What is a CTR curve?
A CTR curve is the pattern of click-through rates across Google's ranking positions: how often searchers click the result in position 1, position 2, position 3 and so on. Plotted as a chart, it drops steeply from the top position and flattens out lower down the page, which is where the name comes from.
Click-through rate itself is clicks divided by impressions [1]. If a result is shown 1,000 times and clicked 80 times, its CTR is 8%. A CTR curve repeats that calculation for every position and lines the results up, so you can read off what share of searchers each position tends to win.
SEOs use CTR curves for two main jobs:
- Forecasting. "If this page moves from position 6 to position 2, how many more clicks will it get?"
- Weighting other metrics. Visibility scores and share of voice estimates usually multiply search volume by an expected CTR for each position, so a CTR curve sits inside many tools' headline numbers.
Why do published CTR curves disagree?
Published CTR curves disagree because each one averages a different set of searches, sites, devices and dates. There is no single CTR for "position 1". The figure depends heavily on what else is on the results page and what the searcher wanted.
Several things move CTR at a given position:
- What sits above the organic results. Ads, an AI Overview, a featured snippet or a map pack all take attention before the first ordinary link.
- Query type. A branded search for a company's own name sends most clicks to that company. An informational question may be answered on the results page with no click at all.
- Device. Mobile and desktop results pages look different, so the same position sits at a different point on the screen.
- Whose data it is. A curve built from a few large publishers will not match a curve built from small business sites.
Three widely cited curves show how much the source matters:
| Study | Data | Position 1 | Position 10 |
|---|---|---|---|
| Backlinko, 2019 | About 4 million results from Search Console accounts, supplied by Semrush | 27.6% | about 2.8% [5] |
| SISTRIX, 2020 | Over 80 million keywords, mobile only | 28.5% | 2.5% [6] |
| Advanced Web Ranking | Monthly since September 2015, from anonymized Search Console data on US results pages, now split by whether an AI Overview appears | Varies by month, intent and device [7] | Varies [7] |
The headline numbers look close, but SISTRIX's own breakdown shows position 1 ranging from about 13.7% on results pages with Google Shopping to 46.9% for a result with sitelinks [6]. The average hides a spread of more than three times.
This is why a CTR curve is widely quoted and poorly sourced. A figure such as "position 1 gets X% of clicks" travels from article to article with the dataset, date and device stripped off. Without those, the number cannot be checked or applied. Search Insider treats any CTR figure without a stated source, date, device split and query mix as unusable for forecasting.
How does Google Search Console count position?
Google Search Console counts position from top to bottom on the main side of the results page, with 1 as the topmost result, and reports the average of the topmost position a site held across impressions [1]. That definition matters for CTR curves because it is not the same as "the tenth blue link".
Google's documentation sets out several rules that change what a position means [1]:
- One element, one position. All items in a carousel share the carousel's position. All links in a knowledge panel share one position.
- Featured snippets typically occupy position 1.
- AI Overviews occupy a single position, and every link inside the AI Overview is assigned that same position.
- Topmost wins. If a site appears at positions 2, 4 and 6 for one query, Search Console counts that query as position 2.
The consequence is easy to miss. "Position 1" in Search Console can mean a link inside an AI Overview, a featured snippet or a classic organic result, and those earn very different click-through rates. A CTR curve built from Search Console data mixes all of them unless you separate them.
How do you build your own CTR curve?
You build your own CTR curve by grouping your Google Search Console queries by average position and dividing total clicks by total impressions in each group. Your own curve beats any published one because it reflects your site, your queries and your results pages.
The Performance report in Search Console gives clicks, impressions, CTR and average position for each query [2][3]. Then:
- Export the query data from the Performance report for a stable period, such as the last three months, which is the report's default view [2].
- Remove branded queries. Searches for your own name have far higher CTRs and will inflate the top positions.
- Group by rounded average position. Put queries at 1.0 to 1.4 into position 1, 1.5 to 2.4 into position 2, and so on.
- Sum, then divide. For each group, add up all clicks and all impressions, then divide clicks by impressions. Do not average the CTR column: a query with 10 impressions would count as much as one with 10,000.
- Check the counts. A position group with only a handful of queries will swing wildly. Note the number of queries and impressions behind every point on the curve, and treat thin groups as unreliable (see sample size).
Split the curve by device and by query type (informational, commercial, local) if you have the data. Separate curves are far more useful than one blended line.
What is the difference between CTR and a CTR curve?
CTR is a single measurement for one result, while a CTR curve is a set of average CTRs across positions. Your page's CTR for a keyword is a fact from your own data. A CTR curve is a model: it says what CTR a typical result at each position tends to get.
The distinction matters when diagnosing a page. If a page at position 3 has a CTR well below your own curve's figure for position 3, the title, description or the results page layout may be the reason. If it sits close to the curve, a better ranking, not a better snippet, is the more likely route to more clicks.
Is CTR a Google ranking factor?
Google has said many times that click-through rate is not used in its ranking algorithm [4]. CTR curves describe what happens after ranking: how clicks are shared out among results Google has already ordered.
That claim is still debated in the SEO industry, and Wikipedia's article on click-through rate records both Google's position and the debate [4]. For the purpose of a CTR curve, the debate does not matter much. The curve is a tool for estimating traffic, not a ranking signal.
How has the CTR curve changed?
The CTR curve has changed as Google has added results that sit above or among the organic links and occupy a position of their own. Google now documents that AI Overviews occupy a single position and featured snippets typically occupy position 1, so features that answer the query on the page can hold the top spot in reporting [1].
CTR as a metric is older than search. Wikipedia records that the first online display ad, run for AT&T on HotWired in 1994, achieved a click-through rate of 44%, and that banner ad CTRs have since fallen to around 0.1% [4]. CTR curves for organic search apply the same metric to ranking positions. The best-known organic curves date from 2019 and 2020 [5][6]. Since AI Overviews arrived, Advanced Web Ranking has split its monthly CTR data by whether an AI Overview appears on the results page [7], so a curve built before 2024 should not be applied to results pages that now carry one.
The practical lesson: a CTR curve has a date. A curve built before AI Overviews appeared on a query set does not describe that query set today.
What are the limits of CTR curves?
The main limit of a CTR curve is that it averages away the results page itself. Two queries at the same position can have completely different click-through rates because one has an AI Overview, ads and a map pack above it and the other has nothing.
Other limits:
- Average position is an average. A query that alternates between positions 1 and 9 reports a middling average position, and its CTR reflects neither.
- Personalization. Google's own documentation notes that results are specific to the time, place, device and recent history of the searcher [2], so a position in a rank tracker is not always the position a real searcher saw.
- Zero-click searches. When the answer appears on the results page, a low CTR at position 1 can mean the result did its job without a click (see zero-click search).
When should you use a CTR curve?
Use a CTR curve for rough traffic forecasts and for spotting pages whose snippets underperform, and build the curve from your own data whenever you can. Use a published curve only when it states its source, date, device and query mix, and only for broad planning.
Do not treat a CTR curve forecast as a promise. A move from position 5 to position 2 will usually increase clicks, but the size of the gain depends on the results page for that specific query. Check what the page actually looks like before multiplying search volume by a number from a chart.