What 5,298 Search Queries Taught Us: A Search Console Data Study From Our Own Site

One of our blog posts looked like a star performer for three months. Search Console showed 39,208 impressions and an average position of 2.9, which is close to the top of Google, yet the post got only 13 clicks. So we opened the query report to see what was going on. It was full of dozens of nearly identical searches that we'd never have expected a person to type, and every one had thousands of impressions with zero clicks. The page wasn't failing, because those numbers weren't describing people at all.

That made us put a plain question to all of our own Search Console data: how much of it can you trust? We run a free dashboard that reads Google Analytics and Search Console for other businesses, so this data is in front of us every day. This study draws only on our own site, from mid-August to early November, and nothing in it is estimated. We describe the method at the end so you can run the same check on your own numbers.

Key findings at a glance

Finding 1: most clicks come from queries you can't see

Over the period, the page report in Search Console counted 789 clicks, while the query report for the same dates accounted for only 218. That leaves 571 clicks, about 72%, coming from queries that Search Console doesn't list, because Google leaves anonymized queries out of query reports so that searchers keep their privacy.

The explanation is in the deep dive into Search Console performance data from Google: "Anonymized queries are those that aren't issued by more than a few dozen users over a two-to-three month period." Rare searches, in other words, are hidden. A site like ours, with a directory, a blog and many service pages, gets a huge share of its real searches from rare ones: long, specific questions, business names and unusual phrasings.

It's an uncomfortable lesson, but a useful one. Don't judge a page by the queries you can see, because a page with "no good keywords" in the query report can still receive steady visits through hundreds of uncommon and very specific searches. Treat the page report as your main measure of what earns clicks, and the query report as a sample.

Finding 2: 98% of queries never earn a click

Of the 5,298 queries that showed our site, only 100 brought even one click. The other 5,198 got no clicks at all, and 1,656 queries showed up exactly once. Google tries your pages against a huge range of searches, and most of those tries go nowhere. Saying "We rank for thousands of keywords" tells you very little by itself.

A site with hundreds of pages will show up, briefly and often far down the results, for thousands of searches nobody wrote it for. The thing to watch is a small group of queries, the ones that send people who might buy from you, and whether that group is growing. It's a bit like counting every person who strolled past your shop window and calling them customers.

Finding 3: position 1 can bring nothing

Queries averaging a position of 1 to 2.5 numbered 600, and together they gathered 25,651 impressions but just 5 clicks. Nearly all of them fit one odd pattern: the word "reddit", a subreddit name and "alternatives". They look machine-made, either automated or tool-generated, rather than typed by people, and they skewed the average position of one post.

Google defines average position as "the topmost position occupied by a link to your property or page in search results, averaged across all queries." On paper, positions 1 to 2 are the spot you'd want, but in our numbers that's where the oddest traffic sat.

Take "reddit r/business alternatives" (4,121 impressions, 0 clicks) and "reddit r/digitalmarketing alternatives" (3,289 impressions, 0 clicks). Dozens of near-identical variants turned up, and every one had thousands of impressions but zero clicks, and all of them pointed at our post on Reddit alternatives. We have no proof of who ran them. Still, real people rarely type something shaped like a page title thousands of times without clicking once. Whatever the cause, the post's average position came out at 2.9, while a search real people use, such as "reddit alternatives", was at roughly position 14, with only 167 impressions.

Had we judged that post by average position, we'd have called it our best-ranking page. Measured by the clicks of real searchers, it sat on page two and had room to grow.

Finding 4: page two still brings real visitors

Queries averaging position 11 to 20 numbered 348, and they brought 45 clicks from 4,015 impressions, a 1.1% click-through rate. That was higher than any top-10 band in our data, because these were real searches from people who meant it, such as "list your business for free" (6 clicks from 45 impressions).

Some of the best were directory searches. "list your business for free" got 6 clicks, "free directories to list your business" got 3 clicks, and "list your business" got 3 clicks from 1,193 impressions. Our free business directory was built for exactly these visitors. They found us on page two, so page two clearly isn't invisible, and lifting these pages a few spots is the SEO work most worth our time. Our guide to striking-distance keywords walks you through finding and improving queries like these.

Finding 5: brand searches convert best

Queries with our name in them brought 64 clicks from 333 impressions, a 19.2% click-through rate, far above anything else. People who search for a business by name usually click, and everything that gets them to type your name feeds that number, whether it's reviews, referrals or social posts.

That's why brand searches work as a health check. When they climb, people are finding and remembering you more. When they stay flat even as you spend more on marketing, that spending isn't building recognition.

Finding 6: the export limits hide data too

Queries that aren't anonymized can still get cut off. Per Google, the Search Console interface stops at 1,000 rows per export, while the Search Analytics API and the Data Studio connector (formerly Looker Studio) each hand back as many as 50,000 rows a day for each site and each search type. A small export misses the long tail.

Google's performance data deep dive puts it this way: "the maximum you can export through the Search Console user interface is 1,000 rows of data." If you analyse queries from a spreadsheet exported in the interface, you only see the top slice. To get the full picture we used the API to pull everything, and our ONE Dashboard gets its numbers the same way.

What to do with your own Search Console data

1. Use the page report as your main measure. A large share of clicks never shows up in the query report.
2. Look for odd patterns. Near-identical phrasings with high impressions and zero clicks usually aren't people, so leave them out when you judge rankings.
3. Focus on real-intent queries in positions 8 to 20. These are your closest wins.
4. Grow brand searches. They convert at many times the rate of everything else.
5. Export through the API or a connector for analysis. Exporting from the interface gives you 1,000 rows at most.
6. Follow the visitor into GA4. A click isn't a customer, and our post on GA4 vs Search Console covers why those two numbers never match.

When impressions are high and clicks are low, start by reading high impressions, low clicks. You can also connect your own data to the free ONE Dashboard and see how your own site breaks down.

What this means for AI search

These same patterns matter when you try to judge AI search. Google counts clicks from AI features within ordinary Search Console totals, so there's no way to separate them out. Your query report already hides most clicks, and average positions can be thrown off by unusual searches, so reading AI effects from small shifts in those numbers is guesswork.

A steadier approach follows from this study: watch page-level clicks over several months, watch brand searches, and follow visitors into GA4 to check whether enquiries stay steady. For independent research on the way AI summaries affect clicks, see our AI search statistics.

Limits of this study

Our numbers come from a single site over 90 days, so the exact percentages are specific to us and you shouldn't expect them to hold for everyone. If your site has fewer pages and its searches are more popular, the hidden share is probably smaller, but a site with a big directory or lots of long-tail pages could see a larger one. We also don't know for certain who ran the odd Reddit-style searches, only that they behaved nothing like real visitors. So use what we found as a method, plus a few warning signs to look for in your own data, and don't treat the figures as benchmarks.

How to repeat this study on your own site

None of this takes special tools. Start by picking a 90-day period. Open the Performance report, remove any filters, and write down the total clicks. Next, export your queries for those same dates through the API or a connector, add up their clicks, and compare the two totals, because the difference between them is the share that's hidden. After that, sort queries into bands of average position (1 to 3, 4 to 10, 11 to 20, 21 and beyond) and work out each band's click-through rate. Last, put them in order of impressions and go through the first 50 by eye. Odd patterns usually jump out within a minute.

Method

Data source: Google Search Console, domain property greatinflux.com, web search, covering the 90 days between mid-August and early November. We pulled page totals and query totals separately for the same dates through the Search Console API. Each query's average position decides its position band, and brand queries are the ones containing "greatinflux". A single site makes a small sample, so take the patterns as the lesson rather than the exact percentages.