The Facts About Google Click Signals, Rankings, And SEO

Clicks as a ranking-related sign have been a topic of debate for over twenty years, though these days most SEOs perceive that clicks are usually not a direct rating issue. The easy reality about clicks is that they’re uncooked knowledge and, surprisingly, processed with some similarity to human rater scores.

Clicks Are A Uncooked Sign

The DOJ Antitrust memorandum opinion from September 2025 mentions clicks as a “uncooked sign” that Google makes use of. It additionally categorizes content material and search queries as uncooked indicators. That is vital as a result of a uncooked sign is the lowest-level knowledge level which is processed into increased degree rating indicators or used for coaching a mannequin like RankEmbed and its successor, RankEmbedBERT.

These are thought-about uncooked indicators as a result of they’re:

• Straight noticed
• However not but interpreted or used for coaching knowledge

The DOJ doc quotes professor James Allan, who gave skilled testimony on behalf of Google:

“Indicators vary in complexity. There are “uncooked” indicators, just like the variety of clicks, the content material of an online web page, and the phrases inside a question.

…These indicators will be created with easy strategies, resembling counting occurrences (e.g., what number of instances an online web page was clicked in response to a selected question). Id.
at 2859:3–2860:21 (Allan) (discussing Navboost sign) “

He then contrasts the uncooked indicators with how they’re processed:

“On the different finish of the spectrum are modern deep-learning fashions, that are machine-learning fashions that discern complicated patterns in massive datasets.

Deep fashions discover and exploit patterns in huge knowledge units. They add distinctive capabilities at excessive price.”

Professor Allan explains that “top-level indicators” are used to supply the “ultimate” scores for an online web page, together with recognition and high quality.

Uncooked Indicators Are Information To Be Additional Processed

Navboost is talked about a number of instances within the September 2025 antitrust doc as recognition knowledge. It’s not talked about within the context of clicks having a rating impact on individal websites.

It’s known as a strategy to measure recognition and intent:

“…recognition as measured by person intent and suggestions techniques together with Navboost/Glue…”

And elsewhere, within the context of explaining why a few of the Navboost knowledge is privileged:

“They’re ‘recognition as measured by person intent and suggestions techniques together with Navboost/Glue’…”

Within the context of explaining why a few of the Navboost knowledge is privileged:

“Beneath the proposed treatment, Google should make accessible to Certified Rivals …the next datasets:

1. Consumer-side Information used to construct, create, or function the GLUE statistical mannequin(s);

2. Consumer-side Information used to coach, construct, or function the RankEmbed mannequin(s); and

3. The Consumer-side Information used as coaching knowledge for GenAI Fashions utilized in Search or any GenAI Product that can be utilized to entry Search.

Google makes use of the primary two datasets to construct search indicators and the third to coach and refine the fashions underlying AI Overviews and (arguably) the Gemini app.”

Clicks, like human rater scores, are only a uncooked sign that’s used additional up the algorithm chain to coach AI fashions to higher in a position match net pages to queries or to generate a high quality or relevance sign that’s then added to the remainder of the rating indicators by a rating engine or a rank modifier engine.

70 Days Of Search Logs

The DOJ doc makes reference to utilizing 70 days of search logs. However that’s simply eleven phrases in a bigger context.

Right here is the half that’s continuously quoted:

“70 days of search logs plus scores generated by human raters”

I get it, it’s easy and direct. However there’s extra context to it:

“RankEmbed and its later iteration RankEmbedBERT are rating fashions that depend on two important sources of knowledge: [Redacted]% of 70 days of search logs plus scores generated by human raters and utilized by Google to measure the standard of natural search outcomes.”

The 70 days of search logs are usually not click on knowledge used for rating functions in Google, AI Mode, or Gemini. It’s knowledge in combination that’s additional processed with the intention to prepare specialised AI fashions like RankEmbedBERT that in flip rank net pages based mostly on pure language evaluation.

That a part of the DOJ doc doesn’t declare that Google is immediately utilizing click on knowledge for rating search outcomes. It’s knowledge, just like the human rater knowledge, that’s utilized by different techniques for coaching knowledge or to be additional processed.

What Is Google’s RankEmbed?

RankEmbed is a pure language strategy to figuring out related paperwork and rating them.

The identical DOJ doc explains:

“The RankEmbed mannequin itself is an AI-based, deep-learning system that has robust natural-language understanding. This enables the mannequin to extra effectively determine the very best paperwork to retrieve, even when a question lacks sure phrases.”

It’s educated on much less knowledge than earlier fashions. The info partially consists of question phrases and net web page pairs:

“…RankEmbed is educated on 1/one hundredth of the info used to coach earlier rating fashions but gives increased high quality search outcomes.

…Among the many underlying coaching knowledge is details about the question, together with the salient phrases that Google has derived from the question, and the resultant net pages.”

That’s coaching knowledge for coaching a mannequin to acknowledge how question phrases are related to net pages.

The identical doc explains:

“The info underlying RankEmbed fashions is a mixture of click-and-query knowledge and scoring of net pages by human raters.”

It’s crystal clear that within the context of this particular passage, it’s describing using click on knowledge (and human rater knowledge) to coach AI fashions, to not immediately affect rankings.

What About Google’s Click on Rating Patent?

Means again in 2006 Google filed a patent associated to clicks referred to as, Modifying search consequence rating based mostly on implicit person suggestions. The invention is in regards to the mathematical formulation for making a “measure of relevance” out of the aggregated uncooked knowledge of clicks (plural).

The patent distinguishes between the creation of the sign and the act of rating itself. The “measure of relevance” is output to a rating engine, which then can add it to current rating scores to rank search outcomes for brand spanking new searches.

Right here’s what the patent describes:

“A rating Sub-system can embody a rank modifier engine that makes use of implicit person suggestions to trigger re-ranking of search outcomes with the intention to enhance the ultimate rating
introduced to a person of an data retrieval system.

Consumer choices of search outcomes (click on knowledge) will be tracked and reworked right into a click on fraction that can be utilized to re-rank future search outcomes.”

That “click on fraction” is a measure of relevance. The invention described within the patent isn’t about monitoring the clicking; it’s in regards to the mathematical measure (the clicking fraction) that outcomes from combining all these particular person clicks collectively. That features the Brief Click on, Medium Click on, Lengthy Click on, and the Final Click on.

Technically, it’s referred to as the LCIC (Lengthy Click on divided by Clicks) Fraction. It’s “clicks” plural as a result of it’s making selections based mostly on the sums of many clicks (combination), not the person click on.

That click on fraction is an combination as a result of:

• Summation:
  The “first quantity” used for rating is the sum of all these particular person weighted clicks for a selected query-document pair.
• Normalization:
  It takes that sum and divides it by the full rely of all clicks (the “second quantity”).
• Statistical Smoothing:
  The system applies “smoothing elements” to this combination quantity to make sure that a single click on on a “uncommon” question doesn’t unfairly skew the outcomes, particularly for spammers.

That 2006 patent describes it’s weighting formulation like this:

“A base LCC click on fraction will be outlined as:

LCC_BASE=[#WC(Q,D)]/[#C(Q,D)+S0)

where iWC(Q.D) is the sum of weighted clicks for a query URL…pair, iC(Q.D) is the total number of clicks (ordinal count, not weighted) for the query-URL pair, and S0 is a smoothing factor.”

That formula describes summing and dividing the data from many users to create a single score for a document. The “query-URL” pair is a “bucket” of data that stores the click behavior of every user who ever typed that specific query and clicked that specific search result. The smoothing factor is the anti-spam part that includes not counting single clicks on rare search queries.

Even way back in 2006, clicks is just raw data that is transformed further up the chain across multiple stages of aggregation, into a statistical measure of relevance before it ever reaches the ranking stage. In this patent, the clicks themselves are not ranking factors that directly influence whether a site is ranked or not. They were used in aggregate as a measure of relevance, which in turn was fed into another engine for ranking.

By the time the information reaches the ranking engine, the raw data has been transformed from individual user actions into an aggregate measure of relevance.

• Thinking about clicks in relation to ranking is not as simple as clicks drive search rankings.
• Clicks are just raw data.
• Clicks are used to train AI systems like RankEmbedBert.
• Clicks are not directly influencing search results. They have always been raw data, the starting point for systems that use the data in aggregate to create a signal that is then mixed into ranking decision making systems at Google.
• So yes, like human rater data, raw data is processed to create a signal or to train AI systems.

Read the DOJ memorandum in PDF form here.

Read about four research papers about CTR.

Read the 2006 Google patent, Modifying search result ranking based on implicit user feedback.

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