The Facts About Google Click Signals, Rankings, And SEO via @sejournal, @martinibuster

Clicks as a ranking-related signal have been a subject of debate for over twenty years, although nowadays most SEOs understand that clicks are not a direct ranking factor. The simple truth about clicks is that they are raw data and, surprisingly, processed with some similarity to human rater scores.

Clicks Are A Raw Signal

The DOJ Antitrust memorandum opinion from September 2025 mentions clicks as a “raw signal” that Google uses. It also categorizes content and search queries as raw signals. This is important because a raw signal is the lowest-level data point which is processed into higher level ranking signals or used for training a model like RankEmbed and its successor, RankEmbedBERT.

Those are considered raw signals because they are:

Directly observed
But not yet interpreted or used for training data

The DOJ document quotes professor James Allan, who gave expert testimony on behalf of Google:

“Signals range in complexity. There are “raw” signals, like the number of clicks, the content of a web page, and the terms within a query.

…These signals can be created with simple methods, such as counting occurrences (e.g., how many times a web page was clicked in response to a particular query). Id.
at 2859:3–2860:21 (Allan) (discussing Navboost signal) “

He then contrasts the raw signals with how they are processed:

“At the other end of the spectrum are innovative deep-learning models, which are machine-learning models that discern complex patterns in large datasets.

Deep models find and exploit patterns in vast data sets. They add unique capabilities at high cost.”

Professor Allan explains that “top-level signals” are used to produce the “final” scores for a web page, including popularity and quality.

Raw Signals Are Data To Be Further Processed

Navboost is mentioned several times in the September 2025 antitrust document as popularity data. It’s not mentioned in the context of clicks having a ranking effect on individal sites.

It’s referred to as a way to measure popularity and intent:

“…popularity as measured by user intent and feedback systems including Navboost/Glue…”

And elsewhere, in the context of explaining why some of the Navboost data is privileged:

“They are ‘popularity as measured by user intent and feedback systems including Navboost/Glue’…”

In the context of explaining why some of the Navboost data is privileged:

“Under the proposed remedy, Google must make available to Qualified Competitors …the following datasets:

1. User-side Data used to build, create, or operate the GLUE statistical model(s);

2. User-side Data used to train, build, or operate the RankEmbed model(s); and

3. The User-side Data used as training data for GenAI Models used in Search or any GenAI Product that can be used to access Search.

Google uses the first two datasets to build search signals and the third to train and refine the models underlying AI Overviews and (arguably) the Gemini app.”

Clicks, like human rater scores, are just a raw signal that is used further up the algorithm chain to train AI models to better able match web pages to queries or to generate a quality or relevance signal that is then added to the rest of the ranking signals by a ranking engine or a rank modifier engine.

70 Days Of Search Logs

The DOJ document makes reference to using 70 days of search logs. But that’s just eleven words in a larger context.

Here is the part that is frequently quoted:

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

I get it, it’s simple and direct. But there is more context to it:

“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 and used by Google to measure the quality of organic search results.”

The 70 days of search logs are not click data used for ranking purposes in Google, AI Mode, or Gemini. It’s data in aggregate that is further processed in order to train specialized AI models like RankEmbedBERT that in turn rank web pages based on natural language analysis.

That part of the DOJ document does not claim that Google is directly using click data for ranking search results. It’s data, like the human rater data, that’s used by other systems for training data or to be further processed.

What Is Google’s RankEmbed?

RankEmbed is a natural language approach to identifying relevant documents and ranking them.

The same DOJ document explains:

“The RankEmbed model itself is an AI-based, deep-learning system that has strong natural-language understanding. This allows the model to more efficiently identify the best documents to retrieve, even if a query lacks certain terms.”

It’s trained on less data than previous models. The data partially consists of query terms and web page pairs:

“…RankEmbed is trained on 1/100th of the data used to train earlier ranking models yet provides higher quality search results.

…Among the underlying training data is information about the query, including the salient terms that Google has derived from the query, and the resultant web pages.”

That’s training data for training a model to recognize how query terms are relevant to web pages.

The same document explains:

“The data underlying RankEmbed models is a combination of click-and-query data and scoring of web pages by human raters.”

It’s crystal clear that in the context of this specific passage, it’s describing the use of click data (and human rater data) to train AI models, not to directly influence rankings.

What About Google’s Click Ranking Patent?

Way back in 2006 Google filed a patent related to clicks called, Modifying search result ranking based on implicit user feedback. The invention is about the mathematical formula for creating a “measure of relevance” out of the aggregated raw data of clicks (plural).

The patent distinguishes between the creation of the signal and the act of ranking itself. The “measure of relevance” is output to a ranking engine, which then can add it to existing ranking scores to rank search results for new searches.

Here’s what the patent describes:

“A ranking Sub-system can include a rank modifier engine that uses implicit user feedback to cause re-ranking of search results in order to improve the final ranking
presented to a user of an information retrieval system.

User selections of search results (click data) can be tracked and transformed into a click fraction that can be used to re-rank future search results.”

That “click fraction” is a measure of relevance. The invention described in the patent isn’t about tracking the click; it’s about the mathematical measure (the click fraction) that results from combining all those individual clicks together. That includes the Short Click, Medium Click, Long Click, and the Last Click.

Technically, it’s called the LCIC (Long Click divided by Clicks) Fraction. It’s “clicks” plural because it’s making decisions based on the sums of many clicks (aggregate), not the individual click.

That click fraction is an aggregate because:

Summation:
The “first number” used for ranking is the sum of all those individual weighted clicks for a specific query-document pair.
Normalization:
It takes that sum and divides it by the total count of all clicks (the “second number”).
Statistical Smoothing:
The system applies “smoothing factors” to this aggregate number to ensure that a single click on a “rare” query doesn’t unfairly skew the results, especially for spammers.

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

“A base LCC click fraction can be defined 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.

Featured Image by Shutterstock/Carkhe

https://www.searchenginejournal.com/the-facts-about-google-click-signals-rankings-and-seo/572827/

Google Ads Posts GEO Partner Manager Role via @sejournal, @MattGSouthern

Google’s Large Customer Sales team has posted a role titled “GEO Partner Manager, Performance Solutions” on Google Careers. The listing is a single job posting inside Google’s ads sales organization.

The term “GEO” appears seven times across the listing, including the title. “Generative Engine Optimization” is spelled out twice. Other references include “GEO players,” “GEO ecosystem,” and “GEO/AEO companies.”

The listing says the role will “shape the GEO ecosystem to prioritize Google surfaces.” Responsibilities include influencing partners to prioritize Google-owned surfaces in their tools and methodologies, as well as in “Share of Model” analysis. “Share of Model” is an industry term for a brand’s presence in AI-generated answers.

Why This Matters

The terminology is worth noting because it sits alongside a different public position from Google’s search side. In July, Google’s Gary Illyes said standard SEO is sufficient for AI Overviews and AI Mode, and that specialized AEO or GEO optimization is not needed. As of publication, Google has not publicly updated that guidance.

Large Customer Sales manages relationships with major advertisers and agencies. The role’s alignment with the 3P Measurement team places it firmly inside Google’s ad-side partner work.

Microsoft and Google are in different places here, and the categories of evidence differ. In March, Bing added “GEO” to its official webmaster guidelines, defining the term and placing it alongside SEO as a named category. Bing’s AI Performance dashboard, launched in February, was positioned as a step toward GEO tooling.

The Google listing is one job posting inside an ads sales team. Both are adoption signals, but not the same level of commitment.

Looking Ahead

The language reflects how one team inside Google’s ads organization frames this work today. It doesn’t carry the same weight as a documentation update, a public statement from Google Search, or a policy change.

Whether similar GEO language appears in other Google job listings across Ads, Cloud, or Search would indicate whether this is a pattern or a single team’s choice.

For brands working with GEO or AEO partners, the listing is worth noting. The listing indicates Google’s ads team wants partner tools and methodologies to prioritize Google surfaces.

Featured Image: Jack_the_sparow/Shutterstock

https://www.searchenginejournal.com/google-ads-posts-geo-partner-manager-role/572741/

AI Adoption Outpaced The PC & Internet: Dive Into The Stanford Report Data via @sejournal, @MattGSouthern

Stanford’s Human-Centered Artificial Intelligence Institute published its 2026 AI Index Report. The report runs over 400 pages across nine chapters covering technical performance, investment, workforce effects, and public sentiment.

The number getting the most attention is that Generative AI reached 53% adoption among the global population within three years of ChatGPT’s launch. That’s faster than either the personal computer or the internet reached comparable levels.

For anyone working in search, the report contains data that connects directly to the changes you’ve been navigating all year.

What The Report Found

This is the ninth annual AI Index, and it covers a lot of ground. A few findings matter most for the search industry.

In terms of capability, frontier models now exceed human performance on PhD-level science questions and in competitive mathematics. AI agents handling real-world tasks improved from a 20% success rate in 2025 to 77% today. Coding benchmarks that models struggled with a year ago are now nearly solved.

On investment, global corporate AI investment hit $581 billion in 2025, up 130% from the prior year. US private AI investment reached $285 billion. More than 90% of frontier models now come from private companies, not academic labs.

Regarding workforce effects, employment among software developers aged 22 to 25 has dropped by nearly 20% since 2024. A similar pattern appeared in customer service and other roles with higher AI exposure.

Transparency is declining. The Foundation Model Transparency Index fell from 58 to 40. The most capable models now disclose the least about their training data, parameters, and methods. Of the 95 most notable models launched last year, 80 were released without their training code.

The Adoption Number Everyone Is Citing

Understanding the 53% figure, what it includes, and what it doesn’t, matters for how you interpret it.

The comparison to PCs and the internet is based on research by the St. Louis Fed, Vanderbilt, and Harvard Kennedy School. The team compared adoption rates by years since each technology’s first mass-market product. The IBM PC launched in 1981. Commercial internet traffic opened in 1995. ChatGPT launched in November 2022.

At comparable points after launch, generative AI adoption runs well ahead of both earlier technologies.

But the comparison isn’t apples-to-apples, and the researchers said so themselves. Harvard’s David Deming pointed out that AI is built on top of PCs and the internet. People already had the hardware and the connectivity. Nobody needed to buy new equipment or wait for connectivity to reach their area. AI adoption rode on decades of prior technology investment.

Adoption numbers also vary depending on who’s counting and how. The Stanford report puts US adoption at 28%, ranking the country 24th globally. The St. Louis Fed’s own tracker puts US adoption at 54% as of August 2025. Same country, nearly double the rate, measured differently. The Fed team even revised its earlier estimate upward from 39% to 44% after changing the order of its survey questions.

“Adoption” also doesn’t distinguish intensity. Someone who signed up for a free ChatGPT account and tried it once counts the same as someone who uses it eight hours a day. The Stanford report notes that most users access free or near-free tiers. That’s a different picture than the one the headline number implies.

None of this means the adoption data is wrong. Generative AI is spreading faster than comparable technologies did at the same stage. But the speed of adoption alone doesn’t tell you how deeply it’s embedded in workflows or how much it’s changing search behavior specifically.

The Jagged Frontier

The report’s most useful concept for search professionals might be its “jagged frontier” of AI capability.

The same models that win gold at the International Mathematical Olympiad read analog clocks correctly only 50% of the time. IEEE Spectrum reported that Claude Opus 4.6 scores at the top of Humanity’s Last Exam while reading clocks at just 8.9% accuracy. Models that ace PhD-level science questions still struggle with video understanding and multi-step planning.

Ray Perrault, co-director of the AI Index steering committee, told IEEE Spectrum that benchmarks don’t map cleanly to real-world results. Knowing a model scores 75% on a legal reasoning benchmark “tells us little about how well it would fit in a law practice’s activities,” he said.

Search professionals have seen similar unevenness in AI search products. Ahrefs research showed that AI Mode and AI Overviews cite different URLs for the same queries, with only 13% overlap. Google’s Robby Stein acknowledged that the system pulls AI Overviews back when people don’t engage with them. Those signals suggest AI search performance is uneven across contexts, even if Google hasn’t fully explained where those differences are most pronounced.

Stanford’s data suggest that strong benchmark performance doesn’t guarantee reliable results across all tasks or query types. Whether that unevenness improves with future models is an open question the report doesn’t answer.

What’s Happening To Transparency

What the report says about transparency connects directly to search.

The Foundation Model Transparency Index dropped from 58 to 40 in a single year. The most capable models score lowest. Google, Anthropic, and OpenAI have all stopped disclosing dataset sizes and training duration for their latest models. 80 of the 95 most notable models launched in 2025 shipped without training code.

TechCrunch noted a disconnect between expert optimism about AI and public anxiety about it. The US reported the lowest trust in its government’s ability to regulate AI among the countries surveyed, at 31%.

For context on the index itself, a drop from 58 to 40 could indicate that companies are becoming more secretive. It could also reflect that the index penalizes closed-source models by design, and the most capable models happen to be closed-source. Both explanations can be true at the same time.

What matters for practitioners is the implication. The models powering AI Overviews, AI Mode, and ChatGPT Search are getting more capable and less explainable simultaneously. You’re optimizing for systems where the companies building them are sharing less about how they work, not more.

The report’s acknowledgments disclose that Stanford HAI receives financial support from Google, OpenAI, and others, and that the report was produced with assistance from ChatGPT and Claude.

The Entry-Level Question

Employment among software developers aged 22 to 25 dropped nearly 20% since 2024, according to the report. Older developers’ headcounts grew over the same period. A similar pattern appeared in customer service roles.

At first glance, that looks like AI replacing entry-level work. But the report included a caveat that complicates that conclusion. Unemployment is rising across many occupations, and workers least exposed to AI have seen it rise more than those most exposed.

That doesn’t rule out AI as a factor. It means the 20% decline could reflect AI displacement, broader hiring slowdowns, companies restructuring their entry-level hiring, or all three at once. The report presents correlation, not causation.

For search and content teams, the signal is directional even if the cause is mixed. The Stanford data is consistent with what the Tufts AI Jobs Risk Index showed earlier this year. Roles that involve assembling information from existing sources face more pressure than roles that require judgment, experience, and original analysis.

Why This Matters For Search Professionals

Even with its caveats, the adoption speed explains the pace of what you’ve been seeing.

Google expanded AI Overviews to 1.5 billion monthly users by Q1 2025. AI Mode reached 75 million daily active users by Q3 2025, then went global. Google expanded Search Live to 200+ countries. Personal Intelligence rolled out to free US users this year.

The adoption curve helps explain why Google has been expanding AI search features at this pace. It doesn’t tell us how much of that usage is happening inside search rather than standalone AI tools.

The “jagged frontier” means you can’t make blanket assumptions about AI search quality across query categories. A query type that returns accurate AI Overviews today might hallucinate with slight variations. Monitoring needs to happen at the query level, not the category level. Search Console doesn’t currently separate AI Overview or AI Mode performance from traditional search metrics, which makes this harder.

The decline in transparency affects how well you can understand why your content appears or doesn’t appear in AI-generated answers. When Google shares less about the models powering its search features, the feedback loop between what you publish and what gets surfaced becomes harder to read.

Shelley Walsh spoke at SEJ Live and referenced Grant Simmons, “golden knowledge” is content built on original data, firsthand experience, and depth that AI summaries can’t replicate from training data. The Stanford report’s data on adoption speed and model limitations support that position. The models are fast and widely used, but they’re uneven. Content that fills the gaps where AI is unreliable has a structural advantage.

What The Report Doesn’t Tell Us

The Stanford report doesn’t break out search-specific adoption data. We don’t know what percentage of that 53% uses AI via search specifically, rather than via ChatGPT, Gemini, or other standalone tools.

Google’s AI search usage numbers are limited. The company reported that AI Overviews reached 1.5 billion monthly users in Q1 2025, and AI Mode reached 75 million daily active users in Q3 2025. Updated figures should be included in the next earnings call.

The report also can’t tell us whether the jagged frontier problem is improving or worsening in search applications. The benchmark data shows models improving overall, but the clock-reading example shows that improvement isn’t uniform. Whether AI Overviews and AI Mode are getting more reliable for the specific queries that matter to your business requires your own monitoring, not aggregate benchmark data.

Looking Ahead

The Stanford report lands one week after Google’s March core update completed. Alphabet’s next earnings call will likely include updated AI search usage numbers.

The adoption data doesn’t predict what search will look like by year-end. But it does confirm that AI-first behavior isn’t speculative anymore. The question is whether Google’s AI search products will get reliable enough to match the pace of adoption.

Read More Resources:

Featured Image: n_a vector/Shutterstock

https://www.searchenginejournal.com/ai-adoption-outpaced-the-pc-internet-dive-into-the-stanford-report-data/572305/

Google Bans Back Button Hijacking, Agentic Search Grows – SEO Pulse via @sejournal, @MattGSouthern

Welcome to the week’s Pulse: updates affect what Google considers spam, what happens when you report it, and what agentic search looks like in practice.

Here’s what matters for you and your work.

Google’s New Spam Policy Targets Back Button Hijacking

Google added back button hijacking to its spam policies, with enforcement beginning June 15. The behavior is now an explicit violation under the malicious practices category.

Key facts: Back button hijacking occurs when a site interferes with browser navigation and prevents users from returning to the previous page. Pages engaging in the behavior face manual spam actions or automated demotions.

Why This Matters

Google called out that some back button hijacking originates from included libraries or advertising platforms, which means the liability sits with the publisher even when the behavior comes from a vendor.

You have two months to audit every script running on your site, including ad libraries and recommendation widgets you didn’t write yourself.

Sites that receive a manual action after June 15 can submit a reconsideration request through Search Console once the offending code is removed.

What SEO Professionals Are Saying

Daniel Foley Carter, SEO Consultant, summed up the community reaction on LinkedIn:

“So basically, that spammy thing you do to try and stop users leaving? Yeah, don’t do it.”

Manish Chauhan, SEO Head at Groww, added on LinkedIn that he was:

“glad this is being addressed. It always felt like a short-term hack for pageviews at the cost of user trust.”

Read our full coverage: New Google Spam Policy Targets Back Button Hijacking

Spam Reports May Now Trigger Manual Actions

Google updated its report-a-spam documentation on April 14 to say user submissions may now trigger manual actions against sites found violating spam policies. The previous guidance said spam reports were used to improve spam detection systems rather than to take direct action.

Key facts: Google may use spam reports to take manual action against violations. If Google issues a manual action, the report text is sent verbatim to the reported website through Search Console.

Why This Matters

Google now states that spam reports can be used to initiate manual actions, making reports explicitly part of its enforcement process in official documentation.

This also raises concerns about potential abuse, as grudge reports and competitor sabotage may become more appealing when reports have a tangible impact. Therefore, the true test will be the quality of reports that Google actually considers.

What SEO Professionals Are Saying

Gagan Ghotra, SEO Consultant, wrote on LinkedIn about why the change may lead to better reports:

“Now spam reports have direct relation to Google issuing manual actions against domains. Google announced if there is a spam report from a user and based upon that report Google decide to issue manual action against a domain then Google will just send the user submitted content in report to the site owner (Search Console – Manual Action report) and will ask them to fix those things. Seems like Google was getting too many generic spam reports and now as the incentive to report are aligned. That’s why I guess people are going to submit reports which have a lot of relevant information detailing why/how a specific site is violating Google’s spam policies.”

Read Roger Montti’s full coverage: Google Just Made It Easy For SEOs To Kick Out Spammy Sites

Agentic Restaurant Booking Expands In AI Mode

Google expanded agentic restaurant booking in AI Mode to additional markets on April 10, including the UK and India. Robby Stein, VP of Product for Google Search, announced the rollout on X.

Key facts: Searchers can describe group size, time, and preferences to AI Mode, which scans booking platforms simultaneously for real-time availability. The booking itself is completed through Google partners rather than directly on restaurant websites.

Why This Matters

Restaurant booking shows how task completion within search works. For local SEOs and marketers, traffic patterns shift: users now often stay within Google during discovery, with bookings routed through partners.

This depends on Google booking partners, which may limit visibility for restaurants outside those platforms, making presence on Google-supported booking sites more important than the restaurant’s own website. This model may or may not extend to other experiences.

What SEO Professionals Are Saying

Glenn Gabe, SEO and AI Search Consultant at G-Squared Interactive, flagged the rollout on X:

I feel like this is flying under the radar -> Google rolls out worldwide agentic restaurant booking via AI Mode. TBH, not sure how many people would use this in AI Mode versus directly in Google Maps or Search (where you can already make a reservation), but it does show how Google is moving quickly to scale agentic actions.

Aleyda Solís, SEO Consultant and Founder at Orainti, noted a key limitation in a LinkedIn post:

“Google expands agentic restaurant booking in AI Mode globally: You still need to complete the booking via Google partners though.”

Read Roger Montti’s full coverage: Google’s Task-Based Agentic Search Is Disrupting SEO Today, Not Tomorrow

Theme Of The Week: Google Gets Specific

What counts as spam, what happens when spam gets reported, and what agentic search looks like all got clearer definitions this week.

Back button hijacking becomes a named violation with an enforcement date. Google’s documentation now says spam reports may be used for manual actions, not just fed into detection systems. Agentic search becomes a live product for restaurant reservations in specific markets rather than a talking point about the future.

Now, the compliance work, reporting mechanics, and agentic experience are all clearly understood enough to be tracked directly, instead of just forecasted.

Top Stories Of The Week:

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/seo-pulse-google-targets-back-button-hijacking-agentic-search-grows/572282/

Google Bans Back Button Hijacking, Agentic Search Grows – SEO Pulse via @sejournal, @MattGSouthern

Welcome to the week’s Pulse: updates affect what Google considers spam, what happens when you report it, and what agentic search looks like in practice.

Here’s what matters for you and your work.

Google’s New Spam Policy Targets Back Button Hijacking

Google added back button hijacking to its spam policies, with enforcement beginning June 15. The behavior is now an explicit violation under the malicious practices category.

Key facts: Back button hijacking occurs when a site interferes with browser navigation and prevents users from returning to the previous page. Pages engaging in the behavior face manual spam actions or automated demotions.

Why This Matters

Google called out that some back button hijacking originates from included libraries or advertising platforms, which means the liability sits with the publisher even when the behavior comes from a vendor.

You have two months to audit every script running on your site, including ad libraries and recommendation widgets you didn’t write yourself.

Sites that receive a manual action after June 15 can submit a reconsideration request through Search Console once the offending code is removed.

What SEO Professionals Are Saying

Daniel Foley Carter, SEO Consultant, summed up the community reaction on LinkedIn:

“So basically, that spammy thing you do to try and stop users leaving? Yeah, don’t do it.”

Manish Chauhan, SEO Head at Groww, added on LinkedIn that he was:

“glad this is being addressed. It always felt like a short-term hack for pageviews at the cost of user trust.”

Read our full coverage: New Google Spam Policy Targets Back Button Hijacking

Spam Reports May Now Trigger Manual Actions

Google updated its report-a-spam documentation on April 14 to say user submissions may now trigger manual actions against sites found violating spam policies. The previous guidance said spam reports were used to improve spam detection systems rather than to take direct action.

Key facts: Google may use spam reports to take manual action against violations. If Google issues a manual action, the report text is sent verbatim to the reported website through Search Console.

Why This Matters

Google now states that spam reports can be used to initiate manual actions, making reports explicitly part of its enforcement process in official documentation.

This also raises concerns about potential abuse, as grudge reports and competitor sabotage may become more appealing when reports have a tangible impact. Therefore, the true test will be the quality of reports that Google actually considers.

What SEO Professionals Are Saying

Gagan Ghotra, SEO Consultant, wrote on LinkedIn about why the change may lead to better reports:

“Now spam reports have direct relation to Google issuing manual actions against domains. Google announced if there is a spam report from a user and based upon that report Google decide to issue manual action against a domain then Google will just send the user submitted content in report to the site owner (Search Console – Manual Action report) and will ask them to fix those things. Seems like Google was getting too many generic spam reports and now as the incentive to report are aligned. That’s why I guess people are going to submit reports which have a lot of relevant information detailing why/how a specific site is violating Google’s spam policies.”

Read Roger Montti’s full coverage: Google Just Made It Easy For SEOs To Kick Out Spammy Sites

Agentic Restaurant Booking Expands In AI Mode

Google expanded agentic restaurant booking in AI Mode to additional markets on April 10, including the UK and India. Robby Stein, VP of Product for Google Search, announced the rollout on X.

Key facts: Searchers can describe group size, time, and preferences to AI Mode, which scans booking platforms simultaneously for real-time availability. The booking itself is completed through Google partners rather than directly on restaurant websites.

Why This Matters

Restaurant booking shows how task completion within search works. For local SEOs and marketers, traffic patterns shift: users now often stay within Google during discovery, with bookings routed through partners.

This depends on Google booking partners, which may limit visibility for restaurants outside those platforms, making presence on Google-supported booking sites more important than the restaurant’s own website. This model may or may not extend to other experiences.

What SEO Professionals Are Saying

Glenn Gabe, SEO and AI Search Consultant at G-Squared Interactive, flagged the rollout on X:

I feel like this is flying under the radar -> Google rolls out worldwide agentic restaurant booking via AI Mode. TBH, not sure how many people would use this in AI Mode versus directly in Google Maps or Search (where you can already make a reservation), but it does show how Google is moving quickly to scale agentic actions.

Aleyda Solís, SEO Consultant and Founder at Orainti, noted a key limitation in a LinkedIn post:

“Google expands agentic restaurant booking in AI Mode globally: You still need to complete the booking via Google partners though.”

Read Roger Montti’s full coverage: Google’s Task-Based Agentic Search Is Disrupting SEO Today, Not Tomorrow

Theme Of The Week: Google Gets Specific

What counts as spam, what happens when spam gets reported, and what agentic search looks like all got clearer definitions this week.

Back button hijacking becomes a named violation with an enforcement date. Google’s documentation now says spam reports may be used for manual actions, not just fed into detection systems. Agentic search becomes a live product for restaurant reservations in specific markets rather than a talking point about the future.

Now, the compliance work, reporting mechanics, and agentic experience are all clearly understood enough to be tracked directly, instead of just forecasted.

Top Stories Of The Week:

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/seo-pulse-google-targets-back-button-hijacking-agentic-search-grows/572282/

Google’s Product Feed Strategy Points To The Future Of Retail Discovery via @sejournal, @brookeosmundson

For years, many advertisers treated product feeds as a channel task tied mainly to Shopping campaigns.

If you were running Shopping ads, feed optimization likely got attention. If you weren’t, it often slipped behind priorities for the PPC campaigns you were running.

Now, that approach is starting to show its age.

Google’s recent Ads Decoded podcast episode suggests that mindset may need to change. Product data was discussed in connection with free listings, AI-powered search experiences, YouTube formats, Lens, virtual try-on, and newer e-commerce surfaces still evolving.

That reflects a much broader role than many advertisers have historically assigned to their feed.

Google appears to be positioning product data as a larger part of how products are discovered across its platforms, not just how Shopping campaigns perform.

Advertisers who still view Merchant Center as a side task may be underestimating how much visibility now starts with product data.

The more interesting question is what that shift tells us about where Google wants retail advertising to go next.

Merchant Center Is Starting To Look Like Retail Infrastructure

What stood out most in the podcast was how broadly Google described the role of Merchant Center data.

Nadja Bissinger, General Product Manager of Retail on YouTube, described Merchant Center feeds as the “backbone that powers organic and ads experiences,” adding that merchants should submit the most robust product data possible to increase discoverability.

That is a wider role than many advertisers have traditionally associated with Merchant Center.

Google said in a 2025 retail insights piece that people shop across Google more than 1 billion times per day. It also highlighted Search, YouTube, Maps, and visual discovery as key parts of modern shopping journeys. That helps explain why reusable product data is becoming more valuable than channel-specific assets alone.

Google also said Google Lens now sees more than 20 billion visual searches per month, and 1 in 4 Lens searches carry commercial intent. That is another signal that structured product data is becoming more important outside traditional Shopping ads.

For years, many brands viewed Merchant Center as a necessary setup for Shopping campaigns. Google now appears to be positioning it as a core input for how products are surfaced across its platforms.

That should change how feed work is prioritized internally.

Feed optimization is no longer just a PPC responsibility. It can influence:

Organic visibility
Merchandising strategy
Creative presentation
Promotions
How products appear in newer AI-led experiences.

For larger organizations, that may require closer coordination between paid media, SEO, e-commerce, merchandising, and product teams.

For smaller brands, it may be as simple as giving feed quality the same level of attention already given to ad copy, landing pages, and campaign structure.

Many advertisers still treat feed work as cleanup work. That mindset is becoming expensive as product data plays a larger role in who gets seen across Google.

Why Is Google Pushing Product Data So Hard Right Now?

Google’s direction here makes sense when you look at where its retail products are heading.

The company wants more e-commerce activity to happen across Search, YouTube, Maps, AI experiences, and future agentic tools. To support that expansion, it needs merchant data that is accurate, structured, and easy to reuse across different surfaces (as Google refers to them as).

Google has financial reasons to expand e-commerce activity beyond traditional ad clicks. In their 2025 Q4 Earnings Release, they reported a 17% growth in Google Search, and YouTube revenue across ads and subscriptions over $60 billion.

A strong feed helps Google understand:

What a product is
Who it is for
What makes it different
Where it is available
What it costs
How the product should be presented

That matters even more as retail experiences, paid or organic, become more visual, more personalized, and more automated.

Traditional search ads leaned heavily on keywords, headlines, and landing pages. Newer e-commerce formats can also depend on product images, attributes, ratings, promotions, availability, shipping details, and other feed inputs that help match products to user intent.

Better data can lead to better experiences for users. It can also create more places where merchants can appear across Google’s properties.

Google is building more e-commerce surfaces, and product data is the fuel behind them. Advertisers who ignore that may keep optimizing campaigns while missing the larger shift happening around them.

Is Google Prepping For A More Strategic Shift?

From my perspective, there is a larger strategic shift behind Google’s product data push.

I don’t see this as a routine push for better feeds or cleaner campaign inputs. I see Google working to become more of a growth engine for advertisers, with a role that reaches beyond media buying and campaign delivery.

That expansion is moving into areas that shape business performance, including merchandising, product discovery, pricing visibility, local commerce, measurement, and newer purchase-ready experiences.

Google is not only trying to improve how ads run. It appears to be building a deeper position in how products are surfaced, how demand is created, how buying decisions are influenced, and how performance is measured.

My view is that the more Google becomes embedded across those moments, the more connected it becomes to broader business growth rather than media performance alone.

Why Many Advertisers Are Still Measuring Feed Value Wrong

One reason feed optimization still gets deprioritized is simple: many teams are using an outdated scorecard.

Google cited a 33% conversion uplift for advertisers using Demand Gen with product feeds during the podcast discussion. Even if results vary by account, it is another sign that feed quality is being tied to campaign types beyond classic Shopping ads.

If the main question is whether Shopping ROAS improved last week, it becomes easy to undervalue the broader impact of stronger product data.

That measurement approach came from a time when feeds were more closely tied to Shopping campaigns. Google is now using the same data across a much wider set of retail experiences, including discovery surfaces, visual placements, AI-led results, and other formats that do not fit neatly into one campaign report.

That creates a gap between where feed work adds value and where many teams are looking for it.

A stronger title may improve discoverability. Better imagery can increase engagement in visual placements. Accurate pricing and promotions can improve click appeal. Richer attributes can help Google better understand relevance. Availability data can support local and omnichannel visibility.

Those gains may show up across multiple touchpoints, assisted paths, and blended performance trends rather than one Shopping dashboard.

That is why some advertisers continue to underinvest in feed quality. The value is there, but their reporting model was built for an earlier version of Google.

As Google expands where products can appear, feed optimization deserves to be measured more like a visibility and growth lever, not just a Shopping maintenance task.

One of the more important quotes from the podcast came from Ginny Marvin, Google Ads Liaison, as she wrapped up the episode:

Merchants with the most structured, high quality data foundations will be positioned to win.

Winning will not come from uploading a feed once and forgetting about it for months at a time.

It comes from treating product data as an ongoing optimization just like your existing campaigns.

What Google’s AI Max Focus May Be Signaling About Search

One of the more revealing parts of the podcast was how often Search strategy was discussed through the lens of AI Max for Search, while traditional standard Search campaigns were barely mentioned.

During the episode, Firas Yaghi, Global Product Lead for Retail Solutions, talked about how advertisers should be thinking about different campaign types:

I think the role of each campaign really depends on your high level objective. Whether you’re prioritizing cross channel efficiency, granular control or hybrid approach that balances top line sales with OKRs.

He mentioned a lot around Performance Max, Demand Gen, with a little bit of AI Max for Search.

I would avoid treating that as proof that standard Search is going away. There is still clear value in campaigns built around tighter search control, brand protection, and proven high-intent terms.

At the same time, it’s hard to ignore the direction of Google’s messaging.

When Google talks about growth, expansion, and newer retail opportunities, the conversation increasingly centers on AI-assisted campaign types. We have seen similar signals elsewhere, including Google’s announcement that Dynamic Search Ads will upgrade into AI Max for Search and that AI Max represents the next step for search expansion.

My read is that standard Search remains important, but it is no longer the only story Google wants advertisers thinking about.

The company appears to be steering incremental growth toward campaign types that rely on broader matching, stronger inputs, automation, and first-party signals.

I think that Search strategies built around legacy structures will become less competitive over time. I’m not confident enough yet to say that standard Search campaigns will go away completely in the near future, but the increasing signals around keyword-less technology has me thinking more changes for Search campaigns are bound to happen.

What This Means For Your Campaigns

The bigger risk for PPC managers is assuming the teams responsible for merchandising or product data already understand how much feed quality can affect campaign performance.

In many organizations, merchandising, e-commerce, product, or development teams control what goes into Merchant Center. Their priorities may be centered on inventory, pricing, site operations, or category management, not media efficiency or visibility across Google.

That is where PPC managers can add real value.

If product information is influencing how products appear across paid, organic, and AI-led surfaces, someone needs to connect those decisions to marketing outcomes. PPC managers are often in the best position to do that because they can see changes in impressions, traffic quality, conversion trends, and missed opportunities firsthand.

That may mean bringing examples into weekly meetings, showing where missing attributes are limiting reach, flagging weak imagery, highlighting pricing issues, or sharing results from tests that improved performance.

You may not own the feed, but you can help the business understand why it deserves greater priority and where better inputs can improve campaign results.

Put More Focus On Inputs That Can Scale Performance

Many teams spend valuable time on small bid changes, minor budget moves, or endless rounds of creative tweaks while core product data remains incomplete or outdated.

Those tasks still have value, but the upside is often limited when the underlying product information is weak.

If titles are thin, images are poor, attributes are missing, or product details are outdated, fixing those gaps may create more value than another round of minor account adjustments.

Add Feed Health To Regular Performance Reviews

Most reporting cycles focus on spend, ROAS, CPA, and conversion volume.

Those metrics are important, but they do not always show whether product data is helping or limiting visibility.

Feed health deserves a place in regular reviews. Look at disapprovals, missing fields, image quality, pricing accuracy, promotional coverage, and product-level gaps with the same discipline used for media metrics.

Broaden How You Test For Growth

Many retail accounts still treat Search, Shopping, YouTube, and newer campaign types as separate lanes.

Google’s recent direction suggests those lines are becoming less rigid.

Growth testing should include where products can appear across newer surfaces, how feeds support Demand Gen and AI-led placements, and whether stronger product data can unlock reach that existing campaigns are not capturing today.

Treat Better Product Data As A Competitive Advantage

Some advertisers will wait until these newer placements are fully mature before investing seriously in feed quality.

While that delay may be costly for them, your proactiveness can pay off significantly.

What PPC Professionals Are Saying

Recent LinkedIn discussions suggest many practitioners are viewing feed quality as a larger performance lever.

Comments from the podcast episode have been overall positive and has many marketers agreeing that feed management needs to be routine.

Zhao Hanbo commented:

Really interesting to see how something that used to feel mostly like ad ops plumbing is now becoming core infra for AI commerce.

Sophie Westall had similar sentiments, stating that “feed quality is quickly becoming a core part of overall media strategy, not just a hygiene task.”

In a recent LinkedIn post, Menachem Ani said that by fixing a product feed, “campaigns start working harder without touching a single bid.”

More marketers appear to be focusing less on isolated settings and more on the quality of the data – regardless if they’re running paid campaigns or not.

What Comes Next For Retail Marketers

Some advertisers will hear Google’s renewed focus on product data and assume it mainly matters for brands running Shopping campaigns.

That interpretation misses how much wider the opportunity has become.

Google is quickly expanding how products can show up across paid placements, organic surfaces, visual experiences, and newer AI-led formats. As that happens, feed quality becomes more connected to visibility and performance than many teams have historically assumed.

In many organizations, product data still gets treated as maintenance work. It gets attention when something breaks or when Shopping results decline, then falls back down the priority list.

That approach may be harder to justify going forward.

Product data needs a larger role in planning, testing, and cross-functional discussions because it can influence far more than one campaign type.

Read more resources:

Featured Image: Summit Art Creations/Shutterstock

https://www.searchenginejournal.com/googles-product-feed-strategy-points-to-the-future-of-retail-discovery/572291/

Google’s Product Feed Strategy Points To The Future Of Retail Discovery via @sejournal, @brookeosmundson

For years, many advertisers treated product feeds as a channel task tied mainly to Shopping campaigns.

If you were running Shopping ads, feed optimization likely got attention. If you weren’t, it often slipped behind priorities for the PPC campaigns you were running.

Now, that approach is starting to show its age.

Google’s recent Ads Decoded podcast episode suggests that mindset may need to change. Product data was discussed in connection with free listings, AI-powered search experiences, YouTube formats, Lens, virtual try-on, and newer e-commerce surfaces still evolving.

That reflects a much broader role than many advertisers have historically assigned to their feed.

Google appears to be positioning product data as a larger part of how products are discovered across its platforms, not just how Shopping campaigns perform.

Advertisers who still view Merchant Center as a side task may be underestimating how much visibility now starts with product data.

The more interesting question is what that shift tells us about where Google wants retail advertising to go next.

Merchant Center Is Starting To Look Like Retail Infrastructure

What stood out most in the podcast was how broadly Google described the role of Merchant Center data.

Nadja Bissinger, General Product Manager of Retail on YouTube, described Merchant Center feeds as the “backbone that powers organic and ads experiences,” adding that merchants should submit the most robust product data possible to increase discoverability.

That is a wider role than many advertisers have traditionally associated with Merchant Center.

Google said in a 2025 retail insights piece that people shop across Google more than 1 billion times per day. It also highlighted Search, YouTube, Maps, and visual discovery as key parts of modern shopping journeys. That helps explain why reusable product data is becoming more valuable than channel-specific assets alone.

Google also said Google Lens now sees more than 20 billion visual searches per month, and 1 in 4 Lens searches carry commercial intent. That is another signal that structured product data is becoming more important outside traditional Shopping ads.

For years, many brands viewed Merchant Center as a necessary setup for Shopping campaigns. Google now appears to be positioning it as a core input for how products are surfaced across its platforms.

That should change how feed work is prioritized internally.

Feed optimization is no longer just a PPC responsibility. It can influence:

Organic visibility
Merchandising strategy
Creative presentation
Promotions
How products appear in newer AI-led experiences.

For larger organizations, that may require closer coordination between paid media, SEO, e-commerce, merchandising, and product teams.

For smaller brands, it may be as simple as giving feed quality the same level of attention already given to ad copy, landing pages, and campaign structure.

Many advertisers still treat feed work as cleanup work. That mindset is becoming expensive as product data plays a larger role in who gets seen across Google.

Why Is Google Pushing Product Data So Hard Right Now?

Google’s direction here makes sense when you look at where its retail products are heading.

The company wants more e-commerce activity to happen across Search, YouTube, Maps, AI experiences, and future agentic tools. To support that expansion, it needs merchant data that is accurate, structured, and easy to reuse across different surfaces (as Google refers to them as).

Google has financial reasons to expand e-commerce activity beyond traditional ad clicks. In their 2025 Q4 Earnings Release, they reported a 17% growth in Google Search, and YouTube revenue across ads and subscriptions over $60 billion.

A strong feed helps Google understand:

What a product is
Who it is for
What makes it different
Where it is available
What it costs
How the product should be presented