CTR by Google Position: What Is a Normal Click-Through Rate?

CTR by Google position refers to reference curves that tell you what click-through rate an average search result earns in position 1, position 3 or position 7 in Google. They’re neither a law nor a goal, but a measuring frame: only once you know what’s normal for a position can you judge whether your page is under- or over-performing.

Below: what those curves look like, why to read them with caution, and how to build your own baseline. It belongs in the broader picture of what SEO really is and how you steer on results rather than on numbers.

What is a CTR benchmark by position?

Your click-through rate (CTR) is the percentage of searchers who click on your result after seeing it. Google Search Console defines CTR as clicks divided by impressions, and average position as the topmost spot your link held, averaged across queries.

A benchmark by position averages that percentage over a very large number of queries and groups it by ranking. The idea is simple: the higher you rank, the more people click. The top of page one captures the lion’s share of attention, and with every lower spot the CTR drops.

That decline is not linear but steep. The gap between position one and position two is large, the gap between two and three smaller, and from the bottom of page one the gap between positions becomes marginal. At the bottom of page one and on page two, only a fraction of the clicks remains. That is precisely why the first positions are so coveted: the curve rewards the top disproportionately hard.

Important: a benchmark is an average, not a prediction. Two results at exactly the same position can earn very different CTRs, depending on how convincing their title and meta description are. The curve says what an average result does; your result may deviate from it, and that deviation is exactly what you want to measure.

What does the curve roughly look like?

In broad strokes, you probably know this pattern already. The first organic position grabs the biggest share of clicks by far, and the drop after it is steep.

One large dataset makes that concrete. Backlinko’s analysis of 4 million Google results (Search Console data, charts dated 2022) found that the number one result gets 27.6% of all clicks and is about 10 times more likely to be clicked than result ten. Moving from position two to one gave a 74.5% relative increase in clicks, the biggest jump between any two neighbouring spots.

Translated graphically, that pattern looks roughly like this. The figures below are illustrative: they only show the shape of the curve, not a norm you must hit.

The exact figures vary from study to study, and that is where the first warning lies immediately. Different sources measure different numbers for the same position, because they use different queries, countries and periods. One industry has many brand searches with an extremely high CTR in position 1, another is full of comparative queries where people open several results. So take an external curve as a guide, not as absolute truth. The shape of the curve (a steep drop from the top) is universal; the precise percentages are not.

A second nuance: search intent shifts the whole curve. For a navigational query where someone is looking for a specific brand, the right result captures almost all the clicks. For a broad informational question, the clicks spread far more evenly across several results. The same position therefore means something different depending on what the searcher actually wants. If you want to understand that more deeply, read how you read search intent before judging a page.

Why you should not chase the exact number

It is tempting to take a benchmark as a goal: “position three should earn X percent, so that is what I am aiming for.” That is precisely the wrong way to use the curve. The number itself is not steerable, because it depends on too many factors outside your page: the search type, the competition in the SERP, the number of ads above it, and increasingly an AI Overview that soaks up attention at the top.

What is usable, though, is the deviation. Put the actual CTR of your page next to the benchmark for its position. If you are clearly below it, that is a signal that your snippet (title, meta, URL) is not living up to the promise, while your position does bring the traffic within reach. If you are above it, you are doing something right and you do not need to spend energy there. The benchmark is, in other words, a diagnostic instrument, not a scoreboard.

That aligns with how we look at SEO: as the acquisition layer of a single growth engine, optimized for pipeline and not for vanity numbers. A high CTR on a keyword that brings no customers is wasted effort. A CTR that follows our curve on a keyword that does buy weighs far more heavily. Our seo specialist therefore always uses benchmarks in combination with the question: does this traffic actually generate revenue?

How do you build your own baseline?

External curves are a starting point. Your real measuring frame you draw from Google Search Console, because that is the only source that measures your queries, your market and your results. Here is how you go about it:

1. Export your performance data. Pull the average position and the corresponding CTR per page and per keyword. Search Console calculates this automatically; you do not have to count anything manually.
2. Group by position range. Bundle your keywords into blocks: positions 1 to 3, 4 to 6, 7 to 10. Calculate your average CTR per block. That is your own curve, based on your industry and search behavior.
3. Look for the downward outliers. Pages that, within their position range, sit far below your own average are your opportunities. Here it pays to rewrite the title and meta.
4. Keep the search type separate. Split brand searches from non-brand searches. Otherwise your own brand name, which almost always clicks high, skews your average and makes every other page look like it is underperforming.

This own baseline is more reliable than any external study, because it filters out the noise of foreign industries and countries. External curves tell you how the web behaves on average; your own data tells you how your searchers behave.

What AI Overviews do to the curve

The classic CTR curve is shifting. AI Overviews, featured snippets and other SERP features increasingly answer the question above the organic results, so fewer people click through. The consequence: the whole curve sinks.

The numbers back that up. Pew Research tracked 900 US adults in March 2025: on searches with an AI summary, 8% of visits led to a click on a regular result, against 15% without one. Ahrefs measured 300,000 keywords and found a 34.5% lower position-one CTR when an AI Overview was present (March 2025). A CTR that looked low five years ago for position three can today be perfectly normal because an AI Overview takes the attention at the top.

How low it can go shows in our own CTR study of 5,460 B2B pages: a non-branded #1 position earned a 0.44% CTR there.

That has two practical consequences. First, external benchmarks age quickly. A curve from a report a few years old probably overestimates what you earn today at each position. Second, your own recent baseline becomes even more important, because only that captures the current reality of your SERPs.

It also means that a declining CTR is not automatically a problem you must solve. Sometimes it is simply the new normal, and your energy is better spent becoming visible within those AI answers than chasing a click-through rate that structurally will not return. That is a trade-off that varies by keyword, and precisely the kind of decision a measuring frame helps you with: not every lower number deserves action.

A benchmark is a compass, not a destination

CTR by position is valuable as long as you use it for what it is meant for: a reference curve to measure your own performance against. It tells you where you are underperforming and where you had better not touch anything. It does not tell you what percentage you “should” hit, and it is not a goal in itself.

The order is therefore: start with an external curve to understand the shape, then build your own baseline from Search Console, and steer on the deviations tied to keywords that bring customers. That way a benchmark becomes an instrument that serves pipeline instead of a number you chase.

Want to know how your CTR by position compares to a realistic curve, and which pages leave the most potential untapped for you? We build the measuring frame with you and also tell you plainly when a lower CTR is simply the new normal. Contact us and we will look at your real numbers.

Frequently asked questions about CTR by position

What is a normal CTR for position 1 in Google? In the largest public study, position one gets about 27.6% of clicks, roughly ten times position ten, but the exact percentage varies strongly by search type and industry. Brand searches earn far more than broad informational questions. Use external figures as a guide and build your real norm from your own Search Console data.

Are CTR benchmarks by position reliable? As a reference for the shape of the curve, yes: ranking higher always brings more clicks. The exact percentages are less reliable, because every study measures different queries and periods, and AI Overviews have recently pushed the curve down.

How do I know if my CTR is too low? Put your actual CTR next to the average CTR for that same position in your own data. If you are clearly below it, your snippet is the problem. If you are in line or above, the page is performing just fine for its spot.

Why is my CTR dropping while my position stays the same? Often because of SERP features and AI Overviews that answer the question at the top already, so fewer people click through. That is increasingly the new normal and no sign of a weak title.

Further reading

• What is the Google Local Pack (3-pack) and how does it work?