Tag: AI

Semantic PPC and SEO Tactics That Still Win With AI

Why advanced semantic techniques still matter in PPC and SEO

Now that I can use AI to generate keywords and launch a paid search campaign in minutes, it is tempting to think the hardest part of PPC and SEO work has already been handled.

But I still need more than fast keyword output if I want structured, scalable performance. I need to understand how search actually works, how people phrase intent, and how noisy search term data can distort a campaign if I do not organize it properly.

That is where semantic techniques such as n-grams, Levenshtein distance, and Jaccard similarity continue to matter. I use them to interpret messy data, apply real client context, and build frameworks that AI alone cannot reliably produce.

What I learn from n-grams in PPC and SEO analysis

I think of n-grams as the “n” words that make up a keyword. In the search term “private caregiver nearby,” I can break the phrase into smaller pieces that are easier to analyze.

3 unigrams (one word): “private,” “caregiver,” and “nearby”
2 bigrams (two consecutive words): “private caregiver” and “caregiver nearby”
1 trigram (three consecutive words): “private caregiver nearby”

I use n-grams because they simplify large keyword lists without stripping away the patterns that matter.

For example, I recently restructured several campaigns that had more than 100,000 search terms. By using n-grams, I reduced those lists into much more workable sets.

~6,000 unigrams.
~23,000 bigrams.
~27,000 trigrams.

Once I have those smaller sets, I can spot patterns quickly. If every keyword containing the “free” unigram performs poorly, I can exclude “free” as a broad match negative.

On the other hand, if I see that “nearby” performs especially well, I may test more local variations, build location-specific landing pages, or adjust campaign structure around that intent.

I still have to respect the limits of this method.

I need a large volume of search terms, so this approach usually works best for accounts with bigger budgets.
As “n” gets larger, the output becomes less useful because the data expands again. At that point, I usually need more advanced methods such as Levenshtein distance or Jaccard similarity.

How I cluster keywords with n-grams

When I analyze SEO and PPC data, I often deal with huge volumes of long-tail search terms. Many appear only once and carry very little standalone data.

N-grams help me turn that chaotic long-tail data into clearer, more manageable intelligence.

That intelligence helps me reduce wasted spend, find new opportunities, and build a structure that can scale.

I start by exporting search term data. In PPC, that includes cost, impressions, clicks, conversions, and conversion value by search term.
For each n-gram, I sum cost, impressions, clicks, conversions, and conversion value.
Then I calculate CPA, ROAS, CTR, CVR, and any other metrics that matter for the account.

With a shorter and more digestible dataset, I can rank the top-spending n-grams that do not convert, which often become negatives, and the ones that do convert, which become positives.

From there, I build ad groups around recurring n-grams that consistently drive performance.

For example, I may find that emergency-related n-grams such as “24/7,” “same day,” or “urgent” deliver higher conversion rates. I would segment those terms so I can control budget, bidding, and messaging more precisely.

Bottom line: I use n-grams to isolate themes that deserve special attention.

Once I have identified those themes, it becomes much easier to build advanced paid search structures around high-impact n-grams and improve ROI.

Dig deeper: How to uncover hidden gems in your paid search accounts

How I use Levenshtein distance to improve keyword quality

Levenshtein distance measures the minimum number of single-symbol edits, including insertions, deletions, or substitutions, needed to turn one string into another.

That may sound complicated, but the idea is simple once I put it into practice.

The Levenshtein distance between “cat” and “cats” is 1 because I only need to add the “s.” Between “cat” and “dog,” the distance is 3.

One common PPC use case is finding brand and competitor misspellings inside search term reports.

For example, “uber” and “uver” have a Levenshtein distance of 1, so I would feel confident excluding the misspelled version from non-brand campaigns.

I can apply the same logic to keyword relevance.

If the distance between a keyword and the search terms it matches is too high, such as 10 or more, those terms probably have very little in common with the keyword and deserve review.

A low distance usually tells me those queries are close enough to be safe and do not need the same level of manual inspection.

How I consolidate PPC keywords with Levenshtein distance

After I use n-grams to create initial keyword clusters, I may still have thousands of search terms to organize into a practical campaign structure.

Manually sorting through 6,000 unigrams is not realistic. This is where Levenshtein distance becomes especially useful.

Venn diagram showing sets A and B with their overlapping intersection labeled A&B, illustrating Jaccard similarity for SEO and PPC keywords. — A simple Venn diagram visualizes how Jaccard similarity measures the shared overlap between keyword sets A and B in semantic PPC and SEO analysis.

My goal is to merge ad groups that target nearly identical keywords so I do not end up with an overly granular, SKAG-like structure.

Too much granularity makes reporting and account management harder. It can also create inefficient bidding and wasted spend.

Using the same dataset, I calculate the Levenshtein distance between queries across different ad groups.

Then I identify the closest keyword and ad group using a predefined threshold. A threshold of 3, for example, gives me a high degree of accuracy.

This helps me consolidate keywords and ad groups with confidence. If I use a looser threshold, such as 6, I can also group or name ad groups by broader similarity or intent.

Here is a simple example showing why these three keywords can be grouped together:

Levenshtein distance	24/7 plumber	24 7 plumber	247 plumber
24/7 plumber	0	1	1
24 7 plumber	1	0	1
247 plumber	1	1	0

Dig deeper: How to use negative keywords in PPC to maximize targeting and optimize ad spend

How I go further with Jaccard similarity

In PPC, I use Jaccard similarity as a practical proxy for understanding the overlap between two sets of n-grams.

The calculation is straightforward: I divide the number of shared unigrams between two sets by the total number of unique unigrams across both sets.

It sounds technical, but I visualize it simply:

Jaccard similarity = Red / Green

Here are a couple of concrete examples I use to explain the concept:

“new york plumber” and “plumber new york” = 1 because all three unigrams appear in both sets, just in a different order.
“new york plumber” and “NYC plumber” = 0.25 because only “plumber” is shared, and there are four unigrams in total.

Jaccard similarity is a helpful first step for deduplicating similar keywords. I see it as a bridge between old phrase match logic and broad match modified logic.

But it has an important limitation: it does not understand meaning.

In the example above, “new york” and “NYC” should be treated as equivalent, but the Jaccard calculation sees them as different.

To handle that kind of nuance, I need more advanced techniques, which I would treat as the next layer of analysis.

How I combine Jaccard similarity and Levenshtein distance

Consider a cybersecurity course campaign with the following top 10 keywords:

Keyword	Semrush average monthly searches in the U.S.
cybersecurity courses	5,400
cybersecurity online course	1,900
free cybersecurity courses	1,300
online cybersecurity courses	1,300
cybersecurity course	1,000
cybersecurity courses online	880
google cybersecurity course	880
cybersecurity courses free	720
cybersecurity free courses	590
cybersecurity online courses	480

By combining singular and plural versions, along with reordered versions of the same idea, I can reduce that top 10 into a more actionable top four.

“Cybersecurity courses.”
“Cybersecurity courses online.”
“Free cybersecurity courses.”
“Google cybersecurity course.”

I could use n-grams to do this, but scaling n-gram analysis across thousands of keywords can quickly become overwhelming.

A more efficient approach is to use both similarity metrics in sequence.

First, I apply Levenshtein distance to consolidate very similar queries.
Then I use Jaccard similarity to deduplicate reordered variants.
At each step, I sum the usual KPIs, including cost, conversions, and other performance metrics, so the analysis stays actionable.

The result is a clear, compressed structure that can hold up even as search term volume grows.

How I restructure paid search campaigns with semantic techniques

With the right semantic techniques, I can restructure massive keyword sets quickly and still produce consistent, high-quality results.

AI can absolutely help me create an initial summary, but I do not rely on it entirely.

Otherwise, I run into the classic problem of “garbage in, garbage out.”

Broad match can be powerful, but it also introduces more noise. These techniques help me verify that the queries I am matching stay aligned with campaign goals.

I use n-grams, Levenshtein distance, and Jaccard similarity to apply client context to raw search data and build a stable structure around real intent.

If the process feels overwhelming at first, I use this summary to decide which technique fits the job:

Scenario	Best technique	Why
Identify high-intent patterns in huge search-term exports	n-grams	Surfaces themes fast; reduces dimensionality
Clean duplicate / near-duplicate keywords at scale	Levenshtein distance	Captures spelling + structural similarity
Deduplicate reordered or slightly varied keyword strings	Jaccard similarity	Order-insensitive token-based comparison
Create scalable clusters for campaign rebuilds	Combo: Levenshtein → Jaccard → n-gram	Sequence gives accuracy + compression

For me, the main lesson is simple: AI can accelerate PPC and SEO work, but semantic analysis gives that work structure, signal quality, and strategic control.

Inspired by this post on Search Engine Land.

July 3, 2026

The Future of SEO Leadership: Navigating the Complexity
The job posting from Anthropic that everyone seems to be discussing is becoming the new standard. Companies who get this right are poised to quietly dominate the next decade.
The latest Anthropic job listing is causing a stir in the SEO community. They may as well have called it the Search Gawd position. To be honest, this is a reality across the board.
I’ve penned this kind of job description multiple times and even interviewed for it myself. I’ll admit, I haven’t seen many of these roles actually filled, but I’ll touch more on that shortly.
Titles vary—from Head of SEO to Director of AI Search, and even VP of Search or Agentic Commerce GEO Consultant. Lots of titles, same core responsibilities: manage technical SEO, grasp paid search, direct content, collaborate with engineering, build metrics, prepare for AI discovery, and translate it all into growth.
It’s predictable that people think this sounds like several jobs rolled into one—a single employee carrying the weight of an entire agency. This might be a fair observation, but it misses the critical point.
Businesses have been on the lookout for such talent for years. The rise of generative search is now compelling action.
This Isn’t Just an Anthropic Issue
While browsing job boards today, I noticed:
- Victoria’s Secret: Director, AI & Organic Search (AEO, GEO, SEO), $152K–$216K.
- Publicis / Starcom: VP, SEO (Performance Content).
- Accenture: Agentic Commerce GEO Consultant.
- SailPoint: AEO/GEO Manager.
- AirOps: Senior SEO Manager spanning SGE, Perplexity, ChatGPT, Gemini.
- Responsive: Senior Manager, Web Strategy — SEO, GEO, plus Next.js, React, Vercel, DNS.
- Danaher, Experian Health, Amazon News: variations of SEO + AEO + GEO.
- Anthropic: SEO Lead, $255K–$320K.
Diverse industries, varying salaries, yet they’re all unconsciously seeking the same elusive candidate.
Misalignment Between Titles and Responsibilities
Consider Agency X looking for a “Director, SEO/SEM,” whose job includes no SEO—just paid platforms, vendor management, and leading a team of seven.
Then there’s Consulting firm Y, seeking a “Director, SEO/AIO,” without clarifying what AIO entails. A smaller agency’s “VP/Director, SEO” asks for paid search, social, and pharma marketing as preferred skills.
A research firm is hiring a “Director, SEO & AEO,” which accurately reflects SEO and AEO duties—an unusual alignment worth highlighting.
If the company can’t settle on pre-defining the role, a candidate standing a chance seems improbable. The taxonomy says one thing, the JD another, the recruiter screens for something else, and the manager interviews for yet another role. Meanwhile, the applicant tracking system (ATS) disregards viable candidates.
In Search of the Missing Link
You’re searching for someone who can bridge technical search, content, PR, product, engineering, analytics, performance media, and brand—someone who knows these interactions are more intertwined than they appear on organizational charts.
Search highlights these intersections. Technical issues may seem like content issues, and content problems could stem from product issues. Visibility issues might be about authority, not just optimization. Paid search often uncovers messaging issues quicker than brand research does.
In the era of generative discovery, these connections can’t be ignored. When results provide answers, SEO shifts from being purely traffic-driven.
To sidestep into Yoda-speak to avoid AI jargon: information exists only if the infrastructure supports it. Content helps understanding, brand garners trust, and product transforms discovery into utility—or it doesn’t.
You’re not expecting one individual to tackle every task; rather, you want someone who understands the cohesion of these parts. That candidate exists, but traditional systems make it difficult to find them.
The Résumé Might Surprise You
The candidate you need won’t be evidently showcased by years with an SEO title or specific software lists. It’s about their judgment:
- Identifying crucial technical issues versus distractions.
- recognizing when content struggles require external resolution.
- Knowing when to invest, automate, or pause, and when to advise leadership against certain actions.
This kind of discernment doesn’t easily translate onto a résumé. The right candidate might have navigated through various roles in agencies, publishing, product, consulting, and operations. Their career might not appear streamlined like a specialist’s, yet that very diversity equips them for this role.
Unfortunately, your ATS will likely disqualify them, while your recruiter labels them as “non-linear.” Your hiring panel might note they’ve never held the precise title before. But remember, this role didn’t exist before, and there’s no consensus on its name.
Clearly, this selection process is heading off-course.
The Alsotative Possibility
Some processes may be more about absorbing insights from interviewing candidates than actually filling the position.
Senior candidates often diagnose: detailing function structure, identifying organizational weaknesses, outlining first-90-day plans, recommending tools, and highlighting tasks to abandon. By inviting numerous candidates, companies might inadvertently gather varied organizational strategies and priorities without making any hires.
Perhaps that wasn’t the original intent. But if roles remain unfilled for months, resurface repeatedly, alter their titles and scope, and produce interview-like advisory sessions, candidates are right to question what the company truly seeks: talent acquisition or strategic input?
Addressing the Real Issue
Narrowing the job description won’t eradicate the work needed. Focus on deciding the core requirement. Is it:
- A specialist to execute tasks?
- A leader to assemble a team?
- An executive to integrate search, content, product, brand, and performance?
- A consultant to advise on necessity?
These are distinct roles, and expecting them to merge into one is unrealistic.
A Final Thought
I’d excel at such a role, along with a few others who’d be filtered out for the same reasons.
Concerning the Anthropic opportunity, it isn’t materializing for me.
Five years under a nonexistent title from five years ago? My resume doesn’t show that. It matches the job spec — perfectly tailored for ATS rejection. It’s a straightforward system to manipulate, especially for those seasoned in the field.
The elusive talent is indeed genuine. Generative search only spotlighted the gap. Before your company finds someone to bridge these systems, ensure the capability to recognize, hire, and support them.
The companies that master the art of identifying the right candidate—and not just crafting an ideal job description—will take the lead in the coming decade. Meanwhile, others will continue LinkedIn debates about whether GEO is truly a word.

Inspired by this post on Search Engine Land.
June 23, 2026
Channel Strategies: Broad Approaches vs. Focused Commitment

When I first started looking at budget allocation, I was tempted to believe that every marketing channel followed the same path: spend a little, get a lot, but with diminishing returns.

Visually, it’s easy to assume all channels mimic this pattern.

The typical log-shaped curve illustrates that the first dollar you spend is often the most productive. With this mindset, spreading the budget across numerous channels seems like the go-to strategy.

However, I quickly learned not all channels conform to this model. Some require much more than just a sprinkle of funds to be effective. These channels start with a less efficient spend but eventually pay off if given time to warm up. This condition shifts away from the usual ‘test small, scale the winners’ strategy many marketers follow.

At the core of this difference lies a fundamental question: Is the response curve C-shaped or S-shaped?

Understanding the shape of the response curve can drastically change how I conduct channel testing and measurement, especially with Google’s increasing inclination towards S-shaped campaigns.

Let’s delve into what these two curves signify and why they are crucial.

Response curves plot conversions or revenue against spend. Typically, we encounter two main types in marketing.

A C-shaped curve means diminishing returns kick in from the first dollar spent. Meanwhile, an S-shaped curve starts slow, becomes steep at the inflection point, and finally leads to saturation.

This insight is crucial for allocation because the marginal curve—the derivative—guides budget decisions. Here, shapes diverge with significant implications.

For a C-shaped curve, the highest marginal return is from the first dollar, decreasing thereafter. Conversely, for an S-shaped curve, the initial return is low, increases up to a peak, and then declines.

This aspect of increasing marginal returns is pivotal. It’s what differentiates channels with productive small budgets from those that seem inefficient but could perform better when scaled correctly.

Mainstream marketing campaigns exhibit this principle clearly. For instance, if your CPA goal is $50, the way the S-shaped channel behaves under scaling tells a critical story.

A preliminary $10,000 test may misleadingly suggest failure, but at $20,000-$25,000, the channel might be your most cost-effective choice. Small trials in the warm-up phase mislead the eventual conclusion.

This common misconception arises as many automatically rely on ‘test small, scale what works’. Yet, without sufficient testing past the warm-up phase of an S-curve, we risk dismissing channels that could have been game-changers.

For allocation logic, in C-shaped channels, going wide is beneficial. One global optimum dictates that spreading your budget thinly across many channels generally works.

But with S-shaped channels, a small budget is inadequate. Either commit enough budget to surpass the inflection point or don’t invest at all. There is a true minimum budget to ensure viability.

In marketing, determining whether a channel requires breadth or depth is critical. Channels historically leaned towards a concave shape, although modern platform dynamics have blurred these lines.

The differences are increasingly relevant with AI-driven campaigns. For example, ‘AI Max’ necessitates sufficient conversion data to learn effectively, affirming the concave-to-sigmoid shift. Campaigns like PMax blend both response types, initially concealing inefficiencies through promising headline numbers.

The key is recognizing the harvest versus create dichotomy. Harvest channels, like branded searches, display fast saturation and diminishing returns. Still, creating new demand—especially through platforms like Meta or YouTube—demands investment beyond superficial trials for truly incremental growth.

In conclusion, understanding whether to expand broadly or concentrate deeply in a specific channel can transform the efficiency of a marketing strategy.

Inspired by this post on Search Engine Land.

June 23, 2026
Deciding to Build or Buy Your Next SEO Tool with AI Insights
Before I consider requesting a new SEO tool, I always ensure that I understand the trade-offs between custom solutions, SaaS platforms, and hybrid approaches that utilize both.

AI has empowered SEO teams, including mine, to become more ambitious about automation. Tasks that once required engineering support are now tackled easily with tools like Claude or ChatGPT.

This is thrilling, yet it brings a new challenge: the assumption that everything can be automated. In today’s language, it boils down to a single question: Do we build or buy the tool?

The build-versus-buy dilemma is intricate, made even more so by AI advancements. It isn’t merely about cost; it’s about security, maintenance, data access, internal capabilities, workflow fit, and whether a custom solution can stay reliable and useful as time progresses.

How AI Lowers the Barrier to Building

AI has drastically lowered the barrier to experimentation. Even those of us without technical know-how can now create custom GPTs, build workflows, connect data sources, or craft an internal AI assistant.

However, maintaining a tool over the years remains a challenge, even if I managed to build it initially with AI support.

AI significantly aids SEO teams in data analysis, pattern recognition, summarizing information, and recommending actions, saving us a lot of time. Ignoring AI would surely leave us trailing behind.

It’s essential to acknowledge that AI still hasn’t reached the level of human creativity. It excels at working from established patterns and predicting outputs. This could evolve in the coming years.

AI tools also come with unseen costs. Internally developed tools may appear free since their invoices typically bypass our SEO teams, but expenses from token usage, API calls, infrastructure, engineering time, security reviews, and maintenance do exist.

Many organizations, as noted by Reuters, are experiencing “AI sticker shock,” finding themselves unable to forecast usage-based AI costs accurately. Companies like Uber, reported by TechCrunch, have even established AI spending caps after exceeding their annual budget in only a few months.

Currently, marketing teams, including mine, aren’t the largest AI consumers compared to engineering teams. Yet, this could shift rapidly.

When this happens, our expenditures will undoubtedly rise, prompting organizations to evaluate which AI tools and processes genuinely add value as opposed to simply consuming our budget.

Start by Defining What You Need

Before choosing whether to build or buy, SEO teams must define their true needs.

Different Ways to Use AI and Automation

I’ve noticed that many teams, including ours, lump various solutions together, yet they differ in cost, complexity, and maintenance.
- A custom tool: Generally a complex internal system necessitating engineering support, often focusing on automation and potentially incorporating AI aspects.
- A custom workflow: A repeatable process built with numerous tools like a custom GPT, spreadsheets, and automation, usually with an AI layer.
- A custom layer on SaaS: Leveraging data from existing tools to shape personalized reporting, prioritization, or recommendation processes.
- A true AI agent: A system capable of taking more autonomous actions, such as scanning Slack and following up on pending communications.
Though similar, these are often misidentified. Overgeneralizing terms like “AI agent” can lead to cost and complexity misjudgments.

Look for Repetitive, Context-Rich Tasks

Our team is still exploring AI capabilities. So far, we have concentrated on daily tasks involving substantial manual work.

For instance, we developed a custom GPT to assess whether our content aligns with our personas and addresses their pain points. The aim is not to replace our copywriters or reviewers, but to ensure that content isn’t generic and suggest pertinent enhancements.

We’ve also leveraged AI for translations, monthly reporting, and creating a weekly summary that integrates meeting notes, Slack, and Jira to identify outstanding tasks or follow-ups.

One of our newest workflows converts internal meeting recordings into structured landing page briefs.

Such tasks are ideal candidates for AI-powered custom workflows, given their dependence on internal context, repeatability, and specific company knowledge.

Not Everything Should Be Built

A case from our team involved a colleague who vibe-coded a prompt tracking tool. Although a good start, data presentation required manual steps for trend graphing, soon becoming a maintenance hassle due to changes in LLM tools.

The core issue was reliability. For AI visibility and prompt tracking, we needed stable data presentation, leading us to switch to a specialized platform like Peec AI, rather than maintain our own version.

This experience was insightful, enhancing our understanding of the problem, complexities, and necessary features when considering external solutions.

Here’s my advice: whether opting to build or purchase a tool, always explore existing market solutions. It helps to narrow down the essential features, preventing reliance on non-essential ones.

Especially for business-critical tools like rank tracking and website crawling, smaller SEO teams without technical support should be cautious of building from scratch. Reliability should be prioritized when data is crucial for decision-making.

Use AI Where Your Data Already Lives

Consider buying a crawler, rank tracker, or AI visibility platform and focus on linking these with custom data like GA or GSC accounts, or CRM data. This integration allows comprehensive analysis in a single view.

MCP connections also warrant consideration. The Model Context Protocol is a standard for linking AI applications with external systems, enhancing current workflows.

Though not necessary to learn coding, understanding enough to ask the right questions is beneficial.

If sensitive data is involved, like proprietary research or customer details, it’s crucial to assess security risks. It may be safer to allocate engineer support to avoid compromising sensitive information.

Deciding on a custom tool requires acknowledging the full cost, including engineering time, security reviews, and API usage, despite invoices not being SEO-related.

Before requesting any tool, SEO teams should articulate the problem, expected value, cost comparison between building and buying, and potential consequences of taking no action.

Effective requests should not start with tool needs, but with the problem, its significance, tested solutions, and the proposed optimal solution.

How to Prioritize What to Build First

No one-size-fits-all matrix exists for prioritizing builds.

Tools vary; from website crawlers to content evaluation systems, each can’t be judged by identical criteria.

In doubt, start by mapping current workflows versus the ideal ones. Patterns often emerge, highlighting primary priorities.

The first group involves tools that aid revenue generation, like identifying content opportunities or improving conversion. Marketing, including SEO, seeks visibility and leads, thus revenue-centric tools can be higher priorities.

The second category concerns tools minimizing repetitive tasks. While they may not directly create revenue, they free up valuable team time for strategic work.

Quick wins should not be ignored. Stakeholders value timely results, thus a small project with potential returns within weeks can build trust and support larger initiatives.

Also, consider cross-team value in your decision. SEO problems often extend beyond one team. Collaborating with other teams can strengthen the business case for shared solutions.

Often, the best tool isn’t the most complex. Starting small could be the strategy for smarter progress.

Remember, effective scoping leads to good decisions. Even with AI easing the build process, proper scoping of what to build remains essential.
- Define the problem, expected value, user base, and post-launch maintenance.
- Engage with your team and other departments, identifying whether it’s solely an SEO issue or a broader business challenge.
- Avoid building for AI’s sake, or being swayed by impressive demos.
Neglecting scoping risks acquiring costly tools that don’t integrate with workflows or building internal tools beyond maintenance capabilities.

Thoughtful consideration of scope is crucial before opting to build, buy, or customize a solution.

Inspired by this post on Search Engine Land.
June 23, 2026
Discover YouTube’s New AI Tools for Enhanced Insights

Google has just unveiled some exciting AI-powered tools on YouTube. These tools are designed to reveal creator trends, enhance understanding of audience behaviors, and optimize marketing campaigns.

YouTube’s expansion of its toolset for creator marketing and campaign intelligence now includes features powered by Gemini. With these updates, I’m able to delve deep into identifying trends, understanding the creator audiences, and boosting the performance of my campaigns.

What’s happening: Google has introduced several insights and optimization tools across YouTube and Google Ads. As a marketer, these tools give me crucial visibility into trends, creator performance, and audience behavior.

The opportunity to make smarter creative and media planning decisions is more important than ever, especially in an AI-driven marketing world. That’s exactly what these new tools are designed to support.

Why I care: With deeper insights into YouTube trends, I can see which creators are resonating most with audiences and assess how my brand is performing in terms of both paid and organic content. This empowers me to make smarter choices about creator partnerships and campaign strategies.

What’s new:

More detailed trend insights: Google Ads’ Insights Finder now provides even more detailed trends in the U.S., giving advertisers like me a better view of what’s capturing attention on YouTube.

Brand Pulse data in Insights Finder: With the integration of select Brand Pulse metrics, I can now evaluate both my paid and organic efforts from a single location.

New creator insights API: The fresh Content & Creator Insights API offers agencies and partners more detailed information about YouTube creators and their audiences, enhancing my media planning and creator selection process.

Gemini-powered creative recommendations: Soon, Gemini will offer creative optimization suggestions for Demand Gen campaigns, including tips on visuals and creative elements that could boost performance.

The bigger picture: As content created by influencers plays a growing role in purchasing decisions and brand discovery, advertisers like me are keen to spot trends early and gauge creator impact effectively.

Google is banking on AI to help marketers like myself uncover insights quickly and plan more efficient campaigns.

Bottom line: YouTube is providing brands and agencies more data on trends, creators, and campaign performance. Using Gemini, these insights can be transformed into more robust creative and media decisions.

Inspired by this post on Search Engine Land.

June 23, 2026
Discover the Leading Aerospace GEO Agencies of 2026

As someone passionate about the aerospace sector, I had the opportunity to dive deep into the performance of 38 GEO agencies that significantly contribute to the defense, aviation, and commercial space industries. Over five months, ending in June 2026, we thoroughly evaluated each agency using six crucial metrics.
The six factors we considered included:
Average Review Score (25%): I looked at ratings from major platforms like Google, Clutch, and G2, all normalized to a 1-5 scale.
AI Visibility Score (20%): This proprietary metric assesses how often an agency’s clients appear in AI-generated responses on platforms like ChatGPT, Perplexity, Gemini, and Claude.
Leadership Experience Score (20%): An evaluation of each agency’s leadership based on tenure, industry background, and influence in GEO and B2B marketing.
Notable Clients (15%): Experience with prominent aerospace companies, weighted by the complexity and scale of engagements.
Year Established (10%): A measure of the agency’s history and experience in the B2B sphere.
Media References (10%): The frequency of mentions in aerospace media, indicating industry reputation.
Through this rigorous process, we identified the top eight aerospace GEO agencies of 2026.
The Top Aerospace GEO Agencies of 2026
1. First Page Sage: Leading with a rich history and exceptional projects for clients like NASA Jet Propulsion Laboratory.
2. Driven Metrics: A data-centric approach delivers transparency and actionable insights.
3. Focus Digital: Known for cost-effective strategies and fostering growth in smaller aerospace entities.
4. Genevate: Pioneering in AI platform citation and authority-building.
5. The ABM Agency: Expertise in creating precise, account-based marketing strategies tailored for aerospace.
6. Echo-Factory: Provides comprehensive marketing solutions for the aerospace sector.
7. Haley Brand Aerospace Agency: Specializes in brand development with an extraordinary focus on client success.
8. Aviation Business Consultants: Offers well-rounded digital marketing services, enhancing SEO for aviation clients.

Inspired by this post on First Page Sage Blog.

June 19, 2026

Discovering the Top GEO Consultants for Breakthrough Success

Recently, as AI-powered search has taken center stage, I’ve been pondering a common question many marketing leaders face: not whether to invest in Generative Engine Optimization (GEO), but rather, who is the right expert for this game-changing strategy.

To answer this, I decided to delve deep, gathering extensive data on 43 top GEO practitioners. I carefully evaluated each consultant against seven essential, weighted criteria to serve as a guide on who currently stands out in the field.

My evaluation metrics included:

Client Results (25%): Demonstrable GEO successes with renowned brands.
Published Research Articles on GEO (20%): Number of innovative studies and methodologies published, reflecting an expert’s methodological depth and reproducibility.
Media References (15%): Frequency of mentions in notable industry and general publications, which acts as proof of the expert’s thought leadership.
Technical GEO Expertise (15%): The practitioner’s profound knowledge and skill in GEO and SEO strategies.
Years of Experience in SEO (10%): Direct hands-on SEO years; even as GEO evolves, SEO fundamentals remain an invaluable metric.
GEO Keynotes (10%): The number of significant conference appearances dedicated to GEO and AI search trends.
LinkedIn Following (5%): An indicator of thought leadership and influence in the digital community.

After meticulous consideration, I identified the leading consultants in GEO, and here are the insights presented in the table below.

The Top GEO Consultants of 2026

Rank	Consultant	Client Results	Published Research Articles on GEO	Media References	Technical GEO Expertise	Years of Experience in SEO	GEO Keynotes	LinkedIn Following	Specialty
1	Evan Bailyn	GEO wins for Salesforce, Microsoft, Chanel, LinkedIn, and US Bank	~35	~2,400	Advanced Generative Engine Optimization	22+	~20	7K	Lead generation, brand building & thought leadership through GEO and SEO
2	Aleyda Solís	SEO/GEO wins with global enterprises	~15	~1,680	Multilingual AI	18+	~20	115K	International & multilingual GEO
3	Lily Ray	Successful consulting for Fortune 500s	~25	~890	AI quality signals	16+	~15	52K	Search quality & AI trustworthiness
4	Kevin Indig	Wins with Shopify, G2, Atlassian	~30	~1,250	LLM traffic patterns	15+	~10	61K	AI search metrics & business impact
5	Marie Haynes	Consulting wins for mid-market and enterprise brands	~20	~750	Agentic Search & AI Overviews	16+	~10	18K	Agentic search preparation & AI citation quality
6	Ross Simmonds	Success with Canva, Jobber, and Procore	~12	~1,100	Distribution strategy	10+	~8	59K	Content distribution for AI visibility
7	Gaetano DiNardi	Wins for 40+ B2B SaaS companies	~10	~600	B2B SaaS AI SEO	10+	~8	50K+	AI SEO for B2B SaaS companies

Evan Bailyn

Evan Bailyn founded First Page Sage in 2009 and has remarkably transformed it into the largest GEO firm in the U.S. His pioneering work recognized generative engine optimization as a crucial marketing discipline by 2023.

His strategy is rooted in fostering thought leadership content that AI algorithms frequently reference. In April 2026, I found him delivering a keynote at the AEO Engine event, helping companies develop strategic research and scalable client delivery approaches.

Client Results: Outstanding GEO achievements with Salesforce, Microsoft, Chanel, LinkedIn, and US Bank
Published Research: ~35
Media References: Exceeding 2,400 mentions
Technical GEO Expertise: Advanced proficiency in Generative Engine Optimization
SEO Experience: Over 22 years
GEO Keynotes: ~20 speeches
LinkedIn Following: 7,000 followers
Specialty: GEO and SEO strategies for lead generation, branding, and thought leadership

Learn more or reach out via First Page Sage

Summary of Online Reviews

Clients appreciate Bailyn for his “unique, data-backed, and meticulously precise analysis,” along with a reputation for “highly tailored and instantly actionable strategies.” Yet some warn that “his calendar is often booked well in advance.”

Aleyda Solís

I discovered Aleyda Solís as the visionary behind Orainti and the LearningAIsearch platform. Her work sheds light on the intricate world of multilingual and international GEO, emphasizing the need for linguistic flexibility beyond English-speaking markets.

Her insights highlight a critical gap: AI systems, predominantly trained on English data, often falter in other languages. For global brands navigating diverse markets, Solís brings unmatched geographic and linguistic depth to the table.

Client Results: Success with global enterprises in SEO/GEO
Published Research: Around 15 articles
Media References: ~1,680 citations
Technical GEO Expertise: Focused on Multilingual AI
SEO Experience: Over 18 years
GEO Keynotes: ~20 delivered
LinkedIn Following: 115,000 followers
Specialty: Navigating international and multilingual GEO challenges

Discover more or reach out via Orainti

Summary of Online Reviews

International clients praise Solís for having “deep cross-market GEO fluency” and for crafting “practical multilingual frameworks.” However, those targeting English-only markets may need to “adapt portions of her guidance.”

Lily Ray

Founding Algorythmic, Lily Ray has emerged as a thought leader on E-E-A-T, focusing her research on how these quality signals influence AI citations in LLMs. Her diagnostic skills are essential for brands excelling in traditional SEO but lacking AI presence.

Ray offers a laser-focused strategy, filling E-E-A-T authority gaps to enhance AI search visibility. However, for broader needs like content strategy or technical execution, her work complements rather than replaces a complete GEO program.

Client Results: Triumphs with Fortune 500 brands
Published Research: ~25 papers
Media References: ~890 mentions
Technical GEO Expertise: Specializes in AI quality signals
SEO Experience: Over 16 years
GEO Keynotes: ~15 published
LinkedIn Following: 52,000
Specialty: Enhancing search quality & AI trustworthiness

More about her work can be found here

Summary of Online Reviews

Professionals and clients appreciate Ray’s “diagnostic approach for AI search gaps,” valuing her for “evidence-based, rigorous recommendations.” While some find her methods “conservative,” this conservatism is often considered a strength.

Inspired by this post on First Page Sage Blog.

June 19, 2026

Discover the Power of Google’s New AI Agent for Ad Manager

I’m excited to share that Google has taken a significant step in integrating Artificial Intelligence into publisher workflows by launching a new AI agent called Ask Ad Manager. This innovative tool leverages a Gemini-powered assistant to help us analyze performance and take action seamlessly through a user-friendly chat interface.

Google is embedding AI into publisher workflows, making it easier to analyze performance and act on insights from a chat interface.

Incorporating generative AI into Google Ad Manager, Ask Ad Manager is specifically crafted to assist publishers like myself in analyzing performance, troubleshooting issues, and navigating the Ad Manager platform effortlessly by using natural language.

The beta version is set to roll out this month, marking Google’s deeper foray into AI-supported ad operations.

What’s happening. Ask Ad Manager acts as a conversational AI agent dedicated to Google Ad Manager users who are publishers. Unlike conventional reporting tools, it allows us to pose questions in everyday language and receive tailored answers, recommendations, and reports based on our own Ad Manager data.

Google assures that this tool is engineered to help us swiftly transition from analysis to action, drastically reducing the time spent on generating reports, diagnosing issues, and navigating the Ad Manager platform.

What it can do:

Troubleshoot delivery issues. Instead of manually gathering reports to understand why certain line items are underperforming, I can now ask the AI agent questions and receive insights on the possible causes and recommended next steps.

Generate reports on demand. With a simple prompt, I can request customized metrics, benchmarks, and performance reports without the hassle of building multiple reports manually.

Navigate Ad Manager faster. Ask Ad Manager guides me to relevant pages on the platform and automatically applies suitable filters and settings rooted in the ongoing conversation.

Why we care. As a publisher managing large inventories and complex campaigns, having the capability to quickly uncover insights and diagnose issues can significantly reduce operational workloads and speed up decision-making processes.

Moreover, this feature signifies a broader trend in ad tech towards employing AI agents that not only generate information but also enhance workflows and task execution.

Looking ahead. According to Google, Ask Ad Manager marks just the start toward a future they envision as being more “agentic”, enhancing advertising operations comprehensively.

Google plans to unveil additional AI features throughout the year, incorporating developer tools like REST APIs and an MCP server aimed at supporting workflow automation and integration efforts.

They’re also working on developing specialized agents that could assist publishers and agencies in exploring inventory, negotiating deals, and executing campaigns with improved efficiency.

Bottom line. Ask Ad Manager introduces Gemini-powered assistance directly within Google Ad Manager. It offers a novel way for us publishers to access insights, resolve issues, and manage advertising operations all through natural language prompts.

Inspired by this post on Search Engine Land.

June 18, 2026
New Google AI Opt-Out: A Smart Move or Risky Gamble?

Recently, I discovered that Google introduced an AI opt-out feature, and it got me thinking.

For as long as I can remember, we’ve been pushing Google for more insight into AI traffic and control over our content’s portrayal in AI settings.

Now, this week, Google answered us with new controls allowing site owners to opt out of AI-powered experiences, like AI Overviews and AI Mode, coupled with fresh AI reporting tools in Google Search Console. Although still in early beta, it signals progress.

Despite this being a step forward, it’s sparked a split. Some are excited about the reporting aspect, while others debate whether opting out is wise.

What intrigued me wasn’t the announcement itself, but how swiftly the conversation pivoted from seeking visibility to potentially forfeiting it.

Let’s clarify what Google really launched with their announcement. The new controls don’t hinder AI Overviews or user engagement with AI Mode, nor do they stall AI’s momentum. Users will continue to engage with AI for searching and queries.

Essentially, publishers have a newfound ability to determine whether their content appears in AI-powered experiences. Was it Google’s plan or a response to external pressure, such as the UK Competition and Markets Authority?

This isn’t a debate about AI itself disappearing. What changes is brand eligibility within AI interactions. If a site like Expedia opts out, people will still plan trips—they’ll just find someone else in the AI-generated responses.

The choice is not about AI’s success, but rather about whether your brand remains present when users turn to AI solutions.

I get it—the appeal to opt out stems from fears around lost traffic and how AI uses our content.

Yet, assuming that opting out changes user behavior is where I disagree. Users aren’t concerned about a brand’s participation; they’re using AI to get quick answers.

Opting out may seem like a decision to curb AI adoption, but it more so enhances your competitors’ visibility. They snag the spotlight and gain trust while yours potentially fades.

The goal isn’t just visibility reduction—it’s about evolving with search behavior changes to remain seen.

Google’s announcement didn’t just focus on opting out but also on the new AI data they’re offering. Though imperfect, it’s a step towards greater transparency in AI search interactions.

Despite demands for more comprehensive reports, reality shows SEO has long dealt with imperfect data. Some of SEO’s big wins came from leveraging imperfect data.

Hence, we shouldn’t be stuck waiting for flawless data. While not perfect, it’s more than what we had before and will likely evolve further.

In my approach, reporting must expand beyond traditional SEO metrics, encompassing a wider discovery landscape, including AI and interaction insights.

We need to assess brand mentions, citation frequency, and how they’re perceived across differing AI platforms. Visibility stretches beyond mere traffic metrics.

Ultimately, we must rethink our questioning. Instead of asking, ‘Should I opt out of AI?’, ask, ‘Can I afford to be absent where users find brands?’ They’re already in these spaces—why shouldn’t we be?

Google’s update isn’t just a feature but a strategic pivot. By choosing to opt out, you aren’t erasing AI; you’re simply amplifying someone else’s presence.

Are you ready to adapt, or will you stay behind, longing for Google’s ‘free clicks’?

Inspired by this post on Search Engine Land.

June 18, 2026
Uncover 7 Unmissable AI Search Trends Transforming Marketing
AI search is reshaping the marketing landscape faster than anything I’ve seen before.

During my time at Zero Click NY, I witnessed how significantly AI search has evolved over the last six months and identified emerging features that might define its future.

Among all the discussions, these seven trends were the most compelling.

From the emergence of marketing engineers, to the way Claude and ChatGPT differ in results, and Claude’s rapid ascent in the business world over the past year, here are the key insights I gathered.

1. Every AI relies on different content

According to Profound data, only 8% of citations are shared between ChatGPT and Claude. This means 92% of the sources that ChatGPT refers to would not be recognized by Claude for the same inquiry. Essentially, a brand may have high visibility in one AI and not exist in another.

Moreover, each AI favors different types of content.
- ChatGPT frequently indexes community content: Reddit, Quora, and forums make up around 16% of its citations.
- In contrast, Claude cites listicles 36% and opinion content 13.2% of the time, compared to ChatGPT’s ~20% and 7.2%, respectively.
The disparity also applies to traditional search. A significant 64% of websites Claude cites appear in Google’s top 50 for equivalent queries, whereas it’s only 37% with ChatGPT.

Takeaway: It’s vital to inform stakeholders that AI visibility differs between LLMs, and strategic prioritization is necessary to reach your audience.

Track your visibility by engine because effective strategies in one platform may not translate to another. UGC helps drive ChatGPT while listicles boost presence on Claude.

2. Claude is quietly winning B2B — so sequence your optimization by audience

Claude may appear insubstantial in AI traffic-share charts, but it’s a different story when it comes to enterprise usage.

Web traffic doesn’t tell the whole tale. Anthropic derives about 85% of its revenue from enterprise and API usage, not visible in consumer data.

A critical chart from Ramp’s AI Index reveals the true penetration of Anthropic in the business sector. A year ago, only a small number of businesses used Anthropic. Now, it’s at 34.4%, surpassing OpenAI at 32.3%.

This insight led me to reconsider: if more business users are engaging with Claude and consumers are on ChatGPT, shouldn’t our optimization priorities reflect audience preferences?

Should B2B entities focus on Claude first, while B2C aim for ChatGPT visibility?

Currently, few distinguish between ChatGPT, Gemini, or Claude usage. This distinction is bound to grow.

3. ChatGPT ads are here, and this is what we’re seeing

The game has changed: competitors are securing visibility through ChatGPT ads. These ads are now live and available for self-serve directly within the chat interface.

Recent weeks also saw the debut of GPT 5.5, citation chips morphing into clickable links (leading to a 60% spike in referral traffic overnight), and Google integrating AI Mode into its main search functionality.

This wasn’t incidental. The hyperlinks are crucial for an ads business. Analyzing over 100,000 ad placements highlighted three essential revelations.

ChatGPT Ads match on topic

Ads align with topic similarity, not intent. Only 14% of real user prompts express commercial intent, yet 20% show ads, even if the prompt involves a math problem.

Embedding analysis indicates that ad titles and descriptions significantly influence which conversations you appear in, transforming them into tactical targeting tools.

Paying for ads

We have entered a “pay-to-play” era. Approximately one-fifth of ad placements appear when a direct competitor is mentioned, but only 8% of organic references belong to the associated brand.

Competitors are twice as likely to advertise around your brand’s organic mentions than you are.

For instance, Startup CRM Adia is targeting prompts mentioning Salesforce, with Salesforce responding by showing paid ads 40% of the time, defending their position even when organically mentioned.

Ad inventory is scarce and expensive

Currently, ChatGPT presents about one ad per conversation, with the median exchange spanning three turns. Only 30% of eligible users ever see ads, and CPMs/CPCs are about four times Meta’s rates.

Expect future changes: additional ad slots per reply, ads woven deeper into conversations, and engineered suggestions to prolong interactions, ultimately increasing inventory.

The insight: Understanding both organic AEO and paid defense strategies is essential. Monitoring your brand’s organic citations without tracking who advertises against them offers a partial view.

4. Claude is the most directly optimizable AI right now

Claude sources web content directly from Brave searches, not merely influenced by it, as discussed in the presentation I attended.

In recent testing by Profound, 79.2% of Claude’s citations were directly aligned with Brave’s top 10 search results for equivalent queries.

Reshuffling is minimal; no other AI model trusts its search provider so extensively.

This transparency makes Claude the most straightforward AI to optimize for: a visible index, checkable rankings, and, as we’ll explore next, predictable retrieval.

If I’ve convinced you of the importance of Claude for B2B, here’s your approach: identify where you stand on Brave for key prompts and use that as your roadmap for Claude visibility.

This level of transparency won’t last forever. Take advantage now while it’s possible.

Dive deeper: New insights suggest Claude’s visibility significantly depends on Brave Search rankings

5. Claude only performs web searches a third of the time

There’s a significant caveat: ChatGPT initiates web searches for nearly 95% of prompts, but Claude does so only a third of the time, likely due to cost ($5 per thousand searches via Brave’s API).

You can optimize Claude effectively only when it conducts a search.

The encouraging part is its predictable search habits. Prompts framed around recent events (“best X in 2026”) initiate searches about 81% of the time.

Ranking-related prompts lead to 67% search initiation, location-specific prompts 55%, and comparisons 51%.

Prompts concerning definitions and procedures rarely trigger searches, making them poor targets for Claude optimization.

The lesson: Before investing to enhance Claude visibility for a prompt category, determine if Claude actually conducts searches for it.

Focus on recent events, rankings, locations, and comparisons for effective Claude optimization using Brave rankings as a guide.

Other areas rely on internal memory beyond our reach.

6. Query fan-out: A raffle on one platform, near-deterministic on another

Two speakers offered perspectives on query fan-out, presenting a contrast worth exploring.

Query fan-out entails background synthetic queries to collect content prior to providing an AI-generated response.

Mike King of iPullRank viewed it as a raffle: The task is to gain more tickets through a wider content range across owned, earned, and shared channels, and the right content formats make all the difference.

Even if you rank for a fanned-out query, the wrong format renders you ineligible.

According to his research, content-to-query cosine similarity and information gain strongly correlate with success in AI search.

Conversely, Josh Blyskal from Profound notes that Claude’s fan-outs are highly predictable; the same prompt results in consistent fan-out strings 65% of the time. Interestingly, 94% of Claude’s fan-outs are current-year stamped, unlike ChatGPT’s 17%

Where ChatGPT’s fan-outs constantly evolve, Claude’s remain relatively stable. Thus, both perspectives may hold true for different engines.

With stable fan-outs like in Claude, content creation can directly focus on them. The year-stamping trend suggests using the current year in titles.

For volatile fan-outs as in ChatGPT, King’s approach applies: maximize exposure through format variety.

One mechanism demands two strategies, tailored by engine, potentially requiring prioritization between them.

7. The marketing engineer is here, and agents are the new workforce

The role of a “marketing engineer” might sound like a buzzword, but the hiring trends prove otherwise.

Google’s recently hired its first marketing engineer, Figma has an opening at a $295,000 salary, and both RBC and Autodesk have placed hires.

It’s a rapidly growing search term, and Google’s AI marketing lead dubbed it “the hire for 2026.”

What makes someone ideal for this role? Is the priority given to an engineer learning marketing or vice-versa?

The emerging profile emphasizes marketing experiences such as someone with channel expertise who builds and runs AI systems, reports to the marketing head, and supports the team by removing obstacles. They are marketers advancing the state-of-the-art.

The underlying concept is that marketing functions decompose into pipelines: data extraction, transformations, and loading into useful formats. Agents can now automate these pipelines.
- Monitoring competitor pricing and auto-generating sales content.
- Scheduling and assessing AEO presence and landing page efficiency.
- Analyzing sales call objections and drafting relevant content solutions.
What previously were backlogged tasks now become brief agent-building exercises. Creativity replaces headcount as the limiting factor.

If marketing engineering isn’t a role in your team yet, it’s likely only a matter of time before it is.

The job now: Figuring out how this all works

There remains no definitive roadmap for AI search. When a guidebook emerges, the key step will be prioritizing one LLM contingent upon who you wish to reach.

In many instances, that “who” will now be agents, simultaneously assisting us in our endeavors and highlighting the rising need for professionals adept at engineering such systems.

Inspired by this post on Search Engine Land.
June 17, 2026