typical session period determines the time users spend on the site.

A statement from Carly Boddy, Product Supervisor at Google Analytics, demonstrates how to add these metrics when constructing customized reports:

If you track views per session and typical session duration for Universal Analytics (UA) homes, there are a couple of essential differences to know regarding GA4.

You’re most likely to see a difference in session counts, which can vary from company to business based on the following elements:

  • Location: UA homes count a new session at midnight even if the user hasn’t left, which implies session counts may be greater than GA4. Consider the time zones of your users and how likely they are to cross the midnight limit to reboot a session.
  • UTM Tagging: UTM tagging on your website will reset the session in UA, which indicates you may see a much higher count of sessions in UA than in GA4.
  • Filters: The data in UA reporting may be subject to see filters that exclude information. The data in GA4 reporting for GA360 clients may go through filters that specify which information from a source home appears in a sub-property.
  • Estimate: GA4 homes utilize an analytical quote of the number of sessions on your website or app by estimating the number of distinct session IDs, while UA residential or commercial properties do not estimate the variety of sessions. GA4 homes more effectively count sessions with high precision and low mistake rate.

Source: @carly_boddy on Buy Twitter Verification
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