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Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it

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Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it

Rand Fishkin just published the most important piece of primary research the AI visibility industry has seen so far.

His conclusion – that AI tools produce wildly inconsistent brand recommendation lists, making “ranking position” a meaningless metric – is correct, well-evidenced, and long overdue.

But Fishkin stopped one step short of the answer that matters.

He didn’t explore why some brands appear consistently while others don’t, or what would move a brand from inconsistent to consistent visibility. That solution is already formalized, patent pending, and proven in production across 73 million brand profiles.

When I shared this with Fishkin directly, he agreed. The AI models are pulling from a semi-fixed set of options, and the consistency comes from the data. He just didn’t have the bandwidth to dig deeper, which is fair enough, but the digging has been done – I’ve been doing it for a decade.

Here’s what Fishkin found, what it actually means, and what the data proves about what to do about it.

Fishkin’s data killed the myth of AI ranking position

Fishkin and Patrick O’Donnell ran 2,961 prompts across ChatGPT, Claude, and Google AI, asking for brand recommendations across 12 categories. The findings were surprising for most.

Fewer than 1 in 100 runs produced the same list of brands, and fewer than 1 in 1,000 produced the same list in the same order. These are probability engines that generate unique answers every time. Treating them as deterministic ranking systems is – as Fishkin puts it – “provably nonsensical,” and I’ve been saying this since 2022. I’m grateful Fishkin finally proved it with data.

But Fishkin also found something he didn’t fully unpack. Visibility percentage – how often a brand appears across many runs of the same prompt – is statistically meaningful. Some brands showed up almost every time, while others barely appeared at all.

That variance is where the real story lies.

Fishkin acknowledged this but framed it as a better metric to track. The real question isn’t how to measure AI visibility, it’s why some brands achieve consistent visibility and others don’t, and what moves your brand from the inconsistent pile to the consistent pile.

That’s not a tracking problem. It’s a confidence problem.

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AI systems are confidence engines, not recommendation engines

AI platforms – ChatGPT, Claude, Google AI, Perplexity, Gemini, all of them – generate every response by sampling from a probability distribution shaped by:

  • What the model knows.
  • How confidently it knows it.
  • What it retrieved at the moment of the query.

When the model is highly confident about an entity’s relevance, that entity appears consistently. When the model is uncertain, the entity sits at a low probability weight in the distribution – included in some samples, excluded in others – not because the selection is random but because the AI doesn’t have enough confidence to commit.

That’s the inconsistency Fishkin documented, and I recognized it immediately because I’ve been tracking exactly this pattern since 2015. 

  • City of Hope appearing in 97% of cancer care responses isn’t luck. It’s the result of deep, corroborated, multi-source presence in exactly the data these systems consume. 
  • The headphone brands at 55%-77% are in a middle zone – known, but not unambiguously dominant. 
  • The brands at 5%-10% have low confidence weight, and the AI includes them in some outputs and not others because it lacks the confidence to commit consistently. 

Confidence isn’t just about what a brand publishes or how it structures its content. It’s about where that brand stands relative to every other entity competing for the same query – a dimension I’ve recently formalized as Topical Position.

I’ve formalized this phenomenon as “cascading confidence” – the cumulative entity trust that builds or decays through every stage of the algorithmic pipeline, from the moment a bot discovers content to the moment an AI generates a recommendation. It’s the throughline concept in a framework I published this week.

Dig deeper: Search, answer, and assistive engine optimization: A 3-part approach

Every piece of content passes through 10 gates before influencing an AI recommendation

The pipeline is called DSCRI-ARGDW – discovered, selected, crawled, rendered, indexed, annotated, recruited, grounded, displayed, and won. That sounds complicated, but I can summarize it in a single question that repeats at every stage: How confident is the system in this content?

  • Is this URL worth crawling? 
  • Can it be rendered correctly? 
  • What entities and relationships does it contain? 
  • How sure is the system about those annotations? 
  • When the AI needs to answer a question, which annotated content gets pulled from the index? 

Confidence at each stage feeds the next. A URL from a well-structured, fast-rendering, semantically clean site arrives at the annotation stage with high accumulated confidence before a single word of content is analyzed. A URL from a slow, JavaScript-heavy site with inconsistent information arrives with low confidence, even if the actual content is excellent.

This is pipeline attenuation, and here’s where the math gets unforgiving. The relationship is multiplicative, not additive:

  • C_final = C_initial × ∏τᵢ

In plain English, the final confidence an AI system has in your brand equals the initial confidence from your entity home multiplied by the transfer coefficient at every stage of the pipeline. The entity home – the canonical web property that anchors your entity in every knowledge graph and every AI model – sets the starting confidence, and then each stage either preserves or erodes it. 

Maintain 90% confidence at each of 10 stages, and end-to-end confidence is 0.9¹⁰ = 35%. At 80% per stage, it’s 0.8¹⁰ = 11%. One weak stage – say 50% at rendering because of heavy JavaScript – drops the total from 35% to 19% even if every other stage is at 90%. One broken stage can undo the work of nine good ones.

This multiplicative principle isn’t new, and it doesn’t belong to anyone. In 2019, I published an article, How Google Universal Search Ranking Works: Darwinism in Search, based on a direct explanation from Google’s Gary Illyes. He described how Google calculates ranking “bids” by multiplying individual factor scores rather than adding them. A zero on any factor kills the entire bid, no matter how strong the other factors are.

Google applies this multiplicative model to ranking factors within a single system, and nobody owns multiplication. But what the cascading confidence framework does is apply this principle across the full 10-stage pipeline, across all three knowledge graphs.

The system provides measurable transfer coefficients at every transition and bottleneck detection that identifies exactly where confidence is leaking. The math is universal, but the application to a multi-stage, multi-graph algorithmic pipeline is the invention.

This complete system is the subject of a patent application I filed with the INPI titled “Système et procédé d’optimisation de la confiance en cascade à travers un pipeline de traitement algorithmique multi-étapes et multi-graphes.” It’s not a metaphor, it’s an engineered system with an intellectual lineage going back seven years to a principle a Google engineer confirmed to me in person.

Fishkin measured the output – the inconsistency of recommendation lists. But the output is a symptom, and the cause is confidence loss at specific stages of this pipeline, compounded across multiple knowledge representations.

You can’t fix inconsistency by measuring it more precisely. You can only fix it by building confidence at every stage.

The corroboration threshold is where AI shifts from hesitant to assertive

There’s a specific transition point where AI behavior changes. I call it the “corroboration threshold” – the minimum number of independent, high-confidence sources corroborating the same conclusion about your brand before the AI commits to including it consistently.

Below the threshold, the AI hedges. It says “claims to be” instead of “is,” it includes a brand in some outputs but not others, and the reason isn’t randomness but insufficient confidence.

The brand sits in the low-confidence zone, where inconsistency is the predictable outcome. Above the threshold, the AI asserts – stating relevance as fact, including the brand consistently, operating with the kind of certainty that produces City of Hope’s 97%.

My data across 73 million brand profiles places this threshold at approximately 2-3 independent, high-confidence sources corroborating the same claim as the entity home. That number is deceptively small because “high-confidence” is doing the heavy lifting – these are sources the algorithm already trusts deeply, including Wikipedia, industry databases, and authoritative media. 

Without those high-authority anchors, the threshold rises considerably because more sources are needed and each carries less individual weight. The threshold isn’t a one-time gate. Once crossed, the confidence compounds with every subsequent corroboration, which is why brands that cross it early pull further ahead over time, while brands that haven’t crossed it yet face an ever-widening gap.

Not identical wording, but equivalent conviction. The entity home states, “X is the leading authority on Y,” two or three independent, authoritative third-party sources confirm it with their own framing, and the AI encodes it as fact.

This fact is visible in my data, and it explains exactly why Fishkin’s experiment produced the results it did. In narrow categories like LA Volvo dealerships or SaaS cloud computing providers – where few brands exist and corroboration is dense – AI responses showed higher pairwise correlation. 

In broad categories like science fiction novels – where thousands of options exist and corroboration is thin – responses were wildly diverse. The corroboration threshold aligns with Fishkin’s findings.

Dig deeper: The three AI research modes redefining search – and why brand wins

Authoritas proved that fabricated entities can’t fool AI confidence systems

Authoritas published a study in December 2025 – “Can you fake it till you make it in the age of AI?” – that tested this directly, and the results confirm that Cascading Confidence isn’t just theory. Where Fishkin’s research shows the output problem – inconsistent lists – Authoritas shows the input side.

Authoritas investigated a real-world case where a UK company created 11 entirely fictional “experts” – made-up names, AI-generated headshots, faked credentials. They seeded these personas into more than 600 press articles across UK media, and the question was straightforward: Would AI models treat these fake entities as real experts?

The answer was absolute: Across nine AI models and 55 topic-based questions – “Who are the UK’s leading experts in X?” – zero fake experts appeared in any recommendation. Six hundred press articles, and not a single AI recommendation. That might seem to contradict a threshold of 2-3 sources, but it confirms it. 

The threshold requires independent, high-confidence sources, and 600 press articles from a single seeding campaign are neither independent – they trace to the same origin – nor high-confidence – press mentions sit in the document graph only.

The AI models looked past the surface-level coverage and found no deep entity signals – no entity home, no knowledge graph presence, no conference history, no professional registration, no corroboration from the kind of authoritative sources that actually move the needle.

The fake personas had volume, they had mentions, but what they lacked was cascading confidence – the accumulated trust that builds through every stage of the pipeline. Volume without confidence means inconsistent appearance at best, while confidence without volume still produces recommendations.

AI evaluates confidence — it doesn’t count mentions. Confidence requires multi-source, multi-graph corroboration that fabricated entities fundamentally can’t build.

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AI citability concentration increased 293% in under two months

Authoritas used the weighted citability score, or WCS, a metric that measures how much AI engines trust and cite entities, calculated across ChatGPT, Gemini, and Perplexity using cross-context questions.

I have no influence over their data collection or their results. Fishkin’s methodology and Authoritas’ aren’t identical. Fishkin pinged the same query repeatedly to measure variance, while Authoritas tracks varied queries on the same topic. That said, the directional finding is consistent.

Their dataset includes 143 recognized digital marketing experts, with full snapshots from the original study by Laurence O’Toole and Authoritas in December 2025 and their latest measurement on Feb. 2. The pattern across the entire dataset tells a story that goes far beyond individual scores.

  • The top 10 experts captured 30.9% of all citability in December. By February, they captured 59.5% – a 92% increase in concentration in under two months.
  • The HHI, or Herfindahl-Hirschman Index, the standard measure of market concentration, rose from 0.026 to 0.104 – a 293% increase in concentration. This happened while the total expert pool widened from 123 to 143 tracked entities.

More experts are being cited, the field is getting bigger, and the top is pulling away faster. Dominance is compounding while the long tail grows.

This is cascading confidence at population scale. The experts who actively manage their digital footprint – clean entity home, corroborated claims, consistent narrative across the algorithmic trinity – aren’t just maintaining their position, they’re accelerating away from everyone else.

Each cycle of AI training and retrieval reinforces their advantage – confident entities generate confident AI outputs, which build user trust, which generate positive engagement signals, which further reinforce the AI’s confidence. It’s a flywheel, and once it’s spinning, it becomes very, very hard for competitors to catch up.

At the individual level, the data confirms the mechanism. I lead the dataset at a WCS of 23.50, up from 21.48 in December, a gain of +2.02. That’s not because I’m more famous than everyone else on the list.

It’s because we’ve been systematically building my cascading confidence for years – clean entity home, corroborated claims across the algorithmic trinity, consistent narrative, structured data, deep knowledge graph presence.

I’m the primary test case because I’m in control of all my variables – I have a huge head start. In a future article, I’ll dig into the details of the scores and why the experts have the scores they do.

The pattern across my client base mirrors the population data. Brands that systematically clean their digital footprint, anchor entity confidence through the entity home, and build corroboration across the algorithmic trinity don’t just appear in AI recommendations.

They appear consistently, their advantage compounds over time, and they exit the low-confidence zone to enter the self-reinforcing recommendation set.

Dig deeper: From SEO to algorithmic education: The roadmap for long-term brand authority

AI retrieves from three knowledge representations simultaneously, not one

AI systems pull from what I call the Three Graphs model – the algorithmic trinity – and understanding this explains why some brands achieve near-universal visibility while others appear sporadically.

  • The entity graph, or knowledge graph, contains explicit entities with binary verified edges and low fuzziness – either a brand is in, or it’s not.
  • The document graph, or search engine index, contains annotated URLs with scored and ranked edges and medium fuzziness.
  • The concept graph, or LLM parametric knowledge, contains learned associations with high fuzziness, and this is where the inconsistency Fishkin documented comes from.

When retrieval systems combine results from multiple sources – and they do, using mechanisms analogous to reciprocal rank fusion – entities present across all three graphs receive a disproportionate boost.

The effect is multiplicative, not additive. A brand that has a strong presence in the knowledge graph and the document index and the concept space gets chosen far more reliably than a brand present in only one.

This explains a pattern Fishkin noticed but didn’t have the framework to interpret – why visibility percentages clustered differently across categories. The brands with near-universal visibility aren’t just “more famous,” they have dense, corroborated presence across all three knowledge representations. The brands in the inconsistent pool are typically present in only one or two. 

The Authoritas fake expert study confirms this from the negative side. The fake personas existed only in the document graph, press articles, with zero entity graph presence and negligible concept graph encoding. One graph out of three, and the AI treated them accordingly.

What I tell every brand after reading Fishkin’s data

Fishkin’s recommendations were cautious – visibility percentage is a reasonable metric, ranking position isn’t, and brands should demand transparent methodology from tracking vendors. All fair, but that’s analyst advice. What follows is practitioner advice, based on doing this work in production.

Stop optimizing outputs and start optimizing inputs

The entire AI tracking industry is fixated on measuring what AI says about you, which is like checking your blood pressure without treating the underlying condition. Measure if it helps, but the work is in building confidence at every stage of the pipeline, and that’s where I focus my clients’ attention from day one.

Start at the entity home

My experience clearly demonstrates that this single intervention produces the fastest measurable results. Your entity home is the canonical web property that should anchor your entity in every knowledge graph and every AI model. If it’s ambiguous, hedging, or contradictory with what third-party sources say about you, it is actively training AI to be uncertain. 

I’ve seen aligning the entity home with third-party corroboration produce measurable changes in bottom-of-funnel AI citation behavior within weeks, and it remains the highest ROI intervention I know.

Cross the corroboration threshold for the critical claims

I ask every client to identify the claims that matter most:

  • Who you are.
  • What you do.
  • Why you’re credible. 

Then, I work with them to ensure each claim is corroborated by at least 2-3 independent, high-authority sources. Not just mentioned, but confirmed with conviction. 

This is what flips AI from “sometimes includes” to “reliably includes,” and I’ve seen it happen often enough to know the threshold is real.

Dig deeper: SEO in the age of AI: Becoming the trusted answer

Build across all three graphs simultaneously

Knowledge graph presence (structured data, entity recognition), document graph presence (indexed, well-annotated content on authoritative sites), and concept graph presence (consistent narrative across the corpus AI trains on) all need attention. 

The Authoritas study showed exactly what happens when a brand exists in only one – the AI treats it accordingly.

Work the pipeline from Gate 1, not Gate 9

Most SEO and GEO advice operates at the display stage, optimizing what AI shows. But if your content is losing confidence at discovery, selection, rendering, or annotation, it will never reach display consistently enough to matter. 

I’ve watched brands spend months on display-stage optimization that produced nothing because the real bottleneck was three stages earlier, and I always start my diagnostic at the beginning of the pipeline, not the end.

Maintain it because the gap is widening

The WCS data across 143 tracked experts shows that AI citability concentration increased 293% in under two months. The experts who maintain their digital footprint are pulling away from everyone else at an accelerating rate. 

Starting now still means starting early, but waiting means competing against entities whose advantage compounds every cycle. This isn’t a one-time project. It’s an ongoing discipline, and the returns compound with every iteration.

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Fishkin proved the problem exists. The solution has been in production for a decade.

Fishkin’s research is a gift to the industry. He killed the myth of AI ranking position with data, he validated that visibility percentage, while imperfect, correlates with something real, and he raised the right questions about methodology that the AI tracking vendors should have been answering all along.

But tracking AI visibility without understanding why visibility varies is like tracking a stock price without understanding the business. The price is a signal, and the business is the thing.

AI recommendations are inconsistent when AI systems lack confidence in a brand. They become consistent when that confidence is built deliberately, through:

  • The entity home.
  • Corroborated claims that cross the corroboration threshold.
  • Multi-graph presence.
  • Every stage of the pipeline that processes your content before AI ever generates a response.

This isn’t speculation, and the evidence comes from every direction.

The process behind this approach has been under development since 2015 and is formalized in a peer-review-track academic paper. Several related patent applications have been filed in France, covering entity data structuring, prompt assembly, multi-platform coherence measurement, algorithmic barrier construction, and cascading confidence optimization.

The dataset supporting the work spans 25 billion data points across 73 million brand profiles. In tracked populations, shifts in AI citability have been observed — including cases where the top 10 experts increased their share from 31% to 60% in under two months while the overall field expanded. Independent research from Authoritas reports findings that align with this mechanism.

Fishkin proved the problem exists. My focus over the past decade has been on implementing and refining practical responses to it.

This is the first article in a series. The second piece, “What the AI expert rankings actually tell us: 8 archetypes of AI visibility,” examines how the pipeline’s effects manifest across 57 tracked experts. The third, “The ten gates between your content and an AI recommendation,” opens the DSCRI-ARGDW pipeline itself.

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Google Ads adds beta data source integrations to conversion settings

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Google Ads is rolling out a beta feature that lets advertisers connect external data sources directly inside conversion action settings, tightening the link between first-party data and campaign measurement.

How it works. A new section in conversion action details — labeled “Get deeper insights about your customers’ behavior to improve measurement” — prompts advertisers to connect external databases to their Google tag.

  • Supported integrations include platforms like BigQuery and MySQL
  • The goal is to enrich conversion metrics and improve performance signals
  • The feature appears in a highlighted prompt within data attribution settings
  • Rollout is gradual and currently marked as Beta

Why we care. Direct integrations could reduce friction in syncing offline or backend data with ad measurement. This beta from Google Ads makes it easier to connect first-party data directly to conversion tracking, which can improve measurement accuracy and campaign optimization.

By integrating sources like BigQuery or MySQL, brands can feed richer customer data into their signals, helping offset data loss from privacy changes. In practical terms, better data in means smarter bidding, clearer attribution, and potentially stronger ROI.

Between the lines. Embedding data connections inside conversion settings — rather than requiring separate pipelines — makes advanced measurement more accessible to everyday advertisers, not just enterprise teams.

Zoom out. As ad platforms compete on measurement accuracy, native data integrations are becoming a key differentiator, especially for brands investing heavily in proprietary customer data.

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How to create a persona GPT for SEO audience research

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How to create a persona GPT for SEO audience research

In a perfect world, you could call up a top customer to pick their brain about a piece of content. But in reality, it can be extremely difficult and time-consuming to conduct audience interviews every time you need to create a new topic or refresh an old piece. 

A few years ago, content marketing was simpler – keyword intent and quality content was enough to rank at the top of Google’s SERP to get clicks. But in the new era of AI, expectations are different.

Audience research has become critical. However, some companies may not have the resources to perform it.

One way to better understand your target audience is to create a custom GPT in ChatGPT, configured with your persona research. These aren’t replacements for audience research or interviews, but they can help you quickly identify what might be missing or wrong in your content. 

Below, I’ll explain how GPTs work so you can use them for audience research.

Perform audience research

Now that the SEO landscape is evolving, audience research is one of your strongest tools to understand the “why” behind search intent. 

Here are several easy-to-use methods and tools to get you started on research. 

  • SparkToro: Search by website, interest, or specific URL to segment different audience types. Research can be in-depth or give an overview of your audience. 
  • Review mining: Create automations through various tools and scrape reviews of your company or competitors to see what users are saying, and then analyze them. What does your target customer like? Why did they like it? What didn’t they like? Why?
  • Listen to calls/review leads: Listen to sales team interactions with customers to hear questions in real time and what led up to a call with a particular client.

Dig deeper: How to do audience research for SEO

Create a customer persona

After completing your research, create a persona – a representation of your target audience. Figma and FigJam are strong tools for building them.

Your persona should include: 

  • Name, bio, and trait slider.
  • Interests, influences, goals, pain points.
  • User stories.
  • The emotional journey during and after.
  • Content focus, trigger words, and calls to action (CTAs).
  • Full customer journey steps.
  • Reviews that support data.

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Create a custom GPT of your persona

Now that you have all your research and your persona, it’s time to make a GPT. 

First, log in to ChatGPT, then go to Explore GPTs in the sidebar. 

In the upper right corner, click on Create.

ChatGPT - Create

Once there, prompt ChatGPT with your audience research data and persona information. You can paste in screenshots of your data to make it easier. 

ChatGPT - Hank persona

Once all your data is in and a GPT is created, you can start talking to it. Under the Configure tab, you can use conversation starters to ask it about changes, updates, and copy.

ChatGPT conversation starters

These GPTs, like all AI models, aren’t 100% accurate. They don’t replace a real audience survey or interview, but they can help you quickly identify issues with a piece of content and how it might not connect with your audience. 

Here’s an example of an optimized page. GPT “Hank” helped make sure the section above the fold did what was intended. 

GPT Hank 1
GPT Hank 2
GPT Hank 3

Hank has said what’s working, what isn’t working, and where to improve.

But should you take his advice 100% of the time? Of course not. 

But the GPT helps quickly identify issues you may have missed. That’s where the real benefit of using a GPT comes in. 

Dig deeper: 7 custom GPT ideas to automate SEO workflows

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Ensure data from your GPT is accurate

Nothing analyzed or generated by AI is conclusive evidence. If you’re unsure your GPT is giving you accurate information, double-check by prompting it to provide evidence from the sources you gave it. 

GPT Hank - data accuracy

The GPT can correct itself if the information sounds off. When it does, again ask for evidence from the persona information you provided to double-check the new information. 

Update your persona-based GPT

You can always add more information to your GPT to make it more robust. 

To do this, go back to Explore GPTs in ChatGPT. 

Instead of Create, go to My GPTs in the top right-hand corner. 

Click on your persona. 

GPT Hank Haul

Click on Configure to update, add, or delete your current information.

GPT Hank Haul configuration

Remember that a persona is never a one-and-done situation. The more you learn about your audience and the more information you give a GPT, the better, to keep it up to date. 

Leverage persona GPTs for SEO content

Personas aren’t absolute, and AI can hallucinate. 

But both tools can still help you optimize content. 

Once you’re comfortable creating personas, you can build them for your general audience, specific segments, and individual campaigns.

SEO and marketing are always changing, and you can’t just set it and forget it. As you gain audience insights or if audience intent shifts, update information or delete anything no longer relevant in your GPT. 

When leveraged correctly, these tools can work with SEO to drive traffic and gain more conversions.

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Google Ads tool is automatically re-enabling paused keywords

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Why Google Ads auctions now run on intent, not keywords

Some advertisers are reporting that a Google Ads system tool designed for low-activity bulk changes is automatically enabling paused keywords — a behavior many account managers say they haven’t seen before.

What advertisers are seeing. Activity logs show entries tied to Google’s “Low activity system bulk changes” tool that include actions enabling previously paused keywords. The log entries appear as automated bulk updates, with a visible “Undo” option.

Historically, the tool has been associated mainly with pausing inactive elements, not reactivating them.

What we don’t know. Google hasn’t publicly documented the behavior or clarified whether this is an intentional feature, a limited experiment, or a bug.

It’s also unclear what triggers the reactivation or how broadly the behavior is rolling out.

Why we care. Unexpected keyword reactivation can quietly alter campaign delivery, affecting budgets, pacing, and performance — especially in tightly controlled accounts where paused keywords are intentional.

For agencies and in-house teams, the change raises new concerns about automation overriding manual controls.

What advertisers should do now. Account managers may want to review change histories regularly, watch for unexpected keyword activations, and use undo functions quickly if unintended changes appear.

Until Google provides clarification, closer monitoring may be necessary for accounts relying heavily on paused keyword structures.

First seen. The issue was first flagged by Performance Marketing Consultant Francesco Cifardi on LinkedIn.

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Google pushes AI Max tool with in-app ads

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Google vs. AI systems visitors

Google is now promoting its own AI features inside Google Ads — a rare move that inserts marketing directly into advertisers’ workflow.

What’s happening. Users are seeing promotional messages for AI Max for Search campaigns when they open campaign settings panels.

  • The notifications appear during routine account audits and updates.
  • It essentially serves as an internal advertisement for Google’s own tooling.

Why we care. The in-platform placement signals Google is pushing to accelerate AI adoption among advertisers, moving from optional rollouts to active promotion. While Google often introduces AI-driven features, promoting them directly within existing workflows marks a more aggressive adoption strategy.

What to watch. Whether this promotional approach expands to other Google Ads features — and how advertisers respond to marketing within their management interface.

First seen. Julie Bacchini, president and founder of Neptune Moon, spotted the notification and shared it on LinkedIn. She wrote: “Nothing like Google Ads essentially running an ad for AI Max in the settings area of a campaign.”

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Bing Webmaster Tools officially adds AI Performance report

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Microsoft today launched AI Performance in Bing Webmaster Tools in beta. AI Performance lets you see where, and how often, your content is cited in AI-generated answers across Microsoft Copilot, Bing’s AI summaries, and select partner integrations, the company said.

  • AI Performance in Bing Webmaster Tools shows which URLs are cited, which queries trigger those citations, and how citation activity changes over time.
  • Search Engine Land first reported on Jan. 27 that Microsoft was testing the AI Performance report.

What’s new. AI Performance is a new, dedicated dashboard inside Bing Webmaster Tools. It tracks citation visibility across supported AI surfaces. Instead of measuring clicks or rankings, it shows whether your content is used to ground AI-generated answers.

  • Microsoft framed the launch as an early step toward Generative Engine Optimization (GEO) tooling, designed to help publishers understand how their content shows up in AI-driven discovery.

What it looks like. Microsoft shared this image of AI Performance in Bing Webmaster Tools:

What the dashboard shows. The AI Performance dashboard introduces metrics focused specifically on AI citations:

  • Total citations: How many times a site is cited as a source in AI-generated answers during a selected period.
  • Average cited pages: The daily average number of unique URLs from a site referenced across AI experiences.
  • Grounding queries: Sample query phrases AI systems used to retrieve and cite publisher content.
  • Page-level citation activity: Citation counts by URL, highlighting which pages are referenced most often.
  • Visibility trends over time: A timeline view showing how citation activity rises or falls across AI experiences.

These metrics only reflect citation frequency. They don’t indicate ranking, prominence, or how a page contributed to a specific AI answer.

Why we care. It’s good to know where and how your content gets cited, but Bing Webmaster Tools still won’t reveal how those citations translate into clicks, traffic, or any real business outcome. Without click data, publishers still can’t tell if AI visibility delivers value.

How to use it. Microsoft said publishers can use the data to:

  • Confirm which pages are already cited in AI answers.
  • Identify topics that consistently appear across AI-generated responses.
  • Improve clarity, structure, and completeness on indexed pages that are cited less often.

The guidance mirrors familiar best practices: clear headings, evidence-backed claims, current information, and consistent entity representation across formats.

What’s next. Microsoft said it plans to “improve inclusion, attribution, and visibility across both search results and AI experiences,” and continue to “evolve these capabilities.”

Microsoft’s announcement. Introducing AI Performance in Bing Webmaster Tools Public Preview 

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How to make automation work for lead gen PPC

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B2B advertising faces a distinct challenge: most automation tools weren’t built for lead generation.

Ecommerce campaigns benefit from hundreds of conversions that fuel machine learning. B2B marketers don’t have that luxury. They deal with lower conversion volume, longer sales cycles, and no clear cart value to guide optimization.

The good news? Automation can still work.

Melissa Mackey, Head of Paid Search at Compound Growth Marketing, says the right strategy and signals can turn automation into a powerful driver of B2B leads. Below is a summary of the key insights and recommendations she shared at SMX Next.

The fundamental challenge: Why automation struggles with lead gen

Automation systems are built for ecommerce success, which creates three core obstacles for B2B marketers:

  • Customer journey length: Automation performs best with short journeys. A user visits, buys, and checks out within minutes. B2B journeys can last 18 to 24 months. Offline conversions only look back 90 days, leaving a large gap between early engagement and closed revenue.
  • Conversion volume requirements: Google’s automation works best with about 30 leads per campaign per month. Google says it can function with less, but performance is often inconsistent below that level. Ecommerce campaigns easily hit hundreds of monthly conversions. B2B lead gen rarely does.
  • The cart value problem: In ecommerce, value is instant and obvious. A $10 purchase tells the system something very different than a $100 purchase. Lead generation has no cart. True value often isn’t clear until prospects move through multiple funnel stages — sometimes months later.

The solution: Sending the right signals

Despite these challenges, proven strategies can make automation work for B2B lead generation.

Offline conversions: Your number one priority

Connecting your CRM to Google Ads or Microsoft Ads is essential for making automation work in lead generation. This isn’t optional. It’s the foundation. If you haven’t done this yet, stop and fix it first.

In Google Ads’ Data Manager, you’ll find hundreds of CRM integration options. The most common B2B setups include:

  • HubSpot and Salesforce: Both offer native, seamless integrations with Google Ads. Setup is simple. Once connected, customer stages and CRM data flow directly into the platform.
  • Other CRMs: If you don’t use HubSpot or Salesforce, you can build a custom data table with only the fields you want to share. Use connectors like Snowflake to send that data to Google Ads while protecting user privacy and still supplying strong automation signals.
  • Third-party integrations: If your CRM doesn’t integrate directly, tools like Zapier can connect almost anything to Google Ads. There’s a cost, but the performance gains typically pay for it many times over.

Embrace micro conversions with strategic values

Micro conversions signal intent. They show a “hand raiser” — someone engaged on your site who isn’t an MQL yet but clearly interested.

The key is assigning relative value to these actions, even when you don’t know their exact revenue impact. Use a simple hierarchy to train automation what matters most:

  • Video views (value: 1): Shows curiosity, but qualification is unclear.
  • Ungated asset downloads (value: 10): Indicates stronger engagement and added effort.
  • Form fills (value: 100): Reflects meaningful commitment and willingness to share personal information.
  • Marketing qualified leads (value: 1,000): The highest-value signal and top optimization priority.

This value structure tells automation that one MQL matters more than 999 video views. Without these distinctions, campaigns chase impressive conversion rates driven by low-value actions — while real leads slip through the cracks.

Making Performance Max work for lead generation

You might dismiss Performance Max (PMax) for lead generation — and for good reason. Run it on a basic maximize conversions strategy, and it usually produces junk leads and wastes budget.

But PMax can deliver exceptional results when you combine conversion values and offline conversion data with a Target ROAS bid strategy.

One real client example shows what’s possible. They tracked three offline conversion actions — leads, opportunities, and customers — and valued customers at 50 times a lead. The results were dramatic:

  • Leads increased 150%
  • Opportunities increased 350%
  • Closed deals increased 200%

Closed deals became the campaign’s top-performing metric because they reflected real, paying customers. The key difference? Using conversion values with a Target ROAS strategy instead of basic maximize conversions.

Campaign-specific goals: An underutilized feature

Campaign-specific goals let you optimize campaigns for different conversion actions, giving you far more control and flexibility.

You can set conversion goals at the account level or make them campaign-specific. With campaign-specific goals, you can:

  • Run a mid-funnel campaign optimized only for lead form submissions using informational keywords.
  • Build audiences from those form fills to capture engaged prospects.
  • Launch a separate campaign optimized for qualified leads, targeting that warm audience with higher-value offers like demos or trials.

This approach avoids asking someone to “marry you on the first date.” It also keeps campaigns from competing against themselves by trying to optimize for conflicting goals.

Portfolio bidding: Reaching the data threshold faster

Portfolio bidding groups similar campaigns so you can reach the critical 30-conversions-per-month threshold faster.

For example, four separate campaigns might generate 12, 11, 0, and 15 conversions. On their own, none qualify. Grouped into a single portfolio, they total 38 conversions — giving automation far more data to optimize against.

You may still need separate campaigns for valid reasons — regional reporting, distinct budgets, or operational constraints. Portfolio bidding lets you keep that structure while still feeding the system enough volume to perform.

Bonus benefit: Portfolio bidding lets you set maximum CPCs. This prevents runaway bids when automation aggressively targets high-propensity users. This level of control is otherwise only available through tools like SA360.

First-party audiences: Powerful targeting signals

First-party audiences send strong signals about who you want to reach, which is critical for AI-powered campaigns.

If HubSpot or Salesforce is connected to Google Ads, you can import audiences and use them strategically:

  • Customer lists: Use them as exclusions to avoid paying for existing customers, or as lookalikes in Demand Gen campaigns.
  • Contact lists: Use them for observation to signal ideal audience traits, or for targeting to retarget engaged users.

Audiences make it much easier to trust broad match keywords and AI-driven campaign types like PMax or AI Max — approaches that often feel too loose for B2B without strong audience signals in place.

Leveraging AI for B2B lead generation

AI tools can significantly improve B2B advertising efficiency when you use them with intent. The key is remembering that most AI is trained on consumer behavior, not B2B buying patterns.

The essential B2B prompt addition

Always tell the AI you’re selling to other businesses. Start prompts with clear context, like: “You’re a SaaS company that sells to other businesses.” That single line shifts the AI’s lens away from consumer assumptions and toward B2B realities.

Client onboarding and profile creation

Use AI to build detailed client profiles by feeding it clear inputs, including:

  • What you sell and your core value.
  • Your unique selling propositions.
  • Target personas.
  • Ideal customer profiles.

Create a master template or a custom GPT for each client. This foundation sharpens every downstream AI task and dramatically improves accuracy and relevance.

Competitor research in minutes, not hours

Competitive analysis that once took 20–30 hours can now be done in 10–15 minutes. Ask AI to analyze your competitors and break down:

  • Current offers
  • Positioning and messaging
  • Value propositions
  • Customer sentiment
  • Social proof
  • Pricing strategies

AI delivers clean, well-structured tables you can screenshot for client decks or drop straight into Google Sheets for sorting and filtering. Use this insight to spot gaps, uncover opportunities, and identify clear strategic advantages.

Competitor keyword analysis

Use tools like Semrush or SpyFu to pull competitor keyword lists, then let AI do the heavy lifting. Create a spreadsheet with columns for each competitor’s keywords alongside your client’s keywords. Then ask the AI to:

  • Identify keywords competitors rank for that you don’t to uncover gaps to fill.
  • Identify keywords you own that competitors don’t to surface unique advantages.
  • Group keywords by theme to reveal patterns and inform campaign structure.

What once took hours of pivot tables, filtering, and manual cleanup now takes AI about five minutes.

Automating routine tasks

  • Negative keyword review: Create an AI artifact that learns your filtering rules and decision logic. Feed it search query reports, and it returns clear add-or-ignore recommendations. You spend time reviewing decisions instead of doing first-pass analysis, which makes SQR reviews faster and easier to run more often.
  • Ad copy generation: Tools like RSA generators can produce headlines and descriptions from sample keywords and destination URLs. Pair them with your custom client GPT for even stronger starting points. Always review AI-generated copy, but refining solid drafts is far faster than writing from scratch.

Experiments: testing what works

The Experiments feature is widely underused. Put it to work by testing:

  • Different bid strategies, including portfolio vs. standard
  • Match types
  • Landing pages
  • Campaign structures

Google Ads automatically reports performance, so there’s no manual math. It even includes insight summaries that tell you what to do next — apply the changes, end the experiment, or run a follow-up test.

Solutions: Pre-built scripts made easy

Solutions are prebuilt Google Ads scripts that automate common tasks, including:

  • Reporting and dashboards
  • Anomaly detection
  • Link checking
  • Flexible budgeting
  • Negative keyword list creation

Instead of hunting down scripts and pasting code, you answer a few setup questions and the solution runs automatically. Use caution with complex enterprise accounts, but for simpler structures, these tools can save a significant amount of time.

Key takeaways

Automation wasn’t built for lead generation, but with the right strategy, you can still make it work for B2B.

  • Send the right signals: Offline conversions with assigned values aren’t optional. First-party audiences add critical targeting context. Together, these signals make AI-driven campaigns work for B2B.
  • AI is your friend: Use AI to automate repetitive work — not to replace people. Take 50 search query reports off your team’s plate so they can focus on strategy instead of tedious analysis.
  • Leverage platform tools: Experiments, Solutions, campaign-specific goals, and portfolio bidding are powerful features many advertisers ignore. Use what’s already built into your ad platforms to get more out of every campaign.

Watch: It’s time to embrace automation for B2B lead gen 

Read more at Read More

Web Design and Development San Diego

Why governance maturity is a competitive advantage for SEO

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How SEO governance shifts teams from reaction to prevention

Let me guess: you just spent three months building a perfectly optimized product taxonomy, complete with schema markup, internal linking, and killer metadata. 

Then, the product team decided to launch a site redesign without telling you. Now half your URLs are broken, the new templates strip out your structured data, and your boss is asking why organic traffic dropped 40%.

Sound familiar?

Here’s the thing: this isn’t an SEO failure, but a governance failure. It’s costing you nights and weekends trying to fix problems that should never have happened in the first place.

This article covers why weak governance keeps breaking SEO, how AI has raised the stakes, and how a visibility governance maturity model helps SEO teams move from firefighting to prevention.

Governance isn’t bureaucracy – it’s your insurance policy

I know what you’re thinking. “Great, another framework that means more meetings and approval forms.” But hear me out.

The Visibility Governance Maturity Model (VGMM) isn’t about creating red tape. It’s about establishing clear ownership, documented processes, and decision rights that prevent your work from being accidentally destroyed by teams who don’t understand SEO.

Think of it this way: VGMM is the difference between being the person who gets blamed when organic traffic tanks versus being the person who can point to documentation showing exactly where the process broke down – and who approved skipping the SEO review.

This maturity model:

  • Protects your work from being undone by releases you weren’t consulted on.
  • Documents your standards so you’re not explaining canonical tags for the 47th time.
  • Establishes clear ownership so you’re not expected to fix everything across six different teams.
  • Gets you a seat at the table when decisions affecting SEO are being made.
  • Makes your expertise visible to leadership in ways they understand.

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The real problem: AI just made everything harder

Remember when SEO was mostly about your website and Google? Those were simpler times.

Now you’re trying to optimize for:

  • AI Overviews that rewrite your content.
  • ChatGPT citations that may or may not link back.
  • Perplexity summaries that pull from competitors.
  • Voice assistants that only cite one source.
  • Knowledge panels that conflict with your site.

And you’re still dealing with:

  • Content teams who write AI-generated fluff.
  • Developers who don’t understand crawl budget.
  • Product managers who launch features that break structured data.
  • Marketing directors who want “just one small change” that tanks rankings.

Without governance, you’re the only person who understands how all these pieces fit together. 

When something breaks, everyone expects you to fix it – usually yesterday. When traffic is up, it’s because marketing ran a great campaign. When it’s down, it’s your fault.

You become the hero the organization depends on, which sounds great until you realize you can never take a real vacation, and you’re working 60-hour weeks.

Dig deeper: Why most SEO failures are organizational, not technical

What VGMM actually measures – in terms you care about

VGMM doesn’t care about your keyword rankings or whether you have perfect schema markup. It evaluates whether your organization is set up to sustain SEO performance without burning you out. Below are the five maturity levels that translate to your daily reality:

Level 1: Unmanaged (your current nightmare)

  • Nobody knows who’s responsible for SEO decisions.
  • Changes happen without SEO review.
  • You discover problems after they’ve tanked traffic.
  • You’re constantly firefighting.
  • Documentation doesn’t exist or is ignored.

Level 2: Aware (slightly better)

  • Leadership admits SEO matters.
  • Some standards exist but aren’t enforced.
  • You have allies but no authority.
  • Improvements happen but get reversed next quarter.
  • You’re still the only one who really gets it.

Level 3: Defined (getting somewhere)

  • SEO ownership is documented.
  • Standards exist, and some teams follow them.
  • You’re consulted before major changes.
  • QA checkpoints include SEO review.
  • You’re working normal hours most weeks.

Level 4: Integrated (the dream)

  • SEO is built into release workflows.
  • Automated checks catch problems before they ship.
  • Cross-functional teams share accountability.
  • You can actually take a vacation without a disaster.
  • Your expertise is respected and resourced.

Level 5: Sustained (unicorn territory)

  • SEO survives leadership changes.
  • Governance adapts to new AI surfaces automatically.
  • Problems are caught before they impact traffic.
  • You’re doing strategic work, not firefighting.
  • The organization values prevention over reaction.

Most organizations sit at Level 1 or 2. That’s not your fault – it’s a structural problem that VGMM helps diagnose and fix.

Dig deeper: SEO’s future isn’t content. It’s governance

How VGMM works: The less boring explanation

VGMM coordinates multiple domain-specific maturity models. Think of it as a health checkup that looks at all your vital signs, not just one metric.

It evaluates maturity across domains like:

  • SEO governance: Your core competency.
  • Content governance: Are writers following standards?
  • Performance governance: Is the site actually fast?
  • Accessibility governance: Is the site inclusive?
  • Workflow governance: Do processes exist and work?

Each domain gets scored independently, then VGMM looks at how they work together. Because excellent SEO maturity doesn’t matter if the performance team deploys code that breaks the site every Tuesday or if the content team publishes AI-generated nonsense that tanks your E-E-A-T signals.

VGMM produces a 0–100% score based on:

  • Domain scores: How mature is each area?
  • Weighting: Which domains matter most for your business?
  • Dependencies: Are weaknesses in one area breaking strengths in another?
  • Coherence: Do decision rights and accountability actually align?

The final score isn’t about effort – it’s about whether governance actually works.

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What this means for your daily life

Before VGMM-style governance:

  • Product launches a redesign → You find out when traffic drops.
  • Content team uses AI → You discover thin content in Search Console.
  • Dev changes URL structure → You spend a week fixing redirects.
  • Marketing wants “quick changes” → You explain why it’s not quick (again).
  • Site goes down → Everyone asks why you didn’t catch it.

After governance maturity improves:

  • Product can’t launch without SEO sign-off.
  • Content AI usage has review checkpoints.
  • URL changes require documented SEO approval.
  • Marketing requests go through defined workflows.
  • Site monitoring includes automated SEO health checks.

You move from reactive firefighting to proactive prevention. Your weekends become yours again.

The supporting models: What they actually check

VGMM doesn’t score you on technical SEO execution. It checks whether the organization has processes in place to prevent SEO disasters.

SEO Governance Maturity Model (SEOGMM) asks:

  • Are there documented SEO standards?
  • Who can override them, and how?
  • Do templates enforce SEO requirements?
  • Are there QA checkpoints before releases?
  • Can SEO block launches that will cause problems?

Content Governance Maturity Model (CGMM) asks:

  • Are content quality standards documented?
  • Is AI-generated content reviewed?
  • Are writers trained on SEO basics?
  • Is there a process for updating outdated content?

Website Performance Maturity Model (WPMM) asks:

  • Are Core Web Vitals monitored?
  • Can releases be rolled back if they break performance?
  • Is there a performance budget?
  • Are third-party scripts governed?

You get the idea. Each domain has its own checklist, and VGMM shows leadership where gaps create risk.

Dig deeper: SEO execution: Understanding goals, strategy, and planning

How to pitch this to your boss

You don’t need to explain VGMM theory. You need to connect it to problems leadership already knows exist.

  • Frame it as risk reduction: “We’ve had three major traffic drops this year from changes that SEO didn’t review. VGMM helps us identify where our process breaks down so we can prevent this.”
  • Frame it as efficiency: “I’m spending 60% of my time firefighting problems that could have been prevented. VGMM establishes processes so I can focus on growth opportunities instead.”
  • Frame it as a competitive advantage: “Our competitors are getting cited in AI Overviews, and we’re not. VGMM evaluates whether we have the governance structure to compete in AI-mediated search.”
  • Frame it as scalability: “Right now, our SEO capability depends entirely on me. If I get hit by a bus tomorrow, nobody knows how to maintain what we’ve built. VGMM establishes documentation and processes that make our SEO sustainable.”
  • The ask: “I’d like to conduct a VGMM assessment to identify where our processes need strengthening.”

What success actually looks like

Organizations with higher VGMM maturity experience measurably better outcomes:

  • Fewer unexplained traffic drops because changes are reviewed.
  • More stable AI citations because content quality is governed.
  • Less rework after launches because SEO is built into workflows.
  • Clearer accountability because ownership is documented.
  • Better resource allocation because gaps are visible to leadership.

But the real win for you personally: 

  • You stop being the hero who saves the day and become the strategist who prevents disasters. 
  • Your expertise is recognized and properly resourced. 
  • You can take actual vacations. 
  • You work normal hours most of the time.

Your job becomes about building and improving, not constantly fixing.

Getting started: Practical next steps

Step 1: Self-assessment

Look at the five maturity levels. Where is your organization honestly sitting? If you’re at Level 1 or 2, you have evidence for why governance matters.

Step 2: Document current-state pain

Make a list of the last six months of SEO incidents:

  • Changes that weren’t reviewed.
  • Traffic drops from preventable problems.
  • Time spent fixing avoidable issues.
  • Requests that had to be explained multiple times.

This becomes your business case.

Step 3: Start with one domain

You don’t need to implement full VGMM immediately. Start with SEOGMM:

  • Document your standards.
  • Create a review checklist.
  • Establish who can approve exceptions.
  • Get stakeholder sign-off on the process.

Step 4: Show results 

Track prevented problems. When you catch an issue before it ships, document it. When a process prevents a regression, quantify the impact. Build your case for expanding governance.

Step 5: Expand systematically

Once SEOGMM is working, expand to related domains (content, performance, accessibility). Show how integrated governance catches problems that individual domain checks miss.

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Why governance determines whether SEO survives

Governance isn’t about making your job harder. It’s about making your organization work better so your job becomes sustainable.

VGMM gives you a framework for diagnosing why SEO keeps getting undermined by other teams and a roadmap for fixing it. It translates your expertise into language that leadership understands. It protects your work from accidental destruction.

Most importantly, it moves you from being the person who’s always fixing emergencies to being the person who builds systems that prevent them.

You didn’t become an SEO professional to spend your career firefighting. VGMM helps you get back to doing the work that actually matters – the strategic, creative, growth-focused work that attracted you to SEO in the first place.

If you’re tired of watching your best work get undone by teams who don’t understand SEO, if you’re exhausted from being the only person who knows how everything works, if you want your expertise to be recognized and protected – start the VGMM conversation with your leadership.

The framework exists. What’s missing is someone in your organization saying, “We need to govern visibility like we govern everything else that matters.”

That someone is you.

Dig deeper: Why 2026 is the year the SEO silo breaks and cross-channel execution starts

Read more at Read More

Tips and tricks to write SEO-friendly blog posts in the AI era

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It is no secret that publishing SEO-friendly blog posts is one of the easiest and most effective ways to drive organic traffic and improve SERP rankings. However, in the era of artificial intelligence, blog posts matter more than ever. They help establish brand authority by consistently delivering fresh, valuable content that can be cited in AI-generated answers.

In this guide, we will share a practical, detailed approach to writing SEO-friendly blog content that not only ranks on Google SERPs but is also surfaced by AI models.

Table of contents

Key takeaways

  • SEO friendly blog post now means writing with search intent, ensuring content is clear and quotable for AI systems
  • Key factors for SEO friendly blog posts include trustworthiness, machine-readability, answer-first structure, and topical authority
  • Conduct thorough keyword research and find readers’ questions to match search intent effectively
  • Use clear headings, improve readability, include inclusive language, and add relevant media to engage readers
  • Write compelling meta titles and descriptions, link to existing content, and focus on building authority to enhance visibility

What does an SEO-friendly blog post mean in the AI era?

The way people search for information has changed, and with it, the meaning of an SEO-friendly blog post. Before the rise of generative AI, writing an SEO-friendly blog post mostly meant this:

‘Writing content with the intention of ranking highly in search engine results pages (SERPs). The content is optimized for specific target keywords, easy to read, and provides value to the reader.’

That definition is not wrong. But it is no longer complete.

In the AI era, an SEO-friendly blog post is written with search intent first, answering a user’s question clearly and efficiently. It is not just about placing keywords in the right spots. It is about creating an information-dense piece with accurate, well-structured, and quotable sentences that AI systems can confidently extract and surface as direct answers.

The new definition clearly shows that strong SEO foundations still matter, and they matter more than ever. What has changed is how content is evaluated and discovered. Search engines and AI models now look beyond clicks and rankings to understand whether your content is trustworthy, helpful, and easy to interpret.

Here are some key factors that play a key role in determining whether a blog post is truly SEO-friendly:

  • Trustworthiness (E-E-A-T): Demonstrating real-world experience, expertise, and credibility helps your content stand out from low-value AI-generated rehashes
  • Machine-readability: Clear structure, clean HTML, and technical signals such as schema markup help search engines and AI systems understand what your content is about
  • Answer-first structure: Placing concise, direct answers at the beginning of sections makes it easier for AI models to extract and reference your content
  • Topical authority: Publishing interconnected, in-depth content around a subject is far more effective than creating isolated blog posts

9 tips to write SEO-friendly blogs for LLM and SERP visibility

Now we get to the core of this guide. Below are some foundational tips to help you plan and write SEO-friendly blog posts that are genuinely helpful, easy to understand, and focused on solving real reader problems. When done right, these practices not only improve search visibility but also shape how your brand is perceived by both users and AI systems.

1. Conduct thorough keyword research

Before you start writing a single word, start with solid keyword research. This step helps you understand how people search for a topic, which terms carry demand, and how competitive those searches are. It also ensures your content aligns with real user intent instead of assumptions.

You can use tools like Google Keyword Planner, Ahrefs, or Semrush for this. Personally, I prefer using Semrush’s Keyword Magic Tool because it quickly surfaces thousands of relevant keyword ideas around a single topic.

keyword magic tool by semrush for keyword researcg
Keyword Magic Tool by Semrush for the relevant keyword list

Here’s how I usually approach it. I enter a broad keyword related to my topic, for example, ‘SEO.’ The tool then returns an extensive list of related keywords along with important metrics. I mainly focus on three of them:

  • Search intent, to understand what the user is really looking for
  • Keyword Difficulty (KD%), to estimate how hard it is to rank
  • Search volume, to gauge demand

This combination helps me choose keywords that are realistic to rank for and meaningful for readers.

If you use Yoast SEO, this process becomes even easier. Semrush is integrated into Yoast SEO (both free and Premium), giving you keyword suggestions directly in Yoast SEO. With a single click, you can access relevant keyword data while writing, making it easier to create focused, useful content from the start.

Looking for keyphrase suggestions? When you’ve set a focus keyword in Yoast SEO, you can click on ‘Get related keyphrases’ and our Semrush integration will help you find high-performing keyphrases!

Also read: How to use the Semrush related keyphrases feature in Yoast SEO for WordPress

2. Finding readers’ questions

Keyword research tells you what people search for. Questions tell you why they search.

When you actively look for the questions your audience is asking, you move closer to matching search intent. This is especially important in the AI era, where search engines and AI models prioritize clear, answer-driven content.

For example, consider these two queries:

What are the key features of good running shoes?

This shows informational intent. The searcher wants to understand what makes a running shoe good.

What are the best running shoes?

This suggests a transactional or commercial intent. The searcher is likely comparing options before making a purchase.

Both questions are valid, but they require very different content approaches.

There are two simple ways I usually find relevant questions. The first is by checking the People also ask section in Google search results. By typing in a broad keyphrase, you can see related questions that Google itself considers relevant.

people also ask section on google serps
The People also ask section showing questions related to the broad keyphrase ‘SEO’

The second method is to use the Questions filter in Semrush’s Keyword Magic Tool. This helps uncover question-based queries directly tied to your main topic.

Apart from these methods, I also like using Google’s AI Overview and AI mode as a quick research layer. When I search for my main topic, I pay close attention to AI-cited sources, as they often surface broad questions people are actively seeking. The structured points and highlighted terms usually reflect the answers and subtopics that matter most to users. If I want to go deeper, I click “Show more,” which reveals additional angles and follow-up questions I might not have considered initially.

google ai overview citing resources
AI cited sources by Google AI Overview

Finding and answering these questions helps you do lightweight online audience research and create content that feels genuinely helpful. It also increases the chances of your blog post being referenced in AI-generated answers, since LLMs are designed to surface clear responses to specific questions.

3. Structure your content with headings and subheadings

In our 2026 SEO predictions, we highlighted that editorial quality is no longer just about good writing. It has become a machine-readability requirement. Content that is clearly structured is easier to understand, reuse, and surface across both search and AI-driven experiences.

How LLMs use headings

AI models rely on headings to identify topics, questions, and answers within a page. When your content is broken into clear sections, it becomes easier for them to extract key information and include it in AI-generated summaries.

Why headings still matter for SEO

Headings help search engines understand the hierarchy of your content and the main points you are trying to rank for. They also improve scannability and usability, especially on mobile devices, and increase the chances of earning featured snippets.

Good structure has always been a core SEO principle. In the AI era, it remains one of the simplest and most effective ways to improve visibility and discoverability.

4. Focus on readability aspects

An SEO-friendly blog post should be easy to read before it can rank or get picked up by AI systems. Readability helps readers stay engaged and helps search engines and AI models better understand your content.

A few key readability aspects to focus on while writing:

  • Avoid passive voice where possible
    Active sentences are clearer and more direct. They make it easier for readers to understand who is doing what, and they reduce ambiguity for AI systems processing your content.
  • Use transition words
    Transition words like “because,” “for example,” and “however” guide readers through your content. They improve flow and make it easier to follow relationships between sentences and paragraphs.
  • Keep sentences and paragraphs short
    Long, complex sentences reduce clarity. Breaking content into shorter sentences and paragraphs improves scannability and comprehension.
  • Avoid consecutive sentences starting in the same way
    Varying sentence structure keeps your writing engaging and prevents it from sounding repetitive or robotic.
The readability analysis in the Yoast SEO for WordPress metabox
The readability analysis in the Yoast SEO for WordPress metabox

If you are a WordPress or Shopify user, Yoast SEO (and Yoast SEO for Shopify for Shopify users) can help here. Its readability analysis checks for passive voice, transition words, sentence length, and other clarity signals while you write. If you prefer drafting in Google Docs, you can use the Yoast SEO Google Docs add-on to get the same readability feedback before publishing.

Use Yoast SEO in Google Docs

Optimize as you draft for SEO, inclusivity, and readability. The Yoast SEO Google Docs add-on lets you export content ready for WordPress, no reformatting required.

Get Yoast for Google Docs add-onOnly $5 / month (ex VAT)

 

Good readability is not just about pleasing algorithms. It helps readers understand your message more quickly and makes your content easier to reuse in AI-generated responses.

5. Use inclusive language

Inclusive language helps ensure your content is respectful, clear, and welcoming to a broader audience. It avoids assumptions about gender, ability, age, or background, and focuses on people-first communication.

From an SEO and AI perspective, inclusive language also improves clarity. Content that avoids vague or biased terms is easier to interpret, digest, and trust. This directly supports brand perception, especially when your content is surfaced in AI-generated responses.

Yoast SEO supports this through its inclusive language check, which flags potentially non-inclusive terms and suggests better alternatives. This feature is available in Yoast SEO, Yoast SEO Premium, and in the Yoast SEO Google Docs add-on, making it easier to build inclusive habits directly into your writing workflow.

Inclusive language ensures your content is intentional, thoughtful, and clear, aligning closely with what modern SEO and AI systems value.

6. Add relevant media and interaction points

A well-written blog post should not feel like a long block of text. Adding the right media and interaction points helps guide readers through your content, keeps them engaged, and encourages them to take action.

Why media matters

Media elements such as images, videos, embeds, and infographics make your content easier to consume and more engaging. Blog posts that include images receive 94% more views than those without, simply because visuals break up large blocks of text and make pages easier to scan.

Video content plays an even bigger role. Embedded videos help explain complex ideas faster and can significantly improve organic visibility compared to text-only posts. Together, these elements encourage readers to stay longer on your page, which is a strong signal of content quality for search engines and AI systems alike.

Media also improves accessibility. Properly optimized images with descriptive alt text make content usable for screen readers, while original visuals, screenshots, or diagrams help reinforce credibility and expertise.

Use interaction points to guide and engage readers

Interaction does not always mean complex features. Even simple elements can significantly improve engagement when used well.

Table of contents and sidebar CTA used as interaction points in a Yoast blog post

A table of contents, for example, allows readers to jump directly to the section they care about most.

Other interaction points include clear calls to action (CTAs) that guide readers to the next step, relevant recommendations that encourage users to keep exploring your site, and social sharing buttons that make it easy to amplify your content. Interactive elements like polls, quizzes, or embedded tools further encourage participation and increase time on page.

7. Plan your content length

Content length still matters, but not in the way many people think it does.

A common question is what the ideal word count is for a blog post that performs well. A 2024 study by Backlinko found that while longer content tends to attract more backlinks, the average page ranking on Google’s first page contains around 1,500 words.

That said, this should not be treated as a fixed benchmark. The ideal length is the one that fully answers the user’s question. In an AI-driven era, publishing long content that adds little value or is padded with unnecessary fluff can do more harm than good.

If a topic genuinely requires a longer format, breaking the content into clear subheadings makes a big difference. I personally prefer structuring long articles this way because it improves readability, helps readers navigate the page more easily, and makes the content easier for search engines and AI systems to understand.

Must read: How to use headings on your site

If you use Yoast SEO or Yoast SEO Premium, the paragraph and sentence length checks can help here. These checks exist to prevent pages from being too thin to provide real value. Pages with very low word counts often lack context and struggle to demonstrate relevance or expertise. Yoast SEO flags such cases as a warning, while clearly indicating that adding more words alone does not guarantee better rankings.

Think of word count as a guideline, not a goal. Your focus should always be on clarity, completeness, and usefulness.

8. Link to existing content

Internal linking is one of the most underrated SEO practices, yet it does a lot of heavy lifting behind the scenes.

By linking to relevant content within your site, you help readers discover additional resources and help search engines understand how your content is connected. Over time, this strengthens topical authority and signals that your site consistently covers a subject in depth.

Good internal linking follows a few simple principles:

  • Link only when it adds value and feels natural in context
  • Use clear, descriptive anchor text so users and search engines know what to expect
  • Avoid linking to outdated URLs or pages that redirect, as this wastes crawl signals

Internal links also keep readers engaged longer by guiding them to related articles. This improves overall site engagement while reinforcing your expertise on a topic.

From an AI and search perspective, internal linking plays an even bigger role. Modern search systems analyze content structure, metadata hierarchies, schema markup, and internal links to assess topical depth and clarity. Well-linked content clusters make it easier for search engines and AI systems to understand what your site is about and which pages are most important.

For WordPress users, Yoast SEO Premium offers internal linking suggestions directly in the editor. This makes it easier to spot relevant linking opportunities as you write, helping you build stronger content connections without interrupting your workflow.

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!

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9. Write compelling meta titles and descriptions

Meta titles and meta descriptions help users decide whether to click on your content. While meta descriptions are not a direct ranking factor, they strongly influence click-through rates, making them an essential part of writing SEO-friendly blog posts.

A good meta title clearly communicates what the page is about. Place your main keyword near the beginning, keep it concise, and aim for roughly 55-60 characters so it doesn’t get truncated in search results.

Meta descriptions act like a short invitation. They should explain what the reader will gain from clicking and why it matters. Instead of stuffing keywords, focus on clarity and usefulness. Mention what aspects of the topic your content covers and how it helps the reader. Simple language works best.

Pro tip: Using action-oriented verbs such as “learn,” “discover,” or “read” can also encourage clicks and make your description more engaging.

If you use Yoast SEO Premium, this process becomes much easier. The AI-powered meta title and description generation feature helps you create relevant, well-structured metadata in just one click. It follows SEO best practices while producing descriptions and titles that are clear, engaging, and aligned with search intent.

Bonus tips

Once you have the fundamentals in place, a few extra refinements can go a long way. The following bonus tips help improve usability, clarity, and long-term discoverability. They are not mandatory, but when applied thoughtfully, they can make your blog posts more helpful for readers and easier to surface across search engines and AI-driven experiences.

1. Add a table of contents

A table of contents (TOC) helps readers quickly understand what your blog post covers and jump straight to the section they care about. This is especially useful for long-form content, where users often scan rather than scroll from top to bottom.

From an SEO perspective, a TOC improves structure and readability and can create jump links in search results, which may increase click-through rates. It reduces bounce rates by helping users find answers faster and improves accessibility by offering clear navigation.

By the way, did you know Yoast can help you here too? Yes, the Yoast SEO Internal linking blocks feature lets you add a TOC block to your blog post that automatically includes all the headings with just one click!

2. Add key takeaways

Key takeaways help readers quickly grasp the main points of your blog post without having to read the whole post. This is especially helpful for time-constrained users who want quick, actionable insights.

Summaries also support SEO by reinforcing topic relevance and improving content comprehension for search engines and AI systems. Well-written takeaways might increase visibility in featured snippets and “People also ask” results.

If you use Yoast SEO Premium, the Yoast AI Summarize feature can generate key takeaways for your content in just one click, making it easier to add concise summaries without extra effort.

3. Add an FAQ section

An FAQ section gives you space to answer specific questions your readers may still have after reading your post. This improves user experience by addressing concerns directly and building trust.

FAQs also help search engines better understand your content by clearly outlining common questions and answers related to your topic. While they can support rankings, their real value lies in reducing friction, improving clarity, and even supporting conversions by clearing doubts.

4. Short permalinks

A permalink is the permanent URL of your blog post. Short, descriptive permalinks are easier to read, easier to share, and more likely to be clicked.

Good permalinks clearly describe what the page is about, avoid unnecessary words, and include the main topic where relevant. They improve usability and help search engines understand page context at a glance.

5. Focus on building authority (EEAT aspect)

Building authority is critical, especially for sites that cover sensitive or high-impact topics. Demonstrating Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) helps both users and search engines trust your content.

This includes citing reliable sources, showing real-world experience, maintaining consistent quality, and clearly communicating who is behind the content. Strong E-E-A-T signals are especially important for YMYL topics, where accuracy and credibility matter most.

6. Plan content distribution

Writing a great blog post is only half the work. Distribution helps your content reach the right audience.

Sharing posts on social media, repurposing key insights into newsletters, and earning backlinks from relevant sites can drive more traffic and visibility. Distribution also increases engagement signals and helps your content gain traction faster, which supports long-term SEO performance.

Target your readers always!

In AI-driven search, retrieval beats ranking. Clarity, structure, and language alignment now decide if your content gets seen. – Carolyn Shelby

This perfectly sums up what writing SEO-friendly blog posts looks like today. Success is no longer just about rankings. It is about being clear, helpful, and easy to understand for both readers and AI systems.

Throughout this guide, we focused on the fundamentals that still matter: understanding search intent, structuring content well, improving readability, using inclusive language, and supporting your writing with media, internal links, and thoughtful metadata. These are not new tricks. They are strong SEO foundations, adapted for how search and discovery work in the AI era.

If there is one takeaway, it is this: always write for your readers first. When your content genuinely helps people, answers their questions, and respects how they search and read, it naturally becomes easier to surface across SERPs and AI-driven experiences.

Good SEO has not changed. It has simply become more human.

The post Tips and tricks to write SEO-friendly blog posts in the AI era appeared first on Yoast.

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Web Design and Development San Diego

Why PPC measurement feels broken (and why it isn’t)

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Why PPC measurement works differently in a privacy-first world

If you’ve been managing PPC accounts for any length of time, you don’t need a research report to tell you something has changed. 

You see it in the day-to-day work: 

  • GCLIDs missing from URLs.
  • Conversions arriving later than expected.
  • Reports that take longer to explain while still feeling less definitive than they used to.

When that happens, the reflex is to assume something broke – a tracking update, a platform change, or a misconfiguration buried somewhere in the stack.

But the reality is usually simpler. Many measurement setups still assume identifiers will reliably persist from click to conversion, and that assumption no longer holds consistently.

Measurement hasn’t stopped working. The conditions it depends on have been shifting for years, and what once felt like edge cases now show up often enough to feel like a systemic change.

Why this shift feels so disorienting

I’ve been close to this problem for most of my career. 

Before Google Ads had native conversion tracking, I built my own tracking pixels and URL parameters to optimize affiliate campaigns. 

Later, while working at Google, I was involved in the acquisition of Urchin as the industry moved toward standardized, comprehensive measurement.

That era set expectations that nearly everything could be tracked, joined, and attributed at the click level. Google made advertising feel measurable, controllable, and predictable. 

As the ecosystem now shifts toward more automation, less control, and less data, that contrast can be jarring.

It has been for me. Much of what I once relied on to interpret PPC data no longer applies in the same way. 

Making sense of today’s measurement environment requires rethinking those assumptions, not trying to restore the old ones. This is how I think about it now.

Dig deeper: How to evolve your PPC measurement strategy for a privacy-first future

The old world: click IDs and deterministic matching

For many years, Google Ads measurement followed a predictable pattern. 

  • A user clicked an ad. 
  • A click ID, or gclid, was appended to the URL. 
  • The site stored it in a cookie. 
  • When a conversion fired, that identifier was sent back and matched to the click.

This produced deterministic matches, supported offline conversion imports, and made attribution relatively easy to explain to stakeholders. 

As long as the identifier survived the journey, the system behaved in ways most advertisers could reason about. 

We could literally see what happened with each click and which ones led to individual conversions.

That reliability depended on a specific set of conditions.

  • Browsers needed to allow parameters through. 
  • Cookies had to persist long enough to cover the conversion window. 
  • Users had to accept tracking by default. 

Luckily, those conditions were common enough that the model worked really well.

Why that model breaks more often now

Browsers now impose tighter limits on how identifiers are stored and passed.

Apple’s Intelligent Tracking Prevention, enhanced tracking protection, private browsing modes, and consent requirements all reduce how long tracking data persists, or whether it’s stored at all.

URL parameters may be stripped before a page loads. Cookies set via JavaScript may expire quickly. Consent banners may block storage entirely.

Click IDs sometimes never reach the site, or they disappear before a conversion occurs.

This is expected behavior in modern browser environments, not an edge case, so we have to account for it.

Trying to restore deterministic click-level tracking usually means working against the constant push toward more privacy and the resulting browser behaviors.

This is another of the many evolutions of online advertising we simply have to get on board with, and I’ve found that designing systems to function with partial data beats fighting the tide.

The adjustment isn’t just technical

On my own team, GA4 is a frequent source of frustration. Not because it’s incapable, but because it’s built for a world where some data will always be missing. 

We hear the same from other advertisers: the data isn’t necessarily wrong, but it’s harder to reason about.

This is the bigger challenge. Moving from a world where nearly everything was observable to one where some things are inferred requires accepting that measurement now operates under different conditions. 

That mindset shift has been uneven across the industry because measurement lives at the periphery of where many advertisers spend most of their time, working in ad platforms.

A lot of effort goes into optimizing ad platform settings when sometimes the better use of time might’ve been fixing broken data so better decisions could be made.

Dig deeper: Advanced analytics techniques to measure PPC

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What still works: Client-side and server-side approaches

So what approaches hold up under current constraints? The answer involves both client-side and server-side measurement.

Pixels still matter, but they have limits

Client-side pixels, like the Google tag, continue to collect useful data.

They fire immediately, capture on-site actions, and provide fast feedback to ad platforms, whose automated bidding systems rely on this data.

But these pixels are constrained by the browser. Scripts can be blocked, execution can fail and consent settings can prevent storage. A portion of traffic will never be observable at the individual level.

When pixel tracking is the only measurement input, these gaps affect both reporting and optimization. Pixels haven’t stopped working. They just no longer cover every case.

Changing how pixels are delivered

Some responses to declining pixel data focus on the mechanics of how pixels are served rather than measurement logic.

Google Tag Gateway changes where tag requests are routed, sending them through a first-party, same-origin setup instead of directly to third-party domains.

This can reduce failures caused by blocked scripts and simplify deployment for teams using Google Cloud.

What it doesn’t do is define events, decide what data is collected, or correct poor tagging choices. It improves delivery reliability, not measurement logic.

This distinction matters when comparing Tag Gateway and server-side GTM.

  • Tag Gateway focuses on routing and ease of setup.
  • Server-side GTM enables event processing, enrichment, and governance. It requires more maintenance and technical oversight, but it provides more control.

The two address different problems.

Here’s the key point: better infrastructure affects how data moves, not what it means.

Event definitions, conversion logic, and consistency across systems still determine data quality.

A reliable pipeline delivers whatever it’s given, so it’d be just as good at making sure the garbage you put in also comes back out.

Offline conversion imports: Moving measurement off the browser

Offline conversion imports take a different approach, moving measurement away from the browser entirely. Conversions are recorded in backend systems and sent to Google Ads after the fact.

Because this process is server to server, it’s less affected by browser privacy restrictions. It works for longer sales cycles, delayed purchases, and conversions that happen outside the site. 

This is why Google commonly recommends running offline imports alongside pixel-based tracking. The two cover different parts of the journey. One is immediate, the other persists.

Offline imports also align with current privacy constraints. They rely on data users provide directly, such as email addresses during a transaction or signup.

The data is processed server-side and aggregated, reducing reliance on browser identifiers and short-lived cookies.

Offline imports don’t replace pixels. They reduce dependence on them.

Dig deeper: Offline conversion tracking: 7 best practices and testing strategies

How Google fills the gaps

Even with pixels and offline imports working together, some conversions can’t be directly observed.

Matching when click IDs are missing

When click IDs are unavailable, Google Ads can still match conversions using other inputs.

This often begins with deterministic matching through hashed first-party identifiers such as email addresses, when those identifiers can be associated with signed-in Google users.

This is what Enhanced Conversions help achieve.

When deterministic matching, if this then that, isn’t possible, the system relies on aggregated and validated signals rather than reconstructing individual click paths.

These can include session-level attributes and limited, privacy-safe IP information, combined with timing and contextual constraints.

This doesn’t recreate the old click-level model, but it allows conversions to be associated with prior ad interactions at an aggregate level.

One thing I’ve noticed: adding these inputs typically improves matching before it affects bidding.

Bidding systems account for conversion lag and validate new signals over time, which means imported or modeled conversions may appear in reporting before they’re fully weighted in optimization.

Matching, attribution, and bidding are related but separate processes. Improvements in one don’t immediately change the others.

Modeled conversions as a standard input

Modeled conversions are now a standard part of Google Ads and GA4 reporting.

They’re used when direct observation isn’t possible, such as when consent is denied or identifiers are unavailable.

These models are constrained by available data and validated through consistency checks and holdback experiments.

When confidence is low, modeling may be limited or not applied. Modeled data should be treated as an expected component of measurement rather than an exception.

Dig deeper: Google Ads pushes richer conversion imports

Boundaries still matter

Tools like Google Tag Gateway or Enhanced Conversions for Leads help recover measurement signal, but they don’t override user intent. 

Routing data through a first-party domain doesn’t imply consent. Ad blockers and restrictive browser settings are explicit signals. 

Overriding them may slightly increase the measured volume, but it doesn’t align with users’ expectations regarding how your organization uses their data.

Legal compliance and user intent aren’t the same thing. Measurement systems can respect both, but doing so requires deliberate choices.

Designing for partial data

Missing signals are normal. Measurement systems that assume full visibility will continue to break under current conditions.

Redundancy helps: pixels paired with hardened delivery, offline imports paired with enhanced identifiers, and multiple incomplete signals instead of a single complete one.

But here’s where things get interesting. Different systems will see different things, and this creates a tension many advertisers now face daily.

Some clients tell us their CRM data points clearly in one direction, while Google Ads automation, operating on less complete inputs, nudges campaigns another way.

In most cases, neither system is wrong. They’re answering different questions with different data, on different timelines. Operating in a world of partial observability means accounting for that tension rather than trying to eliminate it.

Dig deeper: Auditing and optimizing Google Ads in an age of limited data

Making peace with partial observability

The shift toward privacy-first measurement changes how much of the user journey can be directly observed. That changes our jobs.

The goal is no longer perfect reconstruction of every click, but building measurement systems that remain useful when signals are missing, delayed, or inferred.

Different systems will continue to operate with different views of reality, and alignment comes from understanding those differences rather than trying to eliminate them.

In this environment, durable measurement depends less on recovering lost identifiers and more on thoughtful data design, redundancy, and human judgment.

Measurement is becoming more strategic than ever.

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