Are You Misreading Google’s Click Signals—And Hurting Your SEO Rankings?

If you’ve ever stared at Search Console CTR like it’s a mood ring for Google, you’re not alone. A lot of SEO advice still treats “more clicks” like a magic spell for rankings. But the September 2025 DOJ antitrust memo paints a way less dramatic (and way more useful) picture: clicks aren’t the boss of your rankings—they’re the raw ingredients. And raw ingredients don’t taste like dinner until Google cooks them into something else. Let’s talk about what that memo actually says, where Navboost/Glue and RankEmbed models fit, and why chasing click tricks is a great way to burn time (and maybe your site).

Clicks aren’t a “ranking factor” the way you think—they’re raw input

If clicks feel like a magic lever, it’s because we’ve all watched CTR go up and wanted to believe the ranking fairy noticed.

But the September 2025 DOJ antitrust memo describes clicks in a much less mystical way: clicks are a “raw” signal—right down at the “counting stuff” layer—alongside the content of a web page and the terms within a query . Raw means “observed,” not “judged.” Like flour. Nobody eats flour and calls it cake.

The memo even spells out what “raw” can look like in practice: counting occurrences, like “how many times a web page was clicked in response to a particular query” . That’s tracking. That’s logging. That’s data collection. It’s not the same as “Google saw 12 extra clicks and moved you from #7 to #3.”

Raw signals vs “top-level signals” (the stuff that actually affects scoring)

The same testimony contrasts raw signals with the systems that do the heavy lifting: deep-learning models that find patterns in big datasets . And it notes that “top-level signals” are what get used to produce the final scores for a page—things like popularity and quality .

So when someone says “CTR is a ranking factor,” what they often mean (without realizing it) is:

1. Google can observe click behavior (raw signal)

2. Google can process it into something more stable/usable (signal/model training)

3. That processed output can influence retrieval/ranking systems up the chain

That’s a totally different claim than “increase CTR and rankings go up.”

A relatable gut-check: the rage-click doesn’t crown you king

Let’s say one person searches a keyword, angrily clicks your result, bounces in two seconds, comes back, clicks the next result, and stays. Even if you “won” a click, that single action is still just a raw datapoint. On its own, it’s noisy. People misclick. People scroll. People get distracted.

That’s why Google talks about systems that interpret patterns at scale, not one-off clicks acting like a ranking cheat code .

So yes—Google tracks clicks (no surprise). The mistake is thinking your Search Console CTR is a direct dial you can twist for SEO rankings. It’s better to treat CTR as a symptom: your snippet either matches the query intent and feels trustworthy… or it doesn’t.

Where Navboost/Glue fits: popularity + intent feedback, not a CTR vending machine

Okay, so clicks are raw input. Cool. But where do they go?

One place the DOJ memo points to is Navboost/Glue, described as “popularity as measured by user intent and feedback systems including Navboost/Glue” . That phrasing matters. It’s not “CTR wins.” It’s more like: Google has systems watching what people seem to prefer for a given query, then folding that into a bigger picture of what searchers actually meant.

Popularity isn’t the same thing as “high CTR”

If Navboost/Glue were a CTR vending machine, SEO would be easy:

• write a spicy title

• get a few extra clicks

• print rankings

But the memo doesn’t frame Navboost as “clicks make you rank.” It’s referenced as popularity data, and not in the context of “clicks having a ranking effect on individual sites” .

That lines up with what you already see in real SERPs: some queries reward freshness, some reward brand trust, some reward deep how-to content, and some reward a tool or template. A generic CTR bump doesn’t solve the wrong format.

A practical gut-check (for anyone stuck in title-tag tweak mode)

If your SEO plan is basically “rewrite titles until CTR pops”, you’re optimizing for the moment someone clicks, not the moment they silently think, “Yep, this solved it.”

Try this instead—quick, unglamorous, works way better:

1. Name the intent in plain English (learn / compare / buy / fix / find a specific site).

2. Match the content type to the intent (guide, list, product page, calculator, definition, local page).

3. Match the promise in the snippet to the first 10 seconds on-page (same wording, same angle, no bait-and-switch).

Because if Navboost/Glue is measuring popularity through user intent + feedback, the safest bet isn’t “more clicks.” It’s the right clicks from the right searchers, followed by “I’m done searching now.”

RankEmbed + RankEmbedBERT: clicks and raters show up as training food, not a ranking joystick

If Navboost/Glue is the “what do searchers seem to like?” side of the story, RankEmbed is the “how does Google understand language well enough to pick the right pages?” side.

The DOJ memo describes RankEmbed as an AI-based, deep-learning system with strong natural-language understanding—good at identifying the best documents even when the query doesn’t contain all the right words 【】. That alone should make SEOs pause before they obsess over exact-match phrasing like it’s 2012.

What RankEmbed is (in plain English)

RankEmbed is built to get better at matching queries to documents based on meaning, not just shared keywords.

The memo’s description is pretty direct: it helps Google “more efficiently identify the best documents to retrieve, even if a query lacks certain terms” .

So if your page is genuinely the best answer, you’re not doomed because the searcher didn’t type your exact headline.

What it’s trained on (this is where clicks come back in)

Here’s the key bit for the “are clicks a ranking factor?” debate: the data underlying

RankEmbed models is described as a combination of click-and-query data and scoring of web pages by human raters .

That wording points to training and quality measurement, not a “click button = rank up” mechanic.

Think of it like teaching:

• Click-and-query data helps show patterns about what people tend to choose for a given search.

• Human rater scores add a layer of judgment about quality (helpful, satisfying, trustworthy… the stuff people argue about in SEO all day).

Together, those inputs help Google train systems to recognize what “good results” look like at scale .

What this changes for your SEO playbook

If clicks and rater feedback are part of how models learn, your job isn’t to manufacture applause. It’s to be the easiest “yes” on the SERP and the fastest “I’m done searching” once they land.

A simple checklist that’s boring (and effective):

1. Answer the query in the first screen (no throat-clearing intros).

2. Use language that matches how people ask (headings that mirror real questions).

3. Make the page scannable (tight sections, specific subheads, zero filler).

4. Back up claims where it matters (examples, sources, steps—anything that reduces doubt).

Because if Google’s training signals are built from aggregated behavior plus quality judgments, fake clicks are a sugar rush. Being the best match is the meal.

The “70 days of search logs” quote: what people repeat vs what it actually says

This is the part of the DOJ memo that got SEO Twitter acting like it found buried treasure:“70 days of search logs plus scores generated by human raters.”

It’s short. It sounds dramatic. It also gets repeated in a way that quietly changes the meaning.

What people repeat (and why it’s misleading)

You’ll hear versions like:

• “Google ranks pages based on 70 days of clicks.”

• “You only need 70 days of good CTR to win.”

• “Just juice click signals for a couple months.”

That’s a storytelling shortcut. And it turns a training input into a daily ranking rule.

What the memo is actually saying in context

The longer line matters. The memo frames it like this:

“RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [Redacted]% of 70 days of search logs plus scores generated by human raters … used by Google to measure the quality of organic search results.”

That’s not “your page gets a boost because your CTR was up last Tuesday.”

It’s describing what the models rely on as data sources—logs + rater scores—used to measure quality and (by implication) teach systems what “good” looks like at scale .

The doc even calls out the common confusion: the “70 days of search logs” line is a tiny excerpt, and the broader context is about how this data is used, not a direct claim that click data is being applied as a simple ranking switch .

Clean takeaway for SEO (no conspiracy board required)

Search logs can be used like gym footage.

• They help Google study patterns across lots of searches.

• They help train and evaluate ranking systems.

• They don’t mean your rankings are on a 70-day CTR timer.

So if you’re staring at Search Console thinking, “If I can just push CTR up for two months, Google will have to reward me,” you’re chasing the wrong win condition.

The better question is: Are the right people clicking, and do they stop searching after they hit my page?

The 2006 click-fraction patent: it’s all about aggregation, normalization, smoothing, and anti-spam

If the DOJ memo is the “here’s how Google talks about signals today” document, the old-school Google patent is the “yep, they’ve been thinking about click data like this for ages” document.

Back in 2006, Google filed a patent called Modifying search result ranking based on implicit user feedback. The big idea isn’t “track a click and bump a page.” It’s: track lots of clicks, turn them into a statistic, then use that statistic as a relevance input.

The click isn’t the signal. The processed click fraction is.

The patent describes click data being tracked and transformed into a “click fraction” that can be used to re-rank future search results .

That click fraction is basically a measure of relevance built from piles of user behavior, including things like short clicks and long clicks .

It’s even given a name in the write-up: LCIC (Long Click divided by Clicks) Fraction .

Why CTR manipulation is a weak long-term plan (the math is built to resist you)

The patent’s “click fraction” approach isn’t a straight average. It’s engineered to avoid getting fooled.

Here’s what’s happening under the hood, in human language:

• Summation: it adds up weighted clicks for a specific query → URL pair

• Normalization: it divides by the total clicks for that query → URL pair, so the number is relative, not raw

• Statistical smoothing (anti-spam): it applies a smoothing factor (S0) so a tiny amount of activity—like a single click on a rare query—doesn’t swing results unfairly

The patent even shows the “base” click fraction formula and calls out S0 as a smoothing factor .

So if your plan is to goose organic CTR with “one weird trick” (or a low-budget click campaign), you’re up against systems that expect noisy data and try to calm it down.

The SEO takeaway (the kind that saves you time)

If Google is using aggregated, normalized, smoothed behavior as a relevance hint, your best move isn’t fake excitement. It’s real satisfaction:

1. Match the query’s intent (what problem are they trying to solve?).

2. Earn the long click by answering fast and clearly.

3. Make the result feel “right” before the click (title + snippet that match what the page actually delivers).

You can still care about CTR. Just treat it like a feedback signal—not a cheat code.