Google Search Console & Trends: Google's new tools balancing convenience and control

No more keyword chaos: Here's what the big Google Search Console update brings

At Google Search Central Live in Toronto, Google unveiled four major innovations for Search Console and Google Trends that will fundamentally change the daily work of SEO experts and content managers. The clear trend: away from manual, tedious data analysis and toward AI-powered, strategic analysis. With new features like semantic query groups, voice-activated filter configurations, and a true Trends API with consistent scaling, Google is drastically reducing the technical hurdles of data analysis. But what does this paradigm shift mean for the industry? When algorithms take over the tedious, painstaking work, technical filtering knowledge becomes less valuable—while the strategic interpretation of data and effective prompting become more important than ever. This is an in-depth look at Google's new generation of tools and their far-reaching consequences for the future of search engine optimization.

When AI takes over the filtering work – and why this will fundamentally change SEOs

On April 21, 2026, the first Google Search Central Live event in Canada took place in Toronto. Google Search Advocate Daniel Waisberg presented four key innovations for Google Search Console and Google Trends, which together signal a profound shift: from passive data reporting to actively configured, AI-powered analysis.

Thousands of keywords at a glance – the logic behind query groups

The first and, from a strategic perspective, most significant innovation is the introduction of so-called Query Groups in Google Search Console. Anyone who uses Search Console regularly is familiar with the fundamental dilemma of the performance report: thousands of search queries that mean the same thing in terms of content but vary in spelling or lexical form appear as separate entries, fragmenting the overall picture. A brand like Google itself, for example, is searched for using variations such as "classroom," "google classroom," "google clssroom," "gg classroom," "classroom accedi," and dozens of other spellings—each line is counted individually, even though the user intent is the same.

Query Groups solve this problem through AI-powered semantic clustering. The system analyzes linguistic similarities, search behavior patterns, and content contexts to group related search queries into thematic categories. The economic value of this feature is significant: Instead of sifting through hundreds of individual entries, analysts receive a consolidated overview based on thematic focus—with aggregated click counts, impressions, and trends. In the presentation, Waisberg demonstrated this using the "schema checker" group, which bundles variants such as "schema validator," "schema markup checker," and "schema tester"—with a total of 1.85 million clicks and a growth of 937 percent. By comparison, the "SEO" group recorded 644,000 clicks, but with a decline of 9 percent.

The Insights Card in Search Console displays these groups directly in the overview dashboard, allowing for an immediate assessment of trends at the topic level. For those wanting to delve deeper, regular expressions allow access to individual queries via drill-down – the displayed regex filter for the Schema Checker group is “^(sc^(schema checker|schema validator|schema markup checker|che…”, illustrating the automatic conversion of AI clustering into filterable data queries. This is a significant detail: Google doesn't completely relinquish control to the algorithm, but rather allows users to inspect, question, and manually refine the machine-generated groupings.

What AI can't yet do – honest admissions from Google

In Toronto, Waisberg was remarkably candid about the limitations of the new features. In a separate slide, he clearly outlined the conceptual challenges of automatic query grouping: Simple filters are insufficient when user intent is complex or ambiguous. The question of granularity—where does one thematic group end and another begin?—cannot always be answered unambiguously algorithmically. It becomes even more complex when the meaning of a search term shifts over time or when the same term targets different groups in different usage contexts. And sometimes, Waisberg explicitly stated, Google simply cannot determine user intent with certainty.

This transparency is analytically valuable: it signals that query groups are not a magic tool that replaces human judgment, but rather an aid that provides structured orientation in complex datasets. The economic implication is clear – anyone optimizing at the group level without knowing the underlying individual queries risks missing the mark in their planning. The Britney Spears example that Waisberg used to illustrate this was as vivid as it was instructive: hundreds of spellings of the same name – from "britney spears" to "brittney spears" and even more exotic variations – must be recognized as semantically equivalent, despite their significant orthographic differences. This is a classic NLP (Natural Language Processing) problem, which language models solve by inferring semantic equivalence beyond string similarity.

Natural language commands instead of filter bureaucracy – AI-supported configuration

The second major innovation is the AI-powered configuration of the performance report in Google Search Console. This new feature, initially rolled out as an experimental option in December 2025 and made generally available in February 2026, allows users to describe complex analyses in natural language. An example prompt from the presentation was: "Show the CTR of queries containing 'how to' or 'what is' in Canada last week." The AI ​​translates this query into a specific configuration – time period: last 7 days, search type: Web, country: Canada, query filter: queries containing "how to" or "what is", metric: CTR, breakdown: queries – and displays the result as a "Suggested configuration" for the user to confirm before they apply it with a click on "Apply".

This represents a paradigmatic shift in human-machine interaction for SEO analytics. Waisberg emphasized that it remains an experimental feature that doesn't always deliver accurate results and explicitly encouraged users to provide feedback. Further example prompts illustrate its potential applications: "Show me queries on phone searches that contain the word 'sports' in the last 6 months," "Compare traffic for my pages that contain '/blog' in this quarter compared to the same quarter last year," or "Show me the average CTR and average position of my queries in Spain in the last 28 days." The AI ​​covers the four key Search Console metrics: clicks, impressions, average CTR, and average position.

This feature is particularly important for companies without dedicated SEO specialists. Content managers, marketing directors, and managing directors of small and medium-sized enterprises can now independently conduct sophisticated analyses for which they previously required external expertise or relied on simplified dashboards.

The quasi-in-house solution: How Xpert.Digital closes operational gaps in B2B marketing and sales – Smart Content-Driven Business - Image: Xpert.Digital

Xpert.Digital is a data-driven B2B industry hub led by Konrad Wolfenstein . The company acts as an external, quasi-in-house solution for industrial partners, closing operational gaps in marketing, content, and sales – without requiring additional resources on the client side.

More information here:

• The quasi-in-house solution: How Xpert.Digital closes operational gaps in B2B marketing and sales – Smart Content-Driven Business

From web browser to data pipeline – the Google Trends API Alpha

The third change doesn't affect Search Console, but rather Google Trends – and it addresses a problem that has plagued market researchers, data journalists, and academics for years: Web access to Google Trends delivers normalized data that is rescaled with every new query. This makes it difficult to consistently compare data points from different queries. For example, if you view search interest for "pineapple" in Thailand over 12 months and then add "banana," you'll get a different scale for the original curve.

The Trends API (Alpha), initially announced at Search Central Live APAC in July 2025, solves this fundamental scaling problem. The API delivers "consistently scaled search interest"—search data that scales consistently across different API calls and can be directly compared without the scaling basis shifting. In Toronto, Waisberg demonstrated this using the example of pineapples versus bananas in Thailand: While the web interface completely rescales the curve when adding the second row, the API delivers consistent values ​​that can also be retrieved separately and then combined.

Technically, the API operates with a rolling five-year data window (approximately 1,800 days) and offers daily, weekly, monthly, and yearly resolutions. The data is up-to-date to within 48 hours prior to the retrieval time. The API call is made via the Google API Client Library; sample code from the presentation demonstrated querying "World Cup" search data for the USA for the entire year of 2024 with weekly resolution. The response includes both the raw `search_interest` value and the `scaled_search_interest` value for each time interval. For practical application, this API opens up entirely new possibilities: automated trend monitoring for editorial planning, integration of search data into business intelligence systems, and near-daily topic monitoring without manually checking dashboards.

AI as a conversation partner in Google Trends – Trends Explore with Gemini

The fourth and perhaps most far-reaching innovation, in its long-term implications, is the integration of Gemini into the Google Trends Explore feature. Previously, meaningful trend analysis required considerable preparation: users had to know which search terms they wanted to compare. With Trends Explore + Gemini, this step is eliminated. Users describe their area of ​​interest in natural language – in the demo example, the input was simply "dog breeds" – and Gemini automatically suggests relevant search terms that can be compared in Google Trends: Bulldog, Labrador Retriever, Poodle, French Bulldog, German Shepherd, Beagle, Golden Retriever, and Rottweiler.

The system not only provides individual suggestions but also thematic expansion ideas: "Small dog breeds," "Hypoallergenic dog breeds," "Dog breeds for families," "Compare Labrador Retrievers and Golden Retrievers," and "Most popular dog breeds in the US"—all as clickable follow-up analyses. This conversational dimension transforms Google Trends from a descriptive analysis tool into an exploratory research instrument that is also accessible to users without in-depth SEO knowledge.

The triangular relationship of Google data – Search Console, Trends and Analytics

Waisberg placed the new features within Google's broader data ecosystem in an insightful slide, which juxtaposed three distinct but complementary tools: Google Trends provides data on search terms, topics, geographic distribution, and search interest. Google Search Console delivers query data, page performance, geo-data, impressions, clicks, and ranking for a website. Finally, Google Analytics provides user data: users, pages, geographic distribution, events, sessions, and views.

This systematization makes it clear that none of the three tools alone provides a complete picture. Trends shows what the world is searching for; Search Console shows how your own website responds to this search demand; Analytics shows what happens to users after they arrive on the website. Only the combination of all three data sources enables a complete digital market analysis. The Toronto event, with the Trends API and the improved Search Console, has significantly enhanced the integration of this data into external systems.

What Toronto means for the future of SEO analysis

The four innovations presented in Toronto are not isolated feature updates. They are part of a coherent strategy with which Google is fundamentally redesigning its data tools: away from the rigid dashboard that requires experts, towards a flexible, natural language-based analytics interface that is accessible to a wider range of users without sacrificing analytical depth.

For the SEO industry, this means a shift in required skills. Those who are currently considered experts because they know how to build complex regular expression filters in Search Console or manually normalize trend data will lose some of their exclusive technical advantage with these tools. What remains, and continues to grow in value, is the ability to interpret: to understand what the data means, what strategic consequences follow from trend shifts, and how to ask the right questions. Even AI that translates prompts into filter configurations requires the user to know what they want to know. Google Search Central Live in Toronto thus provided not only an update on new products, but also a glimpse into an SEO practice where the quality of the question becomes at least as important as the quality of the answer.

☑️ Our business language is English or German

☑️ NEW: Correspondence in your native language!

Konrad Wolfenstein

I and my team are happy to be available to you as your personal advisor.

You can contact me by filling out the contact form here simply call me at +49 7348 4088 965. My email address is [email protected]:or

I'm looking forward to our joint project.

☑️ SME support in strategy, consulting, planning and implementation

☑️ Creation or realignment of the digital strategy and digitization

☑️ Expansion and optimization of international sales processes

☑️ Global & Digital B2B trading platforms

☑️ Pioneer Business Development / Marketing / PR / Trade Fairs

B2B support and SaaS for SEO and GEO (AI search) combined: The all-in-one solution for B2B companies - Image: Xpert.Digital

AI search changes everything: How this SaaS solution will revolutionize your B2B ranking forever.

The digital landscape for B2B companies is undergoing rapid change. Driven by artificial intelligence, the rules of online visibility are being rewritten. For companies, it has always been a challenge not only to be visible in the digital mass, but also to be relevant to the right decision-makers. Traditional SEO strategies and managing local presence (geo-marketing) are complex, time-consuming, and often a battle against constantly changing algorithms and intense competition.

But what if there were a solution that not only simplified this process but also made it smarter, more predictive, and far more effective? This is where the combination of specialized B2B support with a powerful SaaS (Software as a Service) platform comes into play, specifically designed for the demands of SEO and GEO in the age of AI search.

This new generation of tools no longer relies solely on manual keyword analysis and backlink strategies. Instead, it leverages artificial intelligence to more accurately understand search intent, automatically optimize local ranking factors, and conduct real-time competitive analysis. The result is a proactive, data-driven strategy that gives B2B companies a decisive advantage: they are not only found, but perceived as the leading authority in their niche and location.

Here's the symbiosis of B2B support and AI-powered SaaS technology that transforms SEO and GEO marketing, and how your company can benefit from it to grow sustainably in the digital space.

More information here:

​

• B2B Support & Blog for SEO, GEO and AIS – Artificial Intelligence Search
• Forget expensive SEO tools – this alternative dominates with unbeatable B2B features