Question from user:
Hello,
I read about how they count website hits differently but my numbers are way off. For example for Organic Search:
Monday: GA 136 vs GSC 54
Tuesday: GA 185 vs 69
Wednesday: 164 vs 79
Note: I do not have any Discover hits. I’m assuming GA would be more accurate (and that is what my ad server goes by) but it would be nice if GSC reflected my jump in traffic. Especially so I can see my other metrics for the missing traffic. So what am I missing in GSC?
Answer from Nabil:
The short answer is:
Your reported discrepancy between Google Analytics (GA) and Google Search Console (GSC) is normal and expected because these tools measure fundamentally different things: GA measures sessions or users on your website after a click, while GSC measures clicks from Google Search results to your website.
The significantly higher numbers in GA are likely due to the inclusion of branded organic traffic, repeat sessions from the same user, differences in how bot and spam traffic are filtered, and the general way each tool defines a “hit” or “click.” You aren’t missing anything in GSC; it’s just reporting a different, narrower set of data.
The long answer is:
It is a very common observation for organic search traffic to be much higher in Google Analytics than in Google Search Console, and the data you’ve shared reflects a typical disparity.
You are correct that GA and GSC count website hits differently, and understanding these differences is key to resolving the confusion.
Google Search Console’s “clicks” only include instances where a user physically clicks a link in the Google Search results page and successfully navigates to your site.
This is a very specific, pre-site-load measurement.
Conversely, Google Analytics’ “Organic Search” traffic is based on sessions or users that are tracked by the GA code after the page loads in the user’s browser.
There are several reasons for the large difference you are seeing.
First, GA includes repeat sessions from the same user that originated from organic search.
If a user clicks from Google, browses your site, leaves, and then comes back within a certain timeframe (usually 30 minutes) by typing in your URL directly or clicking a bookmark, GA may still attribute the second visit to the original Organic Search channel due to its default session settings, even though GSC would only count the initial click.
Second, GSC often filters out various bot and spam traffic more aggressively than GA, though GA has its own bot filtering.
Third, GA can experience tracking code failures due to things like ad blockers, privacy settings, or users leaving the page before the GA tracking code fully executes, which can cause GA numbers to be lower than GSC.
However, in your case, where GA is much higher, the session definition difference is the biggest factor.
To truly understand and reconcile these two critical datasets, the most effective solution is to combine and process them server-side using APIs and a unified data platform.
An excellent solution involves leveraging the Google Analytics Data API and the Search Console API to extract the raw, unaggregated data from both sources.
You would then use a platform like Stape’s server-side GTM or Google Cloud Platform to unify this data.
GTM would continue to collect client-side data like page_view and user_engagement with enhanced data layers.
This unified data set could then be fed into a data visualization tool using the Looker Studio API.
This setup allows you to create custom dashboards in Looker Studio where you can blend the two datasets on the same day and landing page.
For instance, you could calculate the precise percentage of repeat sessions by segmenting GA data, or you could create a “Click-to-Session Discrepancy” metric, which is otherwise impossible to see in either tool alone.
This method shifts the data processing from the limited, separate interfaces to a powerful, flexible server environment, giving you complete control over attribution and allowing you to see metrics for the “missing” traffic by accurately cross-referencing user and click data.
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