What We Learned About Google Organic Non-Branded Traffic Calculation and Why It Might Be Flawed

Google Search Console welcome screen: Improve your performance on Google Search
Table of contents:

The Challenge: Google Search Console Sampling and Inaccurate Traffic Calculation

When analyzing organic traffic, distinguishing between branded and non-branded traffic is key for understanding your SEO performance. Google Search Console (GSC) provides valuable insights, but its sampling method poses a challenge. GSC only shows a subset of total queries, and this sampling can lead to incomplete data, especially when trying to calculate the split between branded and non-branded traffic.

Why Filtering Queries in GSC Leads to Inaccurate Results

To calculate branded vs. non-branded traffic in GSC, many SEOs rely on filtering queries:

  • Branded queries: Including the brand name or variations.
  • Non-branded queries: Excluding the brand name.

However, because GSC samples data, this method introduces errors:

  • The branded filter shows only a subset of branded queries.
  • The non-branded filter removes branded queries but still suffers from incomplete sampling.

This results in a lower sum of branded and non-branded clicks, often much lower than the total clicks reported by GSC, which causes discrepancies in the analysis.

Example: How GSC’s Sampling Affects the Traffic Calculation

Let’s look at a real example to see how the filtering method causes inaccuracies:

  • Total clicks reported by GSC: 188K
    Search Console performance totals before filtering: 188K clicks, 8.39M impressions, 2.2% CTR, average position 19.2
  • Branded clicks (via filter): 32K
    Search Console with a branded query filter: 32K clicks, 107K impressions, 29.9% CTR, average position 3.4
  • Non-branded clicks (via filter): 85.5K
    Search Console with a non-branded query filter: 58.5K clicks, 3.76M impressions, 1.6% CTR, average position 22.2

If the filtering method worked accurately, the sum of branded and non-branded clicks would equal the total: 32K + 85.5K = 117.5K

But GSC reports 188K clicks, leaving a 70.5K gap unaccounted for. This gap occurs because GSC samples data and excludes certain queries in its filters, resulting in incomplete insights.

The Better Way: A More Accurate Method for Estimating Non-Branded Traffic

For medium-to-large websites, where there’s a significant gap between total clicks and sampled query clicks in GSC, a more reliable approach is to use the total click count and subtract branded clicks. This method ensures a more accurate estimation, particularly for larger websites where sampled query data may not reflect the full picture. Here’s how:

  1. Extract Total Clicks from GSC for a given period (without filtering).
  2. Extract Branded Clicks using a regex filter for all variations of the brand name.
  3. Estimate Non-Branded Traffic by subtracting branded clicks from total clicks:
    Total Clicks − Branded Clicks = Non-Branded Clicks

Chart of branded vs. non-branded organic traffic from Google, Aug 2023 to Dec 2024, with the monthly share of each

Why This Method Works Better

  • Branded queries are usually well-represented in the sample: Branded searches tend to have higher click volumes, so they’re more likely to be included in the sample.
  • Accurate total clicks: GSC reports total clicks accurately, even though query-level data is sampled.
  • No reliance on filtering: Subtracting branded clicks from the total avoids the issues introduced by query-based filtering and gives a more complete picture.

Key Takeaways

  • Sampling in GSC leads to incomplete data, making it difficult to accurately calculate branded vs. non-branded traffic.
  • Subtracting branded clicks from the total is a better method for estimating non-branded traffic, avoiding the pitfalls of query filtering.
  • Understanding the split between branded and non-branded traffic is essential for refining SEO strategies and measuring brand performance accurately.

By following this approach, SEOs can gain a clearer understanding of organic traffic distribution and avoid being misled by the limitations of GSC’s sampled data.