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Internal Traffic

GA4 internal traffic is company visit data that should be excluded from analysis, including employee and development team IPs. Define IP ranges in the data stream tag configuration and then exclude them from the properties using data filters.

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If you don't exclude internal traffic, site visits by employees or developers will be mixed in with actual user data, reducing the quality of your analysis. In particular, small sites have a high rate of employee visits, which can lead to severe data distortion.

The procedure for setting internal traffic filters in GA4 is as follows. First, create internal traffic definitions and register the IP range in Management → Data Stream → Web Stream → ‘More Tag Configuration’. Then, test and activate the ‘Internal Traffic’ filter in Management → Data Settings → Data Filter.

In cases where the IP is not fixed, such as working from home or in a VPN environment, IP range filters alone have limitations. In this case, consider using DebugView or operating development properties separately.

Key takeaways

  • Define internal traffic by IP address or CIDR range in Management → Data Stream → Tag Configuration.
  • In the data filter, apply the 'Exclude internal traffic' filter in the order of test → active.
  • In a home/VPN environment without a fixed IP, the effectiveness of IP-based filters is limited.
  • Internal traffic events are automatically assigned the traffic_type parameter value 'internal'.
  • Before activating the filter, first check whether the definition is correct using a real-time report or DebugView.

References

Frequently asked questions

How do I manage my internal IP if it changes frequently?

Register the router's public IP range in CIDR notation, or register the VPN exit IP if you use a company VPN. If complete management is difficult, you can separate internal test traffic by connecting the staging environment to a separate GA4 property.

What is the impact of not excluding internal traffic?

It depends on the size of the site and number of employees. If employees visit a small site dozens of times a day, the bounce rate and dwell time in actual user data can be greatly distorted. In particular, when test events accumulate during development, the event count data is also affected.

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