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My findings this week show Google Search Console data is about 75% incomplete, making single-source GSC decisions dangerously unreliable.
1. GSC Used To Be Ground Truth
Search Console data used to be the most accurate representation of what happens in the search results. But privacy sampling, bot-inflated impressions, and AI Overview (AIO) distortion suck the reliability out of the data.
Without understanding how your data is filtered and skewed, you risk drawing the wrong conclusions from GSC data.
SEO data has been on a long path of becoming less reliable, starting with Google killing keyword referrer to excluding critical SERP Features from performance results. But three key events over the last 12 months topped it off:
- January 2025: Google deploys “SearchGuard,” requiring JavaScript and (sophisticated) CAPTCHA for anyone looking at search results (turns out, Google uses a lot of advanced signals to differentiate humans from scrapers).
- March 2025: Google significantly amps up the number of AI Overviews in the SERPs. We’re seeing a significant spike in impressions and drop in clicks.
- September 2025: Google removes num=100 parameter, which SERP scrapers use to parse the search results. The impression spike normalizes, clicks stay down.
On one hand, Google took measures to clean up GSC data. On the other hand, the data still leaves us with more open questions than answers.
2. Privacy Sampling Hides 75% Of Queries
Google filters out a significant amount of impressions (and clicks) for “privacy” reasons. One year ago, Patrick Stox analyzed a large dataset and came to the conclusion that almost 50% are filtered out.
I repeated the analysis (10 sites in B2B out of the USA) across ~4 million clicks and ~450 million impressions.
Methodology:
- Google Search Console (GSC) provides data through two API endpoints that reveal its filtering behavior. The aggregate query (no dimensions) returns total clicks and impressions, including all data. The query-level query (with “query” dimension) returns only queries meeting Google’s privacy threshold.
- By comparing these two numbers, you can calculate the filter rate.
- For example, if aggregate data shows 4,205 clicks but query-level data only shows 1,937 visible clicks, Google filtered 2,268 clicks (53.94%).
- I analyzed 10 B2B SaaS sites (~4 million clicks, ~450 million impressions), comparing 30-day, 90-day, and 12-month periods against the same analysis from 12 months prior.
My conclusion:
1. Google filters out ~75% of impressions.
- The filter rate on impressions is incredibly high, with three-fourths filtered for privacy.
- 12 months ago, the rate was only 2 percentage points higher.
- The range I observed went from 59.3% all the way up to 93.6%.
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