This first release of 2026 brings Site Kit by Google insights into your Yoast SEO Dashboard. After introducing the integration in phases throughout 2025, we are pleased to share that the rollout is now complete and available to all Yoast customers using WordPress.
What you can see in your Yoast SEO Dashboard
You can now view key performance data from Google Search Console and Google Analytics via Site Kit in your Yoast SEO Dashboard, without changing tools or tabs. These insights include search impressions, clicks, average click through rate, average position, and organic sessions, which are combined with your Yoast SEO and readability scores so you can better understand how content quality relates to real search performance.
Find opportunities faster
The integration also surfaces your top performing content and search queries, helping you quickly spot which pages and topics are driving results and where improvements may have the most impact. Connecting Site Kit by Google is straightforward. Once connected, insights become available immediately, giving you faster access to the data you need to guide your SEO work.
If you are interested in the technical background of this integration and our collaboration with Google, we share the full story on our developer blog.
Get started
Update to Yoast SEO 26.7 to start using Site Kit by Google insights in your Dashboard and streamline your workflow with key performance data in one place. For step by step guidance on enabling the integration, see our help center guide.
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The new year is here, which means it’s time to deliver your end-of-year (EOY) PPC report.
But an EOY report isn’t just a longer version of your monthly performance check-in.
It’s typically read by a different audience – often leadership teams who don’t see your regular reporting – and it needs to tell a different story.
Done well, your year-end report sets the tone for 2026, earns buy-in for your strategy, and positions you as a strategic partner rather than just a campaign manager.
Done poorly, it creates confusion and undermines confidence in your work.
Here’s how to build an EOY PPC report that speaks to leadership and sets your work up for success in the year ahead.
1. Identify your audience and their priorities
You wouldn’t launch a new campaign without clearly defined goals and audiences.
Don’t do it with your EOY report, either. Different stakeholders evaluate performance through very different lenses.
For example, among the clients I’m preparing reports for this year are:
A leadership team I’ve never met (despite working with this client for eight years) that wants a maximum five-page report at a very high level.
A data-driven CEO who wants a clear narrative connecting PPC spend, decisions, and outcomes.
A new director who wants context on the competitive landscape, performance, and specific opportunities for next year.
If I were to use a carbon-copy EOY report template for all of them, I’d have at best one happy leadership team and two confused or frustrated teams.
I don’t care for those odds. Instead, I’m customizing each report to match the readers and their specific needs.
If you don’t know the recipients (and if you’re at an agency, there’s a good chance you don’t), ask your primary contact questions like:
Who will be receiving the report?
What do they care about most?
What’s top of mind for them heading into next year?
What decisions will they be making based on this report?
The answers should directly inform the report’s structure, depth, metrics, and length.
When your audience is clearly defined upfront, the final report is far more likely to drive clarity, alignment, and confidence.
2. Create an easy-to-read executive summary
Your executive summary has one job – help leadership quickly understand how PPC performed across key metrics.
Think of it as the “at a glance” page that sets the context for everything that follows.
If you studied communications formally, you probably learned to write executive summaries last, even though they appear first.
Since you’re pulling data rather than crafting prose, flip that approach.
Build this section first to guide the flow of what comes next.
Lead with the KPIs that matter most
Start with the metrics your audience actually cares about – the ones that were established as priorities at the beginning of the engagement or year.
This will usually include revenue, leads, and conversions, but your mileage may vary.
If your leadership team obsesses over market share or engagement, lead with those instead.
Include meaningful benchmarks
Unless your leadership team is dialed into PPC goals and performance, you need to give them benchmarks so they have a comparison tool.
Use at least one of these three key benchmarks:
Year-over-year performance: How did this year stack up against last year?
Performance against target: Did we hit the goals we set out to achieve?
Industry benchmarks: How do we compare to competitors or industry averages?
In the example below, I’m showing revenue, ROAS, and cost for the year, with both percentage changes and raw numbers from the previous year.
This format does the heavy lifting for busy executives.
At a quick glance, they know what happened and whether it’s good news.
More importantly, it sets the stage for invisible CTAs and the deeper analysis that follows.
3. Break down performance details
In the following section, you’ll move from “what happened” to “why it happened and what we learned.”
The executive summary told your reader whether the year was successful. Now you need to show them the engine under the hood.
The level of detail will depend on the format. A five-page executive report may only have room for a few pivotal moments, while a more comprehensive report can get into the weeds.
In either case, selectivity is critical. You can’t — and shouldn’t — document every metric, test, or optimization from the year.
Instead, focus on insights that either explain the results in your executive summary or point directly to opportunities for the year ahead.
Here are some categories to get you started.
What performed best
Show them the winners: your best-converting creative, highest-revenue products, or most efficient channels.
Leadership loves to see what’s working, and it can point to where to double down.
How resources were allocated
Break down spend distribution across campaign types, the split between brand and non-brand, or platform-specific investments like Google versus Bing.
Leadership wants to know if you’re putting money where it matters most, and this section answers that question.
If you made tracking or conversion definition changes during the year, call them out here.
Leadership needs to understand if a metric shift reflects actual performance or a measurement change.
Keep this section platform-specific and substantive. Each insight should clearly tie back to the executive summary.
Use visuals (charts, trend lines, and comparison tables) to make complex data easier to interpret. And resist the temptation to include everything you track.
If a metric doesn’t explain results, answer a question from leadership, or inform future strategy, leave it out.
You’ve already explained what happened in the account and why performance moved the way it did.
Now you need to zoom out and show leadership what else was happening. What external forces shaped your results, for better or worse?
This is where you separate execution from environment.
Without this context, strong strategic work can look mediocre in a difficult year, or weak decisions can hide behind tailwinds.
Leadership needs to understand what you controlled versus what you were responding to.
Think of it this way: performance details add context to your KPIs. External factors add context to your performance.
Digital marketing factors
What influenced performance that was external to paid search execution, but internal to the broader marketing ecosystem?
Non-PPC marketing initiatives: Product launches, pricing changes, promotions, website redesigns, or messaging shifts can all impact conversion rates and search behavior – positively or negatively.
Non-PPC channel performance: Performance in organic search, email, social, affiliates, or offline channels can meaningfully influence paid search results. It also provides a clearer picture of market factors beyond paid channels.
Platform and policy changes:Google Ads feature rollouts, automation shifts, policy enforcement, or reporting changes often affect performance in ways that aren’t immediately visible in metrics alone.
Competitive dynamics: New entrants, aggressive bidding, creative shifts, or changes in competitor behavior can alter auction pressure and efficiency over time.
Macro-economic factors
What forces outside the marketing organization shaped demand, behavior, and constraints?
A useful way to structure this analysis is with a lightweight PESTEL lens, adapted for a marketing context.
Political: Gov actions and policy shifts (e.g., tariffs, shutdowns).
Environmental: Weather and seasonality (e.g., storms, climate shifts).
Legal: Regulations and compliance (e.g., privacy laws, labor rules).
You don’t need to address every category. The goal is to highlight the factors that materially influenced performance and decisions during the year.
In a volatile year like this one, it can even make sense to highlight big events thatdidn’t have an impact on performance, just to assuage any worries.
Doing this helps stakeholders understand what factors contributed to performance. And just as important, it positions you as someone who sees beyond the interface to meaningful business implications.
5. Answer the ‘what’s next?’ question
Leadership wants to know what to expect for next year.
They’re not necessarily expecting a crystal ball, but they do want confidence that there’s a plan, even if the path changes.
The reality is that most paid search strategy isn’t mapped a year in advance.
Platforms change, competitors react, budgets shift, and new constraints appear with little warning.
What matters isn’t having every answer upfront, it’s having a clear way to decide what to do next when conditions change.
This section of your EOY report is your opportunity to show that decision-making framework, and get your audience excited to work with you on what’s to come.
Next steps and recommendations
These are the initiatives you’re committed to pursuing; the strategic moves grounded in last year’s data:
Applied learnings: How insights from the past year are shaping priorities, structure, and decision-making going forward.
Identified opportunities: Areas where data consistently pointed to upside: channels, audiences, products, or tactics that warrant attention.
Known risks: Challenges leadership should expect, along with how you’re monitoring or mitigating them.
Resource clarity: What additional budget, tools, or support would enable — and what remains constrained without them. Be concrete: “With X additional budget, we can test Y based on Z insight from last year.”
These recommendations should feel inevitable; the logical next steps given what happened last year.
Testing pipeline
Then there’s the other category: things you’re watching, interested in, or ready to jump on if circumstances align.
These scratch leadership’s itch for innovation and cutting-edge solutions without overcommitting:
New platform features you’ll test when they’re released.
Emerging platforms or initiatives worth monitoring.
Competitive tactics you’ve identified but need more validation.
Opportunistic tests if budget or priorities shift.
Frame these as “if/then” scenarios or “things we’re monitoring” rather than firm commitments.
Leadership gets to feel like you’re on top of industry trends without expecting guarantees.
A final pass through a leadership lens
You’ve covered a lot of ground.
This final pass is about tightening credibility and making sure this work pays dividends in the coming years, not just this one.
Give your report a final pass
Before sending, review the report the way leadership will:
Source your data clearly: Label where each chart’s data came from and when it was pulled. This prevents follow-up questions and builds trust.
Address negatives head-on: Leadership expects challenges. What erodes confidence isn’t bad news, it’s unexplained bad news. Show what didn’t work, why, and what you did about it.
Pressure-test against the brief: Review your stakeholders’ original requests. Did you actually answer their questions? Ask a colleague (or AI) for a second set of eyes.
Make next year easier
Now that you’ve done the heavy lifting, leverage this work going forward:
Turn your EOY report into a client-specific template: A single format won’t work across all clients, but once you find a structure that resonates with a given audience, reuse it year over year. Incorporate feedback and refresh the data, but keep the core framework consistent.
Track big issues as they happen: Document key events as you progress through the year. Keep a running list, outside of emails and reports. Even the biggest issues today will be hard to accurately remember in 12 months without this.
Year-end reports take real effort. Make sure yours actually resonates.
Follow these steps to strengthen stakeholder relationships and position yourself as a strategic partner for 2026 and beyond.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/sel-reports-25-G1ZyHX.webp?fit=1544%2C486&ssl=14861544http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-06 14:00:002026-01-06 14:00:00A 5-step framework for year-end PPC reports that resonate with leadership
December made one thing clear: AI is no longer a feature layered on top of marketing. It is the system deciding what gets seen, what gets skipped, and what earns trust.
Search pushed deeper into zero-click behavior. Paid ads lost prime real estate. Influencer content matured into a full‑funnel channel. Platforms added tools while quietly tightening control. At the same time, security and data ownership became real business risks, not abstract concerns.
This roundup breaks down what actually mattered in December and how to adjust before these shifts harden in 2026.
Key Takeaways
Google accelerated AI-first search with Gemini 3, AI Mode, and AI-powered Search Console reporting.
AI Overviews and AI Mode are pushing both organic and paid clicks down, reshaping SERP strategy.
Influencer marketing expanded beyond Gen Z, pulling older, high-value audiences into creator ecosystems.
LinkedIn doubled down on video and events, reinforcing its position as the B2B growth platform.
Security threats like Google Ads MCC hijacks escalated, making account governance a priority.
Search & AI
AI is now deciding what gets seen before a click ever happens. December’s updates show Google tightening its grip on discovery while pushing brands to earn visibility inside AI systems.
Search Console Gets AI-Driven Reporting
Google rolled out AI-powered configuration in Search Console, allowing users to request custom reports using natural language. Instead of manually stitching filters together, teams can now ask questions the way they think about performance.
Our POV: This changes who gets access to insight. Reporting no longer bottlenecks around technical SEO or analytics specialists. Strategy conversations can happen faster, and closer to the business question that triggered them.
What this unlocks: Faster pattern recognition across large sites, quicker validation of hypotheses, and fewer reporting cycles spent just getting the data into shape.
What to do next: Standardize a small set of executive-level prompts tied to growth questions (discovery, decline, opportunity). Use this to shorten the distance between signal and decision.
Gemini 3 Lands Directly in Google Search
Google deployed Gemini 3 straight into Search across 120 countries, delivering richer answers, visuals, and interactive elements without requiring users to leave the results page.
Our POV: This is Google asserting itself as the destination, not the doorway. Content that once earned traffic by being explanatory or comparative now competes with Google’s own synthesized answers.
Strategic impact: Informational content becomes less about volume and more about authority. If your content is interchangeable, it becomes invisible.
What to do next: Identify where your content overlaps with Gemini-style answers. Invest more heavily in insight, proprietary data, and perspective that AI cannot compress without losing value.
Google Embeds AI Mode Into the Search Flow
When users tap “show more” under an AI Overview, Google now routes them into a full AI chat experience rather than expanding citations.
Our POV: This confirms that Google is intentionally reducing outbound traffic in favor of guided, AI-mediated discovery.
Strategic impact: Attribution gets murkier. Influence matters more than visits. Brands that only measure success by clicks will underinvest in visibility where decisions actually form.
What to do next: Start treating AI inclusion as a visibility channel. Track brand mentions, citations, and presence inside AI responses alongside traditional KPIs.
AI Overviews Push Ads Below the Fold
Research shows that roughly a quarter of search results now place paid ads beneath AI Overviews, with mobile SERPs most affected.
Our POV: Paid search is losing guaranteed prominence. Bidding harder no longer guarantees being seen.
Strategic impact: Paid media performance becomes dependent on how well it aligns with AI-generated context, not just auction dynamics.
What to do next: Re-evaluate high-value keywords where ads routinely fall below AI content. Coordinate paid and organic teams so messaging reinforces what users see first.
Branded Query Filtering and Chart Notes Arrive in GSC
Search Console now separates branded and non‑branded queries automatically and allows chart-level annotations.
Our POV: This finally closes long-standing reporting gaps that distorted SEO performance narratives.
What to do next: Capture a baseline brand vs non‑brand split now. Add annotations for launches, migrations, PR wins, and algorithm shifts to preserve institutional knowledge.
Paid Media & Risk
Automation keeps increasing, but so does exposure. December highlighted how fragile performance can be without strong governance and clear safeguards.
OpenAI Pauses ChatGPT Ads
OpenAI halted its early test of native ads inside ChatGPT after users struggled to distinguish sponsored content from AI-generated answers.
Our POV: This pause is less about ads failing and more about timing. Conversational interfaces collapse the distance between advice and influence, which raises the bar for trust.
Strategic impact: Future AI advertising will not behave like traditional display or search ads. Brands will compete on usefulness, credibility, and contextual fit rather than interruption.
What to do next: Start pressure-testing what value-driven, answer-oriented advertising could look like for your category. Focus on scenarios where a brand genuinely helps a user decide, not just where it can appear.
Google Ads MCC Hijacks Surge
Phishing attacks targeting Google Ads manager accounts increased sharply, allowing attackers to drain budgets and lock out advertisers within hours.
Our POV: This is no longer an edge case. As accounts scale, risk compounds.
Strategic impact: Performance gains mean little if governance fails. Security lapses can erase months of optimization and undermine executive confidence in paid media.
What to do next: Treat access control as part of your growth strategy. Limit permissions aggressively, audit users regularly, and align security reviews with budget planning.
Product, Design & UX
Product and design updates are quietly shaping how fast teams can ship, test, and iterate. December brought one change that materially reduces friction between design and development.
Figma Introduces CSS Grid-Like Layout Controls
Figma rolled out a new grid system that more closely mirrors how CSS Grid and Flexbox behave in production. Designers can now edit rows and columns directly, reposition elements with keyboard controls, and build layouts that respond more like real front-end frameworks.
Our POV: This narrows the long-standing gap between design intent and shipped experience. Fewer handoff mismatches mean faster iteration and fewer compromises downstream.
Strategic impact: Design systems become more scalable when layouts behave predictably across breakpoints. Teams that rely on rapid experimentation benefit most.
What to do next: Revisit your design system and layout standards. Align designers and developers on grid conventions so prototypes map cleanly to production.
Social & Creator Economy
Creator content is no longer niche or youth-driven. Platforms are shaping social into a full-funnel, multi-generational influence engine.
LinkedIn Sees Another Video Surge
LinkedIn reported continued double-digit growth in video uploads and watch time, with short-form content driving disproportionate reach.
Our POV: LinkedIn has quietly become a daily content destination, not just a professional directory.
Strategic impact: B2B visibility increasingly depends on consistent, human-led storytelling. Brands that delay video adoption will find it harder to build authority as the feed fills up.
What to do next: Commit to a repeatable LinkedIn video cadence. Prioritize clarity and expertise over production polish, and measure engagement trends over time.
LinkedIn Upgrades Event Ads
New integrations with ON24 and Cvent allow LinkedIn Event Ads to capture and route leads directly into CRMs.
Our POV: Events are moving out of the brand bucket and into the revenue conversation.
Strategic impact: This blurs the line between awareness and pipeline, making events accountable in ways they historically avoided.
What to do next: Reframe events as performance channels. Align messaging, registration, and follow-up under a single measurement framework.
Influencer Content Expands Beyond Gen Z
New data shows that more than half of adults aged fifty-five to sixty-four now watch influencer content weekly, often via connected televisions.
Our POV: Influencer marketing has crossed into mainstream media behavior. This is no longer a youth or trend-driven channel.
Strategic impact: Influencers are shaping consideration and trust for higher-value purchases, not just discovery for impulse buys.
What to do next: Test creator partnerships that emphasize expertise and credibility. Treat influencer content as a mid-funnel and upper-funnel asset, not just awareness.
Meta Enhances the Creator Marketplace
Instagram expanded its Creator Marketplace with better discovery, AI recommendations, and stronger paid amplification tools.
Our POV: Meta is positioning creators as a scalable performance input, not just an organic reach lever.
Strategic impact: The line between influencer marketing and paid social continues to erode. Creative quality and creator trust now directly affect efficiency.
What to do next: Identify creators whose content already performs organically. Use paid support to scale what works instead of forcing performance from scratch.
PR, Media, and Trust
As AI pulls from third-party sources, brand credibility is being shaped outside your owned channels. Relationships and presence matter more than volume.
Journalists Push Back on AI Pitches
Surveys show most journalists still prefer human-led outreach, citing AI-written pitches as generic and misaligned with their coverage needs.
Our POV: Efficiency without judgment damages relationships.
Strategic impact: As AI-generated noise increases, thoughtful and relevant outreach becomes a stronger differentiator.
What to do next: Use AI for research and preparation, not substitution. Preserve human insight where trust and creativity matter most.
Discord Emerges as a Media Hub
PR teams are increasingly using Discord servers as live, on-demand press rooms.
Our POV: This flips traditional outreach from push to pull.
Strategic impact: Brands that make themselves accessible become resources journalists return to, not just sources they react to.
What to do next: Pilot a controlled Discord environment for media. Offer clear channels, real access, and timely updates without overwhelming participants.
Platform Playbooks
Smaller platform updates often hide the most practical gains. December delivered clear lessons on how context and native execution drive results.
Reddit Releases Dynamic Product Ad Guidance
Reddit published best practices showing that focused optimizations can lift Dynamic Product Ad performance meaningfully.
Our POV: Reddit rewards relevance over polish.
Strategic impact: Brands that adapt creative to platform norms outperform those that recycle ads from other networks.
What to do next: Speak directly to subreddit context, keep messaging tight, and test incrementally to isolate what actually moves performance.
Conclusion
December reinforced a hard truth: visibility is no longer owned. It is earned repeatedly across AI systems, platforms, and communities.
The brands that win in 2026 will build authority machines, not traffic hacks. They will secure their data, design for AI interpretation, and show up consistently wherever decisions are shaped.
If you want help translating these shifts into a durable growth strategy, the NP Digital team is already doing this work every day.
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SEO now sits at an uncomfortable intersection at many organizations.
Leadership wants visibility in AI-driven search experiences. Product teams want clarity on which narratives, features, and use cases are being surfaced. Sales still depends on pipeline.
Meanwhile, traditional rankings, traffic, and conversions continue to matter. What has changed is the surface area of search.
Pages are now summarized, excerpted, and cited in environments where clicks are optional and attribution is selective.
When a generative AI summary appears on the SERP, users click traditional result links only about 8% of the time.
As a result, SEO teams need a clearer playbook for earning visibility inside generative outputs, not just around them.
This 90-day action plan outlines how to achieve this in a phased, weekly execution, with practical adjustments tailored to the specific purpose of the website.
Phase 1: Foundation (Weeks 1-2)
Define your ‘AI search topics’
Keywords still matter. But AI systems organize information around entities, topics, and questions, not just query strings.
The first step is to decide what you want AI tools to associate your brand with.
Action steps
Identify 5-10 core topics you want to be known for.
For each topic, map:
The questions users ask most often
The comparisons they evaluate
“Best,” “how,” and “why” queries that indicate decision-making intent
Example:
Topic: AI SEO tools
Mapped query types:
Core questions: What are the best AI SEO tools? How does AI improve SEO?
Comparisons: AI SEO tools vs traditional SEO tools.
Intent signals: Best AI SEO tools for content optimization.
Where this shifts by website type
Content hubs (media brands, publishers, research orgs) should prioritize mapping educational breadth – covering a topic comprehensively so AI systems see the site as a reference source, not a transactional endpoint.
Services/lead gen sites (agencies, consultants, local businesses) should map problem-solution queries prospects ask before converting, especially comparison and “how does this work?” questions.
Product and ecommerce sites (DTC brands, marketplaces, subscription ecommerce, retailers) should map topics to use cases, alternatives, and comparisons – not just product names or category terms.
Commercial, long-funnel sites (B2B SaaS, fintech, healthcare) should anchor topics to category leadership – the “what is,” “how it works,” and “why it matters” content buyers research long before demos.
If you can’t clearly articulate what you want AI systems to associate you with, neither can they.
Generative engines consistently surface content that is easy to extract, summarize, and reuse.
In practice, that favors pages where answers are clearly framed, front-loaded, and supported by scannable structure.
High-performing pages tend to follow a predictable pattern.
AI-friendly content structures include:
A short intro (2-3 lines) that establishes scope.
A direct answer placed immediately after the header, written to stand alone if excerpted.
Bulleted lists or numbered steps that break down the explanation.
A concise FAQ section at the bottom that reinforces key queries.
This increases the likelihood your content is:
Quoted in AI Overviews.
Used in ChatGPT or Perplexity answers.
Surfaced for voice and conversational search.
For ecommerce and services sites in particular, this is often where internal resistance shows up. Teams worry that answering questions too directly will reduce conversion opportunities.
In AI-driven search, the opposite is usually true: pages that make answers easy to extract are more likely to be surfaced, cited, and revisited when users move from research to decision-making.
In generative search, content that gets surfaced typically resolves the core question immediately, then provides context and depth.
For many commercial teams, that requires rethinking how early pages prioritize explanation versus persuasion – a shift that’s increasingly necessary to earn visibility at all.
This is where GEO (generative engine optimization) and AEO (answer engine optimization) move from theory into page-level execution.
Add a 1–2 sentence TL;DR under key H2s that can stand on its own if excerpted
Use explicit, question-based headers:
“What is…”
“How does…”
“Why does…”
Include clear, plain-language definitions before introducing nuance or positioning
Example:
What is generative engine optimization?
Generative engine optimization (GEO) helps content get selected as a source in AI-generated answers.
In practice, GEO is the process of structuring and optimizing content so AI tools like ChatGPT and Google AI Overviews can interpret, evaluate, and reference it when responding to user queries.
How does answer-first structure change by site type?
Publishers benefit from definitional clarity because it increases citation frequency.
Lead gen sites see stronger mid-funnel engagement when prospects get clear answers upfront.
Product sites reduce friction by addressing comparison and “is it right for me?” questions early.
B2B platforms establish category authority long before a buyer ever hits a pricing page.
Add structured data (high impact, often underused)
Structured data remains one of the clearest ways to signal meaning and credibility to AI-driven search systems.
It helps generative engines quickly identify the source, scope, and authority behind a piece of content – especially when deciding what to cite.
At a minimum, most sites should implement:
Article schema to clarify content type and topical focus.
Organization schema to establish the publishing entity.
Author or Person schema to surface expertise and accountability.
FAQ schema, where it reflects genuine question-and-answer content, can still reinforce structure and intent – but it should be used selectively, not as a default.
This matters differently by site type:
Content hubs benefit when author and publication signals reinforce editorial credibility and reference value.
Lead gen and services sites use schema to connect expertise to specific problem areas and queries.
Product and ecommerce sites help AI systems distinguish between informational content and transactional pages.
Commercial, long-funnel sites rely on schema to support trust signals alongside relevance in high-stakes categories.
Structured data doesn’t guarantee inclusion – but in generative search environments, its absence makes exclusion more likely.
As generative systems decide which sources to reference, demonstrated experience increasingly outweighs polish alone.
Pages that surface consistently tend to show clear evidence that the content comes from real people with real expertise.
Meaning, signals associated with E-E-A-T – experience, expertise, authoritativeness, and trust – remain central to how generative systems decide which sources to reference.
Key signals to reinforce:
Clear author bios that establish credentials, role, or subject-matter relevance.
First-hand experience statements that indicate direct involvement (“We tested…”, “In our experience…”).
Original visuals, screenshots, data, or case studies that can’t be inferred or synthesized
This is where generic, AI-generated content reliably falls short.
Without visible signals of experience and accountability, AI systems struggle to distinguish authoritative sources from interchangeable ones.
How different site types should demonstrate experience and authority
Media and research sites should reinforce editorial standards, sourcing, and author attribution to support citation trust.
Agencies and consultants benefit from foregrounding lived client experience and specific outcomes, not abstract expertise.
Ecommerce brands earn trust through real-world product usage, testing, and visual proof.
High-ACV B2B companies stand out by showcasing practitioner insight and operational knowledge rather than marketing language alone.
If your content reads like it could belong to anyone, AI systems will treat it that way.
Certain page types are more likely to be cited in AI-generated answers because they organize information in ways that are easy to extract, compare, and reference.
These pages are designed to serve as reference material – resolving common questions clearly and completely, rather than advancing a particular perspective.
Formats that consistently perform well include:
Ultimate guides that consolidate a topic into a single, authoritative resource.
Comparison tables that make differences explicit and scannable.
Statistics pages that centralize data points AI systems can reference.
Glossaries that define terms clearly and consistently.
Pages with titles such as “AI SEO Statistics (2025)” or “Best AI SEO Tools Compared” are frequently surfaced because they signal completeness, recency, and reference value at a glance.
For commercial sites, citation-worthy pages don’t replace conversion-focused assets.
They support them by capturing early-stage, informational demand – and positioning the brand as a credible source long before a buyer enters the funnel.
Generative systems increasingly synthesize signals across text, images, and video when assembling answers.
Content that performs well in AI-driven search is often reinforced across formats, not confined to a single page or medium.
Add descriptive, specific alt text that explains what an image shows and why it’s relevant.
Create short-form videos paired with transcripts that mirror on-page explanations.
Repurpose core content into formats AI systems can encounter and contextualize elsewhere:
YouTube videos.
LinkedIn carousels.
X threads.
How this supports different site goals
Publishers extend the reach and reference value of core reporting and explainers.
Services and B2B sites reinforce expertise by repeating the same answers across multiple surfaces.
Ecommerce brands support discovery by contextualizing products beyond traditional listings and category pages.
Track AI visibility – not just traffic
As generative results absorb more of the discovery layer, traditional click-based metrics capture only part of search performance.
AI visibility increasingly shows up in how often – and where – a brand’s content is referenced, summarized, or surfaced without a click.
With 88% of businesses worried about losing organic visibility in the world of AI-driven search, tracking these signals is essential for demonstrating continued influence and reach.
Signals worth monitoring include:
Featured snippet ownership, which often feeds AI-generated summaries.
Appearances within AI Overviews and similar answer experiences.
Brand mentions inside AI tools during exploratory queries.
Search Console impressions, even when clicks don’t follow.
For long sales cycles in particular, these signals act as early indicators of influence.
AI citations and impressions often precede direct engagement, shaping consideration well before a buyer enters the funnel.
These tools support different parts of an SEO-for-AI workflow, from topic research and content structure to schema implementation and visibility tracking.
Content and AI SEO
Surfer, Clearscope, Frase
Used to identify gaps in topical coverage and evaluate whether content resolves questions clearly enough to be excerpted in AI-generated answers.
Schema and structured data
RankMath, Yoast, Schema App
Useful for implementing and maintaining schema that helps AI systems interpret content, authorship, and organizational credibility.
Visibility and performance tracking
Google Search Console, Ahrefs
Essential for monitoring impressions, query patterns, and how content surfaces in search – including cases where visibility doesn’t result in a click.
AI research and validation
ChatGPT, Perplexity, Gemini
Helpful for testing how topics are summarized, which sources are cited, and where your content appears (or doesn’t) in AI-driven responses.
The rule that matters most
AI systems tend to favor content that provides definitive answers to questions.
If your content can’t answer a question clearly in 30 seconds, it’s unlikely to be selected for AI-generated answers.
What separates teams succeeding in this environment isn’t experimentation with new tactics, but consistency in execution.
Pages built to be understandable, referenceable, and trustworthy are the ones generative systems return to.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/semrush-discover-ai-optimization-031q2A.webp?fit=800%2C440&ssl=1440800http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-05 13:00:002026-01-05 13:00:00A 90-day SEO playbook for AI-driven search visibility
In fact, 74% of shoppers give up because there’s too much choice, according to research by Business of Fashion and McKinsey.
Now?
A shopper submits a query. AI gives one clear answer — often with direct links to products, reviews, and retailers. They can even click straight to purchase.
So, how do you make sure AI recommends your fashion brand?
We analyzed how fashion brands appear in AI search. And why some brands dominate while others disappear.
In this article, you’ll learn how large language models (LLMs) interpret fashion, what drives visibility, and the levers you can pull to get your brand visible in AI searches (plus a free fashion trend calendar to help you plan).
There are three ways people will see your brand in AI search: brand mentions, citations, and recommendations.
Brand mentions are references to your brand within an answer.
Ask AI about the latest fashion trends, and the answer includes a couple of relevant brands.
Citations are the proof that backs up AI answers. Your brand properties get linked as a source. This could be product pages, sizing guides, or care instructions.
Citations also include other sites that talk about your brand, like Wikipedia, Amazon, or review sites.
Product recommendations are the most powerful form of AI visibility. Your brand isn’t just mentioned; it’s actively suggested when someone is ready to buy.
For example, I asked ChatGPT for recommendations of aviator sunglasses:
Ray-Ban doesn’t just show up as a mention — they’re a recommended option with clickable shopping cards.
How AI Models Choose Which Fashion Brands to Surface
If you’ve ever wondered how AI chooses which fashion brands to surface, here are the two basic factors:
By evaluating what other people say about you online
By checking how consistently factual and trustworthy your own information is
Let’s talk about consensus and consistency. Plus, we’ll discuss real fashion brands that are winning at both.
Consensus
If you ask all your friends for their favorite ice cream shop, they’ll probably give different answers.
But if almost everyone coincided in the same answer, you trust that’s probably the best place to go.
AI does something similar.
First, it checks different sources of information online. This includes:
Editorial websites, like articles in Vogue, Who What Wear, InStyle, and others
Community and creator content, including TikTok try-ons, Reddit threads, and YouTube product roundups
Retailer corroboration, like ratings and reviews on Amazon, Nordstrom, Zalando, and more
Sustainability verification from third parties like B Corp, OEKO-TEX, or Good On You
After analyzing this information, it gives you recommendations for what it perceives to be the best option.
Here’s an example of what that consensus looks like for a real brand:
Carhartt is mentioned all over the web. They appear in retail listings, editorial pieces, and in community discussions.
The result?
They get consistent LLM mentions.
Consistency
AI also judges your brand based on the consistency of your product information.
This includes:
Naming & colorways: Identical names/color codes across your own site, retailers, and mentions
Fit & size data: Standardized size charts, fit guides, and model measurements
Materials & care: The same composition and instructions across all channels
Imagery/video parity: The same SKU visuals (like hero, 360, try-on) on your site and retailer sites
Price & availability sync: Real-time updates during drops or restocks to avoid stale or conflicting data
For example, Lululemon does a great job of keeping product availability updated on their website.
If you ask AI where to find a specific product type, it directs you back to the Lululemon website.
This happens because Lululemon’s site provides accurate, up-to-date information.
Plus, it’s consistent across retailer pages.
The Types of Content That Dominate Fashion AI Search
Mentions get you into the conversation. Recommendations make you the answer. Citations build the credibility that supports both.
The brands winning in AI search have all three — here’s how to diagnose where you stand.
Let’s talk about the fashion brands that are consistently showing up in AI search results, and the kind of content that helps them gain AI visibility.
Editorial Shopping Guides and Roundups
Editorial content has a huge impact on results.
Sites like Vogue, Who What Wear, and InStyle are regularly cited by LLMs.
These editorial pieces are key for AI search, since they frame products in context — showing comparison, specific occasions, or trends.
There are two ways to play into this.
First, you can develop relationships with editorial websites relevant to your brand.
Start by researching your top three competitors. Using Google (or a quick AI search), find out which publications have featured those competitors recently.
Then, reach out to the editor or writers at those publications.
If they’re individual creators, you might send sample products for them to review.
Looking for mentions from bigger publications?
You might consider working with a PR team to get your products listed in articles.
To build consistency in that content, provide data sheets with information about material, fit, or care.
Second, you can build your own editorial content.
That’s exactly what Huckberry does:
They regularly produce editorial-style content that answers questions.
Many of these posts include a video as well, giving them more opportunity for discovery in LLMs:
Retailer Product Pages and Brand Stores
Think of your product detail page (PDP) as the source of truth for AI.
If you don’t have all the information there, AI will take its answers from other sources — whether or not they’re accurate.
Product pages (your own website or a retailer’s) need to reflect consistent, accurate information. Then, AI can understand and translate into answers.
Some examples might include:
Structured sizing information
Consistent naming and colorways
Up-to-date prices and availability
Ratings (with pictures)
Fit guides (like sizing guides and images with model measurements and sizing)
Materials and care pages
Transparent sustainability modules
For example,Everlane provides the typical sizing chart on each of its products. But they take it a step further and include a guide to show how a piece is meant to fit on your body.
You can even see instructions to measure yourself and find the right size.
That’s why, when I ask AI to help me pick the right size for a pair of pants, it gives me a clear answer.
And the citations come straight from Everlane’s website.
Everlane’s product pages also include model measurements and sizing.
So when I ask ChatGPT for pictures to help me pick the right size, I get this response:
However you choose to present this information on your product pages, just remember: It needs to be identical on all retailer pages as well.
Otherwise, your brand could confuse the LLMs.
User Generated Video Content
What you say about your own brand is one thing.
But what other people say about you online can have a huge influence on your AI mentions.
Of course, you don’t have full control over what consumers post about you online.
So, proactively build connections with creators. Or, try to join the conversation online when appropriate.
This can help you build a positive sentiment toward your brand, which AI will pick up on.
Not sure which creators to work with?
Try searching for your competitors on channels like TikTok or Instagram. See which creators are mentioning their products, and getting engagement.
Search by social channels, and filter by things like follower count, location, and pricing.
Here’s an example: Aritzia has grown a lot on TikTok. They show up in creator videos, fit checks, and unboxing-style videos.
In fact, the hashtag #aritziahaul has a total of 32k posts, racking up 561 million views overall.
Other fashion brands, like Quince, include a reviewing system on their PDPs.
This allows consumers to rate the fit and add pictures of themselves wearing the product.
LLMs also use this information to answer questions.
Creator try-ons, styling videos, and similar content can help increase brand mentions in “best for [body type]” or “best for [occasion]” prompts.
Pro tip: Zero-click shopping is coming. Perplexity’s “Buy with Pro” and ChatGPT’s “Instant Checkout” hint at a future where AI answers lead straight to one-click purchases. The effects are still emerging, but as with social shopping, visibility wins. So, make sure your brand shows up in the chats that drive buying decisions.
Reddit and Community Threads
Reddit is a major source of information for fashion AI queries.
This includes information about real-world fit, durability, comfort, return experiences, and comparisons.
For example, Uniqlo shows up regularly in Reddit threads and questions about style.
You can also find real reviews of durability about the products.
As a result, the brand is getting thousands of mentions in LLMs based on Reddit citations.
Plus, this leads to a ton of organic traffic back to the Uniqlo website.
Obviously, it’s impossible to completely control the conversation around your brand. So for this to work, there’s one key thing you can’t miss:
Your products need to be truly excellent.
A mediocre product that has a lot of negative sentiment online won’t show up in AI search results.
And no amount of marketing tactics can fool the LLMs.
Further reading: Learn how to join the conversation online with our Reddit Marketing guide.
Lab Tests and Fabric Explainers
This kind of content shows the quality of your products.
It gives LLMs a measurable benchmark to quote on things like pilling or color fastness.
This content could include:
“6-month wear” style videos
Pages that explain the fabrics and materials used
Third party tests
Clear care instructions
For example, Quince has an entire page on their website talking about cashmere.
And in Semrush’s AI Visibility dashboard, you can see this page is one of the top cited sources from Quince’s website.
Another option is to create content that shows tests of your products.
Here’s a great example from a brand that makes running soles, Vibram.
They sponsored pro trail runner Robyn Lesh, and teamed up with Huckberry to lab test some of their shoes.
This kind of content is helping Vibram maintain solid AI visibility.
And for smaller brands who don’t have Vibram’s sponsorship budget?
Try doing product testing content with your own team.
For example, have a team member wear a specific product every day for a month, and report back on durability.
Or, bury a piece of clothing underground and watch how long it takes to decompose, like Woolmark did:
Get creative, and you’ll have some fun creating content that can also help your brand be more visible.
Start by checking your AI visibility score. You’ll see how this measures up against the industry benchmarks.
You can prioritize next steps based on the Topic Opportunities tab.
There, you’ll see topics where your competitors are being mentioned, but your brand is missed.
Then, jump to the Brand Perception tab to learn more about your Share of Voice and Sentiment in AI search results.
You’ll also get some clear insights on improvements you can make.
Comparisons and Alternatives Content
AI loves a good comparison post (and honestly, who doesn’t?). So, creating content that compares your products to other brands is a great way to get more mentions.
It helps you get brand exposure without depending on organic traffic dependence. Plus, it helps level the playing field with bigger competitors.
For instance, Quince is often cited online as a cheaper alternative to luxury clothing.
I asked ChatGPT for affordable cashmere options, and Quince was the first recommendation.
So, why is this brand showing up consistently?
One reason is their comparison content.
In each PDP, you’ll see the “Beyond Compare” box, showing specific points of comparison with major competitors.
The right comparisons are handled honestly and tastefully.
Focus on real points of difference (like Quince does with price). Or, show which products are best for certain occasions.
For example: “Our sweaters are great for hiking in the snow. Our competitors’ sweaters are better for indoor activities.”
Comparisons give AI a reason to recommend your fashion brand when someone asks for an alternative.
What This Shift Means for Your Fashion Brand
AI search has changed the way people discover products, and even their path to purchase.
Before, this involved multiple searches, clicking on different websites, or scrolling through forums. Now, you can do this in one simple interface.
So, how is AI changing fashion, and how can your brand adapt?
Editorial, Retailer, and PDP Split
AI search doesn’t treat every source of information equally.
And depending on which model your audience uses, the “default” source of truth can look very different.
ChatGPT leans heavily on editorial and community signals.
It rewards cultural traction — what people are talking about, buying, and loving.
For example, articles like this one from Vogue are a prime source for ChatGPT answers:
Meanwhile, Google’s AI Mode and Perplexity skew toward retailer PDPs.
They look for structured data like price, availability, or fit guides. In other words, they trust whoever has the cleanest, richest product data.
The most visible brands win in both arenas: cultural conversation and PDP completeness.
Here’s What You Can Do
To show up in all major LLMs, you need two parallel pipelines.
Cultural traction: Like press mentions, creator partnerships, and community visibility
Citation-ready proof: For example, complete and accurate PDPs across retailer channels
Here’s an Example: Carhartt
Carhartt is a great example of a brand that’s winning on both sides.
First, they get consistent cultural visibility.
For instance, Vogue reported that the Carhartt WIP Detroit jacket made Lyst’s “hottest product” list. That led to searches for their brand increasing by 410%.
This makes it more likely for LLMs to recommend their products in answers:
This is the kind of loop that works wonders for a fashion brand.
At the same time, Carhartt is also stocked across a huge range of retailers. You can find them in REI, Nordstrom, Amazon, and Dick’s, plus their own direct-to-consumer website.
So, Google AI Mode has an abundance of PDPs, videos, reviews, and Q&A to cite.
This makes Carhartt extremely “citation-friendly” in both models.
No wonder it has such a strong AI visibility score.
Trend Shocks and Seasonal Volatility
Trend cycles aren’t a new challenge in the fashion industry. But it becomes a bigger challenge to maintain visibility when those trends affect which brands appear in AI search.
Micro-trends pop up all the time, triggering quick shifts in how AI answers fashion queries.
When the trend heats up, LLMs pull in brands that appear online in listicles or TikTok roundups.
And when the trend cools? Those same brands disappear just as quickly.
Here’s What You Can Do
To stay present during each trend swing, you need a content and operations pipeline that speaks in real time to the language models are echoing.
Build a proactive trend calendar: Map your content to seasonal moments, like spring tailoring, fall layers, holiday capsules, back-to-school basics, and so on
Refresh imagery and copy to mirror trend language: Update PDPs, on-site copy, and retailer description to match the phrasing used in cultural content
Create rapid-fire listicles and lookbooks: Listicle-style content, creator videos, and other trend-related mentions can help boost visibility. This includes building your own content and working with creators and publications to feature your product in their content.
Anyone who was around for Y2K may have been shocked to see UGG boots come around again.
But the brand was ready to jump onto the trend and make the most of their moment.
Vogue reported that UGG made Lyst’s “hottest products” list in 2024.
Since then, they’ve been regularly featured in seasonal “winter wardrobe essentials” style roundups.
One analyst found that there had been a 280% increase in popularity for the shoes. Funny enough, that trend seems to be a regular occurrence every year once “UGG season” rolls around.
In fact, on TikTok, the hashtag #uggseason has almost 70k videos.
UGG stays visible even as seasons trends shift. That’s because the brand is always present in the content streams that LLMs treat as cultural indicators. By partnering with influencers, UGG amplified its presence so effectively that the boots themselves became a moment — something people wanted to photograph, share, and join in on without being asked.
The result?
They have one of the highest AI Visibility scores I saw while researching this article.
(As a marketer, I find this encouraging. As a Millennial, I find it deeply disturbing.)
Pro tip: Want to measure the results? Track how often your brand or SKUs appear in new listicles per month, plus how they rank in those roundups. Then use Semrush’s AI Visibility Toolkit to track your brand’s visibility using trend-related prompts.
Sustainability and Proof (Not Claims)
Sustainability has become one of the strongest differentiators for fashion brands in AI search.
But only when brands back it up with verifiable proof.
LLMs don’t reward vague eco-friendly language. Instead, they surface brands with certifications, documentation, and third-party validation.
Models also pull heavily from Wikipedia and third-party certification databases. These pages often act as trust anchors for AI search results.
Here’s What You Can Do
You need to build a clear, credible footprint that models can cite.
Centralize pages on materials, care, and impact: Make them brief, structured, and verifiable. Include materials, sourcing, certifications, and repair/resale info.
Maintain third-party profiles: Keep your certifications up-to-date. This includes things like Fair Trade, Bluesign, B-Corp, GOTs, etc.
Standardize sustainability claims across all retailers: If your DTC site says “Fair Trade Certified” but your Nordstrom PDP doesn’t? Models treat that as unreliable.
Here’s an Example: Patagonia
Patagonia is the ruler of AI visibility with a 21.96% share of voice.
In part, this is because of their incredible dedication to sustainability. They basically own this niche category within fashion.
Patagonia’s sustainability claims are backed up by third-party certifications.
And they’re displayed proudly on each PDP.
They’re also transparent about their efforts to help the environment.
They keep pages like this updated regularly.
These sustainable efforts aren’t just big talk.
Review sites and actual consumers speak positively online about these efforts.
They’ve made their claim as a sustainable fashion brand.
So, Patagonia shows up first, almost always, in LLMs when talking about sustainable fashion:
That’s the power of building a sustainable brand.
Make AI Work for Your Fashion Brand
You’ve seen how the top fashion brands earn AI visibility.
The path forward is simple: Consensus + Consistency.
Build consensus by getting people talking: Create shareable content, encourage customer posts, or work with creators and publications.
Build consistency by keeping your product info aligned across your site and retail partners.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-02 15:14:362026-01-02 15:14:36Fashion AI SEO: How to Improve Your Brand’s LLM Visibility
PPC didn’t stand still in 2025. It adjusted. These articles resonated because they answered the real questions advertisers are asking: how to stay competitive, cut wasted spend, work with automation instead of against it, and prepare for what’s next.
Below are links to the 10 most-read Search Engine Land PPC columns of 2025, written by our exceptional subject matter experts.
With the right strategy, even the smallest business can stand out, win customers, and make a lasting impact. Here’s how. (By Sophie Logan. Published Sept. 16.)
Shift your optimization mindset in 2025 with fresh strategies for keywords, Performance Max, and audience targeting. (By Pauline Jakober. Published Feb. 6.)
CPCs are rising – but how fast? Compare ad cost inflation to consumer price index and see what it means for your ad strategy. (By Mark Meyerson. Published April 16.)
AI-driven search is blurring the line between organic and paid. Learn how uniting SEO and PPC boosts visibility, intent, and brand authority. (By Jen Cornwell. Published Oct. 6.)
PPC scripts hit limits. Vibe coding removes the roadblocks. Turn complex seasonal patterns into simple, data-driven planning tools. (By Frederick Vallaeys. Published Aug. 21.)
Speed up your ad creation process without losing your message. Use generative AI to craft relevant, personalized copy that connects. (By Jason Tabeling. Published Aug. 1.)
These filtering tactics help refine your targeting, reduce spend on low-quality clicks, and uncover new keyword opportunities. (By Menachem Ani. Published July 22.)
Streamline campaign management with Google Ads scripts. Get insights, use cases, and practical tips for using automation to boost performance. (By Frederick Vallaeys. Published Jan. 9.)
Fewer clicks mean higher stakes. Win visibility with precise targeting, value-based bidding, and authority across paid and organic search. (By Sarah Stemen. Published Oct. 7.)
Some PPC practices no longer fit today’s automated Google Ads environment. Here’s what to phase out – and what to prioritize next year. (By Sarah Vlietstra. Published Nov. 4.)
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/ppc-columns-2025-search-engine-land-FOWDMV.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-30 14:00:002025-12-30 14:00:00Top 10 PPC expert columns of 2025 on Search Engine Land
Writing strong page titles is one of the simplest and most impactful SEO optimizations you can make. The title tag is often the first thing users see in search results, and it helps search engines understand the content of your page.
In this article, you’ll learn what SEO page titles are, why they matter, and how to write titles that improve visibility and attract clicks.
Key takeaways
Crafting a strong page title is vital for SEO; it attracts clicks and helps search engines understand your content
An SEO page title appears in search results and browser tabs, serving as the first impression for users
To optimize your page title, include relevant keywords and ensure it aligns with the content to improve your ranking
Yoast SEO provides tools to help check title width and keyword usage, and includes an AI-powered title generator
You can change the page title after publication, and doing so may significantly improve click-through rates
Let’s start with the basics. If you look at the source of a page (right-click on the page, then choose View Page Source), you find a title in the head section. It looks like this:
This is an example SEO title - Example.com
This is the HTML title tag, also called the SEO title. When you look something up in a search engine, you get a list of results that appear as snippets. The part that looks like a headline is the SEO title. The SEO title typically includes the post title but may also incorporate other elements, such as the site name. Or even emojis!
An example of a Google snippet with a favicon, site name, URL, meta description, and title in the largest font
In most cases, the SEO title is the first thing people see, even before they get on your site. In tabbed browsers, you will usually also see the SEO title in the page tab, as shown in the image below.
An SEO title in a browser tab
What’s the purpose of an SEO title?
Your SEO title aims to entice people to click on it, visit your website, read your post, or purchase your product. If your title is not good enough, people will ignore it and move on to other results. Essentially, there are two goals that you want to achieve with an SEO title:
It must help you rank for a keyword
It must make the user want to click through to your page
Google uses many signals when deciding your relevance for a specific keyword. While click-through rate is not a direct ranking factor, user interaction with search results can be a signal that a result matches search intent.
If your page ranks well but attracts few clicks, that may indicate your title doesn’t resonate with searchers. Improving your SEO title can increase clicks and help you perform better over time.
Additionally, as mentioned earlier, Google uses the SEO title specified for your website as a ranking input. So, it’s not just about those clicks; you also need to ensure that your title reflects the topic being discussed on your page and the keyword that you’re focusing on. The SEO title you use has a direct influence on your ranking.
Now that you know the importance of SEO titles, let’s look at how to evaluate and improve them. Tools like Yoast SEO (Free) can help by checking key elements such as title width and keyword usage. Yoast SEO Premium uses generative AI to create titles.
A smarter analysis in Yoast SEO Premium
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
Yoast SEO Premium includes an AI-powered title generator that can help you create SEO-friendly page titles based on your content and focus keyphrase. This can be useful for inspiration or for quickly generating alternatives when you’re unsure how to phrase a title.
As with any AI-generated content, it’s best to review and refine the suggested titles to ensure they align with your page’s intent, brand voice, and audience expectations.
Simply hit the Use AI button to have Yoast SEO Premium generate great titles for you
What does the empty title check in Yoast SEO do?
The empty title check in Yoast SEO Premium is self-explanatory: it checks whether you’ve filled in any text in your post’s ‘Title’ section. If you haven’t, you’ll see a red traffic light reminding you to add a title. Once this is filled in, the post title can be automatically added to the SEO title field using the ‘Title’ variable.
You can edit your titles in the Search appearance section of Yoast SEO
Note that your post title is output as an H1 heading. A clear H1 helps users quickly understand what a page is about, improves accessibility for screen readers, and aids search engines in interpreting the page structure. You should only use one H1 heading per page to avoid confusing search engines. Don’t worry; we’ve got a check for multiple H1 headings in Yoast SEO!
What does the SEO title width check in Yoast SEO do?
You will find this check in the SEO tab of the Yoast SEO sidebar or meta box. If you haven’t written an SEO title yet, this will remind you to do so. Additionally, Yoast SEO verifies the width of your SEO title. When it is too long, you will get a warning.
We used to warn you if your SEO title was too short, but we’ve changed that since our Yoast 17.1 release. A title with an optimal width gets you a green traffic light in the analysis. Remember that we exclude the separator symbol and site title from the title width check. We don’t consider these when calculating the SEO title progress bar.
You can find the SEO title width check in the Yoast SEO sidebar or the meta box
How to write an SEO title with an optimal width
If your SEO title doesn’t have the correct width, parts of it may be cut off in Google’s search results. The result may vary, depending on the device you’re using. That’s why you can also check how your SEO title will look in the mobile and desktop search results in the Search appearance section of Yoast SEO. The tool defaults to the mobile version, but you can also switch to view it in the desktop version.
Here’s a desktop result:
The Search appearance in Yoast SEO lets you switch between the mobile and desktop results
And here’s the mobile result for the same URL:
A mobile preview for this particular page
As a general guideline, aim for a title that fully displays on mobile search results, clearly communicates the main topic, and avoids unnecessary filler words. If your title fits visually and still reads naturally, you’re on the right track.
Width vs. Length
Have you noticed that we talk about width rather than length? Why is that? Rather than using a character count, Google has a fixed width for the titles counted in pixels. While your title tags can be long, and Google doesn’t have a set limit on the number of characters you can use, there is a limit on what’s visible in the search results. If your SEO title is too wide, Google will visually truncate it. That might be different from what you want. Additionally, avoid wasting valuable space by keeping the title concise and clear. Additionally, the SEO title often informs other title-like elements, such as the og:title, which also has display constraints.
Luckily, our Search appearance section can help you out! You can fill in your SEO title; our plugin will provide you with immediate feedback. The green line underneath the SEO title turns red when your title is too long. Keep an eye on that and use the feedback to create great headlines.
The Search appearance section in the Yoast SEO for WordPress block editor
The Google preview in Yoast SEO for Shopify
What does the keyphrase in the SEO title check in Yoast SEO do?
This check appears in the SEO tab of the Yoast SEO sidebar in WordPress and Shopify, as well as in the meta box in WordPress. It checks if you’re using your keyphrase in the SEO title of your post or page. This check is intentionally strict because the SEO title plays an important role in signaling a page’s topic to both search engines and users. Since Google uses the title to figure out your page’s topic, not having the focus keyphrase in the SEO title may harm your rankings. Additionally, potential visitors are more likely to click on a search result that matches their query. For optimal results, try to include your keyphrase at the beginning of the SEO title.
This check finds out if you’ve used your focus keyphrase in your SEO title
How to use your keyphrase in the SEO title
Sometimes, when optimizing for a highly competitive keyword, everyone will have the keyword at the beginning of the SEO title. In that case, you can try making it stand out by putting one or two words before your focus keyword, thereby slightly “indenting” your result. In Yoast SEO, if you start your SEO title with “the”, “a”, “who”, or another function word followed by your keyphrase, you’ll still get a green traffic light.
At other times, such as when you have a very long keyphrase, adding the complete keyphrase at the beginning doesn’t make sense. If your SEO title looks weird with the keyphrase at the beginning, try to add as much of the keyphrase as early in the SEO title as possible. But always keep an eye on the natural flow and readability.
How to reduce the chance of Google rewriting your SEO title
Google may rewrite titles when they are overly long, stuffed with keywords, misleading, or inconsistent with the page’s main heading.
To reduce the likelihood of rewrites:
Make sure your SEO title closely matches your page’s H1
Avoid excessive separators, repetition, or boilerplate text
Ensure the title accurately reflects the page content
While rewrites can still happen, clear and concise titles are more likely to be shown as written.
Want to learn how to write text that’s pleasant to read and optimized for search engines? Our SEO copywriting course can help you with that. You can access this course and our other SEO courses with Yoast SEO Premium. This also gives you access to extra features in the Yoast SEO plugin.
Are you struggling with more aspects of SEO copywriting? Don’t worry! We can teach you to master all facets, so you’ll know how to write awesome copy that ranks. Take a look at our SEO copywriting training and try the free trial lessons!
The post title, also known as the H1 heading, is the main heading users see on the page. Its primary role is to help readers understand what the page is about and to add structure to your content. You should always write your H1 with users in mind.
The SEO title is the title that appears in search results and in the browser tab. This title helps search engines understand the topic of your page and influences whether users click on your result.
While the SEO title and H1 can be similar, they do not need to be identical. In WordPress, tools like Yoast SEO allow you to set a separate SEO title, giving you more control over how your page appears in search results without changing the on-page heading.
Should you add your brand to the SEO title?
For quite some time, it was a common practice among some SEOs to omit the site name from the SEO title. The idea was that the “density” of the title mattered, and the site name wouldn’t help with that. Don’t do this. If possible, your SEO title should include your brand, preferably in a recognizable way. If people search for a topic and see your brand several times, even if they don’t click on it the first time, they might click when they see you again on their next page of results.
However, with the site name and favicon updates, be sure to fill in the site settings, upload a favicon, and make general changes to the design of the snippets. This will increase your brand’s visibility in search results. Today, you’ll notice that Google hardly shows your brand name in the snippet’s title. However, Google often has a mind of its own when generating titles to change them for any given reason. The design and function of the SERPs can change at any moment, so we still recommend adding your brand to your titles.
Can you change the SEO title after a page is published?
Yes. You can change the SEO title even after a page has been published, and doing so can improve performance.
At Yoast, we once noticed that although we ranked well for “WordPress security,” the page was not getting as much traffic as expected. We updated the SEO title and meta description to make them more engaging and relevant. As a result, traffic to that page increased by over 30 percent.
The original SEO title was:
WordPress Security • Yoast
We changed it to:
WordPress Security in a few easy steps! • Yoast
This change did not significantly affect rankings, but it did improve click-through rates. The keywords stayed largely the same, but the title became more compelling for searchers.
This shows that optimizing SEO titles after publication can be an effective way to increase traffic, especially if your page already ranks well but receives fewer clicks than expected.
Does Google always use the SEO title you set?
No. Google does not always display the exact SEO title you set in search results.
That said, the HTML title tag is still the most common source Google uses for generating title links. Google Search uses the following sources to automatically determine title links:
Google typically selects one title per page and does not change it for different queries.
What does this mean for you? The SEO title you set remains important for ranking and relevance. Even if Google sometimes displays a different version, your title still helps search engines understand the content of your page.
To stay on top of changes, monitor your key pages in Google Search Console, check how titles appear in search results, and watch for shifts in click-through rates.
Can you use the same title for SEO and social media?
You can, but it is often better not to.
What might be a good SEO title isn’t necessarily a good title for social media. In social media, keyword optimization is less important than creating a title that entices people to click. You often don’t need to include the brand name in the title. This is especially true for Facebook and X if you include some branding in your post image. Our social media appearance previews in Yoast SEO Premium and Yoast SEO for Shopify can help you.
If you use Yoast SEO, you can set different titles for Google, Facebook, and X. Enter your SEO title in the snippet editor, then customize the social media titles in the social tab. If you do not set a specific X title, X will use the Facebook title by default.
This flexibility allows you to optimize your titles for both search engines and social platforms without compromise.
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Another year in search has come and gone, and Google called it year three of a 10-year platform shift. In 2025, that shift became impossible to ignore. AI moved from experiments and previews into the core of how search actually works.
Below are the biggest SEO news stories of 2025 on Search Engine Land.
Note: This article doesn’t include any stories related to Google algorithm updates. Barry Schwartz wrote a separate recap on that, which will also publish today.
10. Perplexity ranking factors and systems
Independent researcher Metehan Yesilyurt analyzed browser-level interactions to reveal how Perplexity scores, reranks, and sometimes drops content. He uncovered a three-layer machine-learning reranker for entity searches, manual authority whitelists, and dozens of engagement and relevance signals.
Yesilyurt’s research also found boosts for authoritative domains, strong early performance, and topics centered on tech and AI. Rankings further reflected time decay, interconnected content clusters, and synchronized YouTube trends that increased visibility across platforms.
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9. Google Search Console Query groups
Google added Query groups to the Search Console Insights report. The feature uses AI to cluster similar search queries into clear audience topics and does not affect rankings. It rolled out gradually to high-volume sites and replaced long query lists with topic-level groupings that make performance shifts easier to spot.
HubSpot’s organic traffic appeared to fall from 13.5 million to 8.6 million in a month, with most of the losses coming from its blog. The drop followed several Google updates, and SEOs publicly pointed to thin, off-topic, traffic-at-all-costs content that drifted beyond HubSpot’s core expertise.
The SEO identity crisis continued as Google dismissed new acronyms like GEO (generative engine optimization) and AEO (answer engine optimization), arguing that good SEO is good GEO, and that the same fundamentals drive AI Overview rankings.
That stance collided with Google’s own admission that search traffic decline is inevitable as AI answers replace clicks, even while traditional search still dominates discovery at a massive scale.
Yet, search behavior is fracturing: users turn to AI for quick answers and to Google for deeper research, pushing brands to optimize for visibility, not just traffic.
Google rapidly expanded AI Mode from an opt-in experiment into a widely available, and possibly soon default, search experience. It added deeper research, agentic actions, personalization, and Gemini 2.5, signaling longer and more complex search behavior.
At the same time, AI Mode exposed major transparency gaps. It initially broke referral tracking and still blends performance data into standard Search Console reports, raising new concerns about visibility, attribution, and what SEO becomes as AI takes on a larger role in search.
Cloudflare CEO Matthew Prince said AI was breaking the web’s search-driven business model. He said Google scraped far more content while sending back much less traffic because of zero-click results. He added that AI companies deepen the imbalance by consuming huge amounts of content with little return to creators, putting original publishing at risk unless the economic model changes.
Statcounter data showed Google’s global search share fell below 90% in October, November, and December 2024, the first time its search share remained under 90% since early 2015. The decline was driven mainly by Asia, alongside a December U.S. dip to 87.39%. Bing, Yandex, and Yahoo captured much of the lost share.
Google tightened its stance on AI-generated content by telling quality raters to give the Lowest ratings to pages where most main content is auto- or AI-generated with little originality or added value. It also expanded its spam definitions to target scaled, low-effort AI use.
At the same time, Google tested AI-generated and AI-summarized search snippets, pointing to a future where AI both judges content more harshly and increasingly controls how that content appears in search.
Analyses from Seer, Ahrefs, Amsive, and BrightEdge all showed the same pattern. Google Search produced more impressions and more AI Overview visibility, but sent fewer clicks. The drop was sharpest on non-branded, informational queries, where AI Overviews pushed classic results down, and CTR fell hard.
The studies also found a winner-take-some dynamic. Brands cited in AI Overviews saw higher paid and organic CTR, while those left out lost ground, showing that AI visibility increasingly drives results.
Google’s removal of the long-standing &num=100 search parameter disrupted SEO data across the industry. It broke rank-tracking tools and coincided with sharp drops in Google Search Console impressions and query counts.
Early analysis showed most sites lost reported visibility, especially beyond Page 1. The change suggested years of inflated metrics from scrapers and a new, possibly more accurate, view of organic performance.
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SEO didn’t stand still in 2025. It didn’t reinvent itself either. It clarified what actually matters. If you followed The SEO Update by Yoast monthly webinars this year, you’ll recognize the pattern. Throughout 2025, our Principal SEOs, Carolyn Shelby and Alex Moss, cut through the noise to explain not just what was changing but why it mattered as AI-powered search reshaped visibility, trust, and performance. If you missed some sessions or want the full picture in one place, this wrap-up is for you. We’re looking back at how SEO evolved over the year, what those changes mean in practice, and what they signal going forward.
Key takeaways
In 2025, SEO shifted its focus from rankings to visibility management, as AI-driven search reshaped priorities
Key developments included the rise of AI Overviews, a shift from clicks to citations, and increased importance of clarity and trust
Brands needed to prioritize structured, credible content that AI systems could easily interpret to remain visible
By December, SEO transformed to retrieval-focused strategies, where success rested on clarity, relevance, and E-E-A-T signals
Overall, 2025 clarified that the fundamentals still matter but emphasized the need for precision in content for AI-driven systems
AI-powered, personalized search accelerated. Zero-click results increased. Brand signals, E-E-A-T, performance, and schema shifted from optimizations to requirements.
SEO expanded from ranking pages to representing trusted brands that machines can understand.
February
Massive AI infrastructure investments. AI Overviews pushed organic results down. Traffic dropped while brand influence and revenue held steady.
SEO outcomes can no longer be measured by traffic alone. Authority and influence matter more than raw clicks.
March
AI Overviews expanded as clicks declined. Brand mentions appeared to play a larger role in AI-driven citation and selection behavior than links alone. Search behavior grew despite fewer referrals.
Visibility fractured across systems. Trust and brand recognition became the differentiators for inclusion.
April
Schema and structure proved essential for AI interpretation. Multimodal and personalized search expanded. Zero-click behavior increased further.
SEO shifted from optimization to interpretation. Clarity and structure determine reuse.
May
Discovery spread beyond Google. AI Overviews reached mass adoption. Citations replaced visits as success signals.
SEO outgrew the SERP. Presence across platforms and AI systems became critical.
June – July
AI Mode became core to search. Ads entered AI answers. Indexing alone no longer offers guaranteed visibility. Reporting lagged behind reality.
Traditional SEO remained necessary but insufficient. Resilience and adaptability became essential.
August
Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond the number of sessions.
Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond sessions.
September
AI Mode neared default status. Legal, licensing, and attribution pressures intensified. Persona-based strategies gained relevance.
Control over visibility is no longer guaranteed. Trust and credibility are the only durable advantages.
October
Search Console data reset expectations. AI citations outweighed rankings. AI search became the destination.
SEO success depends on presence inside AI systems, not just SERP positions.
November
AI Mode became core to search. Ads entered AI answers. Indexing alone is no longer a guarantee of visibility. Reporting lagged behind reality.
Clarity and structure beat scale. Authority decides inclusion.
December
SEO fully shifted to retrieval-based logic. AI systems extracted answers, not pages. E-E-A-T acted as a gatekeeper.
SEO evolved into visibility management for AI-driven search. Precision replaced volume.
January: SEO enters the age of representation
January set the tone for the year. Not through a single disruptive update, but through a clear signal that SEO was moving away from pure rankings toward something broader. The search was becoming more personalized, AI-driven, and selective about which sources it chose to surface. Visibility was no longer guaranteed just because you ranked well.
From the start of the year, it was clear that SEO in 2025 would reward brands that were trusted, technically sound, and easy for machines to understand.
What changed in January
Here are a few clear trends that began to shape how SEO worked in practice:
AI-powered search became more personalized: Search results reflected context more clearly, taking into account location, intent, and behavior. The same query no longer produced the same result for every user
Zero-click searches accelerated: More answers appeared directly in search results, reducing the need to click through, especially for informational and local queries
Brand signals and reviews gained weight: Search leaned more heavily on real-world trust indicators like brand mentions, reviews, and overall reputation
E-E-A-T became harder to ignore: Clear expertise, ownership, and credibility increasingly acted as filters, not just quality guidelines
The role of schema started to shift: Structured data mattered less for visual enhancements and more for helping machines understand content and entities
What to take away from January
January wasn’t about tactics. It was about direction.
SEO started rewarding clarity over cleverness. Brands over pages. Trust over volume. Performance over polish. If search engines were going to summarize, compare, and answer on your behalf, you needed to make it easy for them to understand who you are, what you offer, and why you are credible.
That theme did not fade as the year went on. It became the foundation for everything that followed.
February: scale, money, and AI made the shift unavoidable
If January showed where search was heading, February showed how serious the industry was about getting there. This was the month where AI stopped feeling like a layer on top of search and started looking like the foundation underneath it.
Massive investments, changing SERP layouts, and shifting performance metrics all pointed to the same conclusion. Search was being rebuilt for an AI-first world.
What changed in February
As the month unfolded, the signs became increasingly difficult to ignore.
AI Overviews pushed organic results further down: AI Overviews appeared in a large share of problem-solving queries, favoring authoritative sources and summaries over traditional organic listings
Traffic declined while brand value increased: High-profile examples showed sessions dropping even as revenue grew. Visibility, influence, and brand trust started to matter more than raw sessions
AI referrals began to rise: Referral traffic from AI tools increased, while Google’s overall market share showed early signs of pressure. Discovery started spreading across systems, not just search engines
What to take away from February
February made January’s direction feel permanent.
When AI systems operate at this scale, they change how visibility works. Rankings still mattered, but they no longer told the full story. Authority, brand recognition, and trust increasingly influenced whether content was surfaced, summarized, or ignored.
The takeaway was clear. SEO could no longer be measured only by traffic. It had to be understood in terms of influence, representation, and relevance across an expanding search ecosystem.
March: visibility fractured, trust became the differentiator
By March, the effects of AI-driven search were no longer theoretical. The conversation shifted from how search was changing to who was being affected by it, and why.
This was the month where declining clicks, citation gaps, and publisher pushback made one thing clear. Search visibility was fragmenting across systems, and trust became the deciding factor in who stayed visible.
What changed in March
The developments in March added pressure to trends that had already been forming earlier in the year.
AI Overviews expanded while clicks declined: Studies showed that AI Overviews appeared more frequently, while click-through rates continued to decline. Visibility increasingly stopped at the SERP
Brand mentions mattered more than links alone: Citation patterns across AI platforms varied, but one signal stayed consistent. Brands mentioned frequently and clearly were more likely to surface
Search behavior continued to grow despite fewer clicks: Overall search volume increased year over year, showing that users weren’t searching less; they were just clicking less
AI search struggled with attribution and citations: Many AI-powered results failed to cite sources consistently, reinforcing the need for strong brand recognition rather than reliance on direct referrals
Search experiences became more fragmented: New entry points like Circle to Search and premium AI modes introduced additional layers to discovery, especially among younger users
Structured signals evolved for AI retrieval: Updates to robots meta tags, structured data for return policies, and “sufficient context” signals showed search engines refining how content is selected and grounded
March exposed the tension at the heart of modern SEO.
Search demand was growing, but traditional traffic was shrinking. AI systems were answering more questions, but often without clear attribution. In that environment, being a recognizable, trusted brand mattered more than being the best-optimized page.
The implication was simple. SEO was no longer just about earning clicks. It was about earning inclusion, recognition, and trust across systems that don’t always send users back.
April: machines started deciding how content is interpreted
By April, the focus shifted again. The question was no longer whether AI would shape search, but how machines decide what content means and when to surface it.
After March exposed visibility gaps and attribution issues, April zoomed in on interpretation. How AI systems read, classify, and extract information became central to SEO outcomes.
What changed in April
April brought clarity to how modern search systems process content.
Schema has proven its value beyond rankings: Microsoft has confirmed that schema markup helps large language models understand content. Bing Copilot used structured data to generate clearer, more reliable answers, reinforcing the schema’s role in interpretation rather than visual enhancement
AI-driven search became multimodal: Image-based queries expanded through Google Lens and Gemini, allowing users to search using photos and visuals instead of text alone
AI Overviews expanded during core updates: A noticeable surge in AI Overviews appeared during Google’s March core update, especially in travel, entertainment, and local discovery queries
Clicks declined as summaries improved: AI-generated content summaries reduced the need to click through, accelerating zero-click behavior across informational and decision-based searches
Content structure mattered more than special optimizations: Clear headings that boost readability, lists, and semantic cues helped AI systems extract meaning. There were no shortcuts. Standard SEO best practices carried the weight
What to take away from April
April shifted SEO from optimization to interpretation.
Search engines and AI systems didn’t just look for relevance. They looked for clarity. Content that was well-structured, semantically clear, and grounded in real entities was easier to understand, summarize, and reuse.
The lesson was subtle but important. You didn’t need new tricks for AI search. You needed content that was easier for machines to read and harder to misinterpret.
By May, it was no longer sufficient to discuss how search engines interpret content. The bigger question became where discovery was actually happening.
SEO started expanding beyond Google. Visibility fractured across platforms, AI tools, and ecosystems, forcing brands to think about presence rather than placement.
What changed in May
The month highlighted how search and discovery continued to decentralize.
Search behavior expanded beyond traditional search engines: Around 39% of consumers now use Pinterest as a search engine, with Gen Z leading adoption. Discovery increasingly happened inside platforms, not just through search bars
AI Overviews reached mass adoption: AI Overviews reportedly reached around 1.5 billion users per month and appeared in roughly 13% of searches, with informational queries driving most of that growth
Clicks continued to give way to citations: As AI summaries became more common, being referenced or cited mattered more than driving a visit, especially for top-of-funnel queries
AI-powered search diversified across tools: Chat-based search experiences added shopping, comparison, and personalization features, further shifting discovery away from classic result pages
Economic pressure on content ecosystems increased: Industry voices warned that widespread zero-click answers were starting to weaken the incentives for content creation across the web
May reframed SEO as a visibility problem, not a traffic problem.
When discovery happens across platforms, summaries, and AI systems, success depends on how clearly your content communicates meaning, credibility, and relevance. Rankings still mattered, but they were no longer the primary measure of success.
The message was clear. SEO had outgrown the SERP. Brands that focused on authenticity, semantic clarity, and structured information were better positioned to stay visible wherever search happened next.
By early summer, SEO entered a more uncomfortable phase. Visibility still mattered, but control over how and where content appeared became increasingly limited.
June and July were about adjustment. Search moved closer to AI assistants, ads blended into answers, and traditional SEO signals no longer guaranteed exposure across all search surfaces.
What changed in June and July
This period introduced some of the clearest operational shifts of the year.
AI Mode became a first-class search experience: AI Mode was rolled out more broadly, including incognito use, and began to merge into core search experiences. Search was no longer just results. It was conversation, summaries, and follow-ups
Ads entered AI-generated answers: Google introduced ads inside AI Overviews and began testing them in conversational AI Mode. Visibility now competes not only with other pages, but with monetized responses
Measurement lagged behind reality: Search Console confirmed AI Mode data would be included in performance reports, but without separate filters or APIs. Visibility changed more rapidly than reporting tools could keep pace.
Citations followed platform-specific preferences: Different AI systems favored different sources. Some leaned heavily on encyclopedic content, others on community-driven platforms, reinforcing that one SEO strategy would not fit every system
Most AI-linked pages still ranked well organically: Around 97% of URLs referenced in AI Mode ranked in the top 10 organic results, showing that strong traditional SEO remained a prerequisite, even if it was no longer sufficient
Content had to resist summarization: Leaks and tests showed that some AI tools rarely surfaced links unless live search was triggered. Generic, easily summarized modern content became easier to replace
Infrastructure became an SEO concern again: AI agents increased crawl and request volume, pushing performance, caching, and server readiness back into focus
Search moved beyond text: Voice-based interactions, audio summaries, image-driven queries, and AI-first browsers expanded how users searched and consumed information
What to take away from June and July
This period forced a mindset shift.
SEO could no longer assume that ranking, indexing, or even traffic guaranteed visibility. AI systems decided when to summarize, when to cite, and when to bypass pages entirely. Ads, assistants, and alternative interfaces now often sit between users and websites more frequently than before.
The conclusion was pragmatic. Strong fundamentals still mattered, but they weren’t the finish line. SEO now requires resilience: content that carries authority, resists simplification, loads fast, and stays relevant even when clicks don’t follow.
By the end of July, one thing was clear. SEO wasn’t disappearing. It was operating under new constraints, and the rest of the year would test how well teams adapted to them.
August: the gap between visibility and value widened
By August, SEO teams were staring at a growing disconnect. Visibility was increasing, but traditional outcomes were harder to trace back to it.
This was the month when the mechanics of AI-driven search became more transparent and more uncomfortable.
What changed in August
August surfaced the operational realities behind AI-powered discovery.
Impressions rose while clicks continued to decline: AI Overviews dominated the results, driving exposure without generating traffic. In some cases, conversions still improved, but attribution became harder to prove
The “great decoupling” became measurable: Visibility and performance stopped moving in sync. SEO teams saw growth in impressions even as sessions declined
Zero-click searches accelerated further: No-click behavior climbed toward 69%, reinforcing that many user journeys now ended inside search interfaces
AI traffic stayed small but influential: AI-driven referrals still accounted for under 1% of traffic for most sites, yet they shaped expectations around answers, speed, and convenience
Retrieval logic shifted toward context and intent: New retrieval approaches prioritized meaning, relationships, and query context over keyword matching
It reinforced the reality that SEO could no longer rely on traffic as the primary proof of value. Visibility still mattered, but only when paired with outcomes that could survive reduced clicks and blurred attribution.
The lesson was strategic. SEO needed to connect visibility to conversion, brand lift, or long-term trust, not just sessions. Otherwise, its impact would be increasingly hard to defend.
September: control, attribution, and trust were renegotiated
September pushed the conversation further. It wasn’t just about declining clicks anymore. It was about who controlled discovery, attribution, and access to content.
This was the month where legal, technical, and strategic pressures collided.
What changed in September
September reframed SEO around governance and credibility.
AI Mode moved closer to becoming the default: Search experiences shifted toward AI-driven answers with conversational follow-ups and multimodal inputs
The decline of the open web was acknowledged publicly: Court filings and public statements confirmed what many publishers were already feeling. Traditional web traffic was under structural pressure
Legal scrutiny intensified: High-profile settlements and lawsuits highlighted growing challenges around training data, summaries, and lost revenue
Licensing entered the SEO conversation: New machine-readable licensing approaches emerged as early attempts to restore control and consent
Snippet visibility became a gateway signal: AI tools relied heavily on search snippets for real-time answers, making concise, extractable content more critical
Persona-based strategies gained traction: SEO began shifting from keyword targeting to persona-driven content aligned with how AI systems infer intent
Trust eroded around generic, formulaic, AI writing styles: Formulaic, overly polished AI content raised credibility concerns, reinforcing the need for editorial judgment
Measurement tools lost stability again: Changes to search parameters disrupted rank tracking, reminding teams that SEO reporting would remain volatile
What to take away from September
September forced SEO to grow up again.
Control over visibility, attribution, and content use was no longer guaranteed. Trust, clarity, and credibility became the only durable advantages in an ecosystem shaped by AI intermediaries.
The takeaway was sobering but useful. SEO could still drive value, but only when it is aligned with real user needs, strong brand signals, and content that earned its place in AI-driven answers.
October marked a turning point in how SEO performance needed to be interpreted. The data didn’t just shift. It reset expectations entirely.
This was the month when SEO teams had to accept that AI-powered search was no longer a layer on top of results. It was becoming the place where searches ended.
What changed in October
October brought clarity, even if the numbers looked uncomfortable.
AI Mode reshaped user behavior: Around a third of searches now involve AI agents, with most sessions staying inside AI panels. Clicks became the exception, not the default
AI citations increasingly rivalled rankings: Visibility increasingly depended on whether content was selected, summarized, or cited by AI systems, not where it ranked
Search engines optimized for ideas, not pages: Guidance from search platforms reinforced that AI systems extract concepts and answers, not entire URLs
Metadata lost some direct control: Tests of AI-generated meta descriptions suggested that manual optimization would carry less influence over how content appears
Commerce and search continued to merge: AI-driven shopping experiences expanded, signaling that transactional intent would increasingly be handled inside AI interfaces
What to take away from October
October reframed SEO as presence within AI systems.
Traffic still mattered, but it was no longer the primary outcome. The real question became whether your content appeared at all inside AI-driven answers. Clarity, structure, and extractability replaced traditional ranking gains as the most reliable levers.
From this point on, SEO had to treat AI search as a destination, not just a gateway.
November: structure and credibility decided inclusion
If October reset expectations, November showed what actually worked.
This month narrowed the gap between theory and practice. It became clearer why some content consistently surfaced in AI results, while other content disappeared.
What changed in November
November focused on how AI systems select and trust sources.
Structured content outperformed clever content: Clear headings, predictable formats, and direct answers made it easier for AI systems to extract and reuse information
Schema supported understanding, not visibility alone: Structured data remained valuable, but only when paired with clean, readable on-page content
AI-driven shopping and comparisons accelerated: Product data quality, consistency, and accessibility directly influenced whether brands appeared in AI-assisted decision flows
Citation pools stayed selective: AI systems relied on a relatively small set of trusted sources, reinforcing the importance of brand recognition and authority
Search tooling evolved toward themes, not keywords: Grouped queries and topic-based insights replaced one-keyword performance views
What to take away from November
November made one thing clear. SEO wasn’t about producing more content or optimizing harder. It was about making content easier to understand and harder to ignore.
Clarity beats creativity. Structure beat scale. Authority determined whether content was reused at all.
This month quietly reinforced the fundamentals that would define SEO going forward.
Instead of introducing new disruptions, it clarified what 2025 had been building toward all along. SEO was no longer primarily about ranking pages. It was about enabling retrieval.
What changed in December
The year-end review highlighted the new reality of SEO.
Search systems retrieved answers, not pages: AI-driven search experiences pulled snippets, definitions, and summaries instead of directing users to full articles
Literal language still mattered: Despite advances in understanding, AI systems relied heavily on exact phrasing. Terminology choices directly affected retrieval
Content structure became mandatory: Front-loaded answers, short paragraphs, lists, and clear sections made content usable for AI systems
Relevance replaced ranking as the core signal: Being the clearest and most contextually relevant answer mattered more than traditional ranking factors
E-E-A-T acted as a gatekeeper: Recognized expertise, authorship, and trust signals determined whether content was eligible for reuse
Authority reduced AI errors: Strong credibility signals helped AI systems select more reliable sources and reduced hallucinated answers
What to take away from December
December didn’t declare the end of SEO. It defined its next phase.
SEO matured into visibility management for AI-driven systems. Success depended on clarity, credibility, and structure, not shortcuts or volume. The fundamentals still worked, but only when applied with discipline.
By the end of 2025, the direction was clear. SEO didn’t get smaller. It got more precise.
SEO evolved into visibility management for AI-driven search. Precision replaced volume.
2025 didn’t rewrite SEO. It clarified it.
Search moved from ranking pages to retrieving answers. From rewarding volume to rewarding clarity. From clicks to credibility. And from optimization tricks to systems-level understanding.
The fundamentals still matter. Technical health, helpful content, and strong SEO foundations are non-negotiable. But they are no longer the finish line. What separates visible brands from invisible ones now is how clearly their content can be understood, trusted, and reused by AI-driven search systems.
Going into 2026, the goal isn’t to outsmart search engines. It’s to make your expertise unmistakable. Write for humans, structure for machines, and build authority that holds up even when clicks don’t follow.
SEO didn’t get smaller this year. It got more precise. Stay with us for our 2026 verdict on where search goes next.
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