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  • How I Build a Powerful SEO Budget Case My CFO Can’t Ignore

    How I Build a Powerful SEO Budget Case My CFO Can’t Ignore

    You're losing the SEO budget conversation before you walk into the room

    If I walk into a budget meeting armed only with rankings, traffic, and keyword reports, I know I am making the wrong case. CFOs do not approve SEO budgets because channel metrics look encouraging. They approve investments that reduce business risk, improve commercial outcomes, and justify the way capital is allocated.

    As AI reshapes search economics and customer acquisition costs continue to climb, I believe translating SEO into business risk is becoming as important as the search strategy itself. This is how I prepare for that conversation before I enter the room.

    Why I see SEO budget conversations break down

    A global enterprise software company recently shared a revealing example with us, and I keep returning to it because it captures the problem so clearly.

    One of the company’s core product lines generated 291 inbound demo requests during a single month in 2008. In the corresponding month of 2026, it generated only 274. Nearly two decades later, and despite a digital marketing budget roughly eight times larger, the business was producing fewer qualified opportunities.

    I do not see that as a simple search strategy problem. I see it as a structural problem—and the CFO had already noticed it.

    The head of search entered the budget review with a 24-slide deck. Slide 3 documented ranking improvements. Slide 7 highlighted year-over-year organic traffic growth. Slide 12 outlined keyword opportunities.

    Every number was accurate, but none of them answered the question that mattered to the CFO: Why was the company spending more each year to generate roughly the same number of qualified opportunities?

    The CFO let the presentation continue. Then, at slide 19, she put down her pen and said, “This is all interesting. But I can’t see the connection to pipeline.”

    The head of search began to explain. The CFO looked toward the CMO, and the meeting was effectively over.

    The lesson I take from this is that many search leaders lose the CFO budget conversation before they enter the room. Their strategies may be sound, and their numbers may hold up, but they arrive speaking in sessions, rankings, and organic traffic share. That is not the language of financial decision-making.

    When I prepare for this kind of meeting, I assume the CFO wants to discuss the P&L, risk, payback periods, and opportunity cost.

    If I open with “organic traffic grew 23% year over year,” I risk telling the CFO, unintentionally, that I cannot connect my work to revenue. If the CFO has already seen cost per opportunity moving in the wrong direction, that disconnect does more than create skepticism. It creates a reason to cut the budget.

    The structural shift I diagnose first

    I start with the diagnosis before I discuss tactics. Without a clear diagnosis, everything else becomes a more polished way to lose the same argument.

    In 2008, paid search behaved like an undersupplied monopoly channel: high intent, limited competition, and relatively linear returns. A dollar invested could produce a reasonably predictable return. There was no AI layer absorbing clicks before they happened, no army of comparison aggregators siphoning away high-intent traffic, and no group of competitors with 18 years to build organic authority in the category.

    That environment is gone.

    Today, I operate in a search landscape where organic authority is fiercely contested. AI Overviews can intercept high-intent queries before users reach paid ads, while attribution models designed for the old environment are still being used to defend budgets in the new one.

    The message I bring to a CFO is not simply, “I need more budget,” or, “Our rankings are improving.” I explain that the structural conditions that once made search efficient have changed, show how those changes affect commercial performance, and present my plan for adapting.

    Why I do not lead with channel metrics

    I understand the temptation to showcase channel performance. After spending months building organic authority, improving rankings, and growing traffic, I naturally want that work to be visible. The problem is that presenting it without a commercial connection can undermine the very case I am trying to make.

    CFOs have been burned by marketing attribution models before. They have seen enough ranking charts and organic traffic reports to know that neither metric connects directly to the P&L without additional evidence.

    When I lead with channel metrics, I invite two immediate questions: “According to which model?” and “What does this mean for revenue?” Every slide that raises those questions before I have framed the argument spends some of my credibility.

    How I handle the counterfactual problem

    The deeper issue is the question I expect every CFO to bring into the room: “Would this revenue have happened anyway?”

    I consider that the hardest question in marketing attribution, yet many budget presentations never answer it. They treat the connection between organic performance and commercial outcomes as self-evident. It is not. A CFO who has watched the marketing budget expand for a decade while blended customer acquisition cost drifts upward is right to challenge that assumption.

    If I am asked, “How do we know those customers would not have found us anyway?” and I do not have a prepared answer, I have lost the thread. That is why I do not build my budget case on an attribution model I cannot defend under pressure. I build it around something much harder to dismiss: measurable business risk.

    Dig deeper: Stop paying for traffic: The enterprise CMO’s guide to ROI-driven SEO

    How I frame SEO as business risk

    I think of CFOs primarily as risk managers, not channel optimizers. Their job is to protect the business from downside scenarios, allocate capital efficiently, and prevent unpleasant surprises in the P&L.

    If I enter the room talking only about upside—what a larger budget might achieve—I am appealing to the wrong instinct.

    Instead, I lead with downside and focus on three risks that a CFO can price, model, and act on.

    1. Competitive displacement risk

    I never treat organic search positions as permanent assets. They are contested positions in a live market. If I reduce investment, competitors do not pause and reduce theirs to match. They usually accelerate.

    I also avoid saying only, “We will lose rankings.” Rankings are still a channel metric. I frame the risk in commercial terms:

    • “A 30% budget reduction will not create a simple 30% reduction in output. I expect it to trigger a compounding decline over the next three to 18 months as competitor content accumulates, our positions erode, and the cost of recovering those positions exceeds the cost of maintaining them.”

    I am presenting a deferred-liability argument, not merely a channel-performance argument. It gives the CFO a risk that can be modeled. For example, I can calculate how much a 20% decline in organic share of voice would add to CAC over 12 months if paid search had to compensate for the lost visibility.

    When I show that calculation, I can move the conversation from “Can we afford this investment?” to “Can we afford the cost of withdrawing it?”

    2. AI visibility risk

    I see AI visibility as the newest and least understood risk in many boardrooms. That gives me an opportunity if I can explain it clearly and connect it to financial outcomes.

    As AI Overviews and LLM citations become a primary discovery layer for high-intent queries, I no longer think of organic authority solely in terms of rankings. I also ask whether our brand appears in the AI-generated answer.

    A paid campaign can often be restarted next quarter by adding budget. AI citation share is different. It depends on content depth, structured data, brand signals, and domain authority built over months or years. I cannot rebuild that visibility with a quick media buy; I need a content and authority program measured in quarters rather than weeks.

    The commercial connection is crucial. If I lose AI visibility, I do not just lose traffic. The business may have to buy back those same high-intent users through paid search, often at CPCs inflated by competitors that continued investing and preserved their citation share.

    I do not treat this as a distant concern. For many organizations, declining AI visibility can be the trigger for a broader CAC blowout, so I price the risk explicitly.

    The framing I use with the CFO:

    • “We currently hold strong AI citation share across our 10 most important commercial queries. I do not expect that position to maintain itself. Here is what it cost us to build, what I estimate it would cost to recover if we lost it, and the quarterly investment I recommend to defend it.”

    Dig deeper: The bureaucracy tax: How disruptors are winning AI search visibility

    3. CAC blowout risk

    I find that this risk lands hardest because it is already materializing in many enterprise organizations.

    Glowing blue streams of people converge on a search bar and digital portal, symbolizing SEO traffic, AI visibility, and customer acquisition.
    As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

    When I return to the enterprise software example, the year-over-year picture is even more revealing than the 18-year comparison:

    • April 2025: Roughly $420,000 in Google spend, 681 inbound demo requests, and approximately $617 per opportunity.
    • April 2026: Roughly $310,000 in Google spend, 418 inbound demo requests, and approximately $741 per opportunity.

    Spend fell by 26%, qualified opportunities fell by 39%, and cost per opportunity increased by 20% in one year. The deterioration happened not simply despite the budget reduction, but partly because of it.

    I expect a CFO to test a simpler explanation: Perhaps performance was already declining and the budget was cut in response. That is a reasonable hypothesis, but it does not fully fit the data. Cost per opportunity had started rising before the reduction. The cut did not create the original efficiency problem; it exposed a structural problem that already existed.

    The search environment had changed, but the budget strategy had not. AI Overviews were absorbing high-intent category and solution queries before many of those searches became clicks.

    At the same time, the organic authority that took years to build was generating fewer visits as zero-click search expanded. When paid spend fell, the organic foundation was not strong enough to carry the load. Together, the two effects caused more damage than either would have caused independently.

    This is how I explain the CAC blowout mechanism: When organic visibility weakens and paid media has to compensate, blended CAC rises. If paid investment is then reduced before the organic gap is repaired, CAC can rise even further.

    The CFO sees a negative trend and may conclude that search no longer works. I see a different problem: The structural relationship between paid and organic was never actively managed.

    I do not consider this unique to enterprise software. It is a predictable outcome when paid and organic search are managed as separate budget lines with separate accountability, as they still are in many enterprise organizations.

    The framing I use with the CFO: I show the relationship between organic share of voice and blended CAC across the previous 18 to 24 months. If organic visibility declined while paid CPCs rose, I have direct evidence of the risk.

    If I have completed a cannibalization audit and redirected spend away from terms where paid ads competed with strong organic coverage, I also present that work. Moving the budget toward genuine demand gaps gives me a concrete example of the structural fix in action.

    Why I brief the CMO before the meeting

    One of the most valuable preparation steps I can take is briefing the CMO before I enter the budget meeting. I do this not simply to seek approval, but to stress-test my argument.

    The CMO has usually participated in more CFO conversations than I have. They know which objections carry the most weight, which risks currently concern the CFO, and which parts of my case are likely to receive the greatest scrutiny. I cannot gain that perspective if I build the deck in isolation.

    A CMO who has already challenged and strengthened my argument becomes an ally in the room. A CMO who hears the case for the first time alongside the CFO can become a liability. If the CMO hesitates over a number or qualifies a claim I presented with confidence, the CFO will notice.

    That is why I brief the CMO and enter the meeting aligned. In my experience, much of the budget conversation is won or lost before anyone sits down.

    How I prepare for three inevitable questions

    Before I prepare the answers, I plan my opening move.

    I do not spend the first 60 seconds summarizing last quarter’s performance, and I do not jump into risk without establishing common ground. Instead, I begin with the structural diagnosis.

    I might say:

    • “Before I walk through the data, I want to explain why we are having this conversation. The search environment has changed materially over the past three years. I want to show how that change is affecting our cost per opportunity and what I recommend we do about it.”

    From there, I present the evidence, explain the risks, and prepare for the questions. These questions are not hypothetical. Search leaders hear them repeatedly, so I want my answers ready before I enter the room.

    “What happens if we cut this by 30%?”

    I do not respond by declaring the cut unacceptable or catastrophic. A CFO asking this question may be testing how well I understand the program’s efficiency curve rather than announcing an actual reduction. If I become defensive, I signal that I have not modeled the scenario.

    I prepare a specific answer in advance:

    • “A 30% reduction applied evenly across the program would cost us approximately [X] in organic traffic within six months. At our current organic conversion rate, that represents [Y] in pipeline impact. If we need to remove 30%, I would make these specific cuts to minimize commercial damage. This is the threshold below which I believe the program becomes structurally unsustainable and the cost of recovery exceeds the savings.”

    With that answer, I demonstrate P&L literacy, anticipate the follow-up questions, and shift the meeting from budget defense to business problem-solving. I am not protecting a line item; I am helping the CFO make a better capital allocation decision.

    “How do we know these conversions would not have happened anyway?”

    I do not try to defend an attribution model as if it were indisputable. I am unlikely to win that argument, and fighting it can damage the credibility of everything else I have presented.

    Instead, I acknowledge the attribution problem and pivot to incrementality:

    • “I agree that last-click attribution overstates organic search’s contribution, so I do not use it as my primary evidence. Instead, I track periods when organic visibility declined across our most important commercial queries and paid CAC increased as paid search compensated. I consider that our most defensible proxy for organic search’s incremental contribution, and I have deliberately kept the estimate conservative.”

    I find that intellectual honesty about attribution limitations builds credibility with a financially trained audience. CFOs have seen too many models that appear designed to prove whatever the presenter wants to prove.

    By acknowledging the limitation first and offering a conservative proxy, I can earn more trust than I would by making an aggressive ROI claim.

    “What is the payback period?”

    I avoid answering with a broad argument about long-term brand equity or compounding authority. CFOs working within quarterly reporting cycles are unlikely to approve capital based only on a three-year organic growth narrative. If I lead with that answer, I suggest that I do not understand how the allocation decision is being made.

    I separate the investment into two components with different payback profiles.

    Maintenance spend covers the work required to preserve existing positions, keep content current, and maintain technical health. I frame its payback as immediate because it protects value the business has already created. The relevant comparison is the future cost of recovering the positions if they are lost.

    Growth spend covers new content, category expansion, and authority building. For content aimed at existing demand with known search volume, I model the payback across six to 12 months. I make the assumptions visible, including query volume, conversion rate, and revenue per conversion.

    I show my work. If the CFO stress-tests my assumptions and challenges specific numbers, I consider that constructive engagement with the model. It is a better outcome than polite agreement followed by a budget cut because my methodology failed to inspire confidence.

    The data I leave behind—and the data I bring

    Before I build the deck, I decide what to remove. Most search budget presentations do not fail because they lack useful data. They fail because the valuable evidence is buried beneath metrics that erode credibility before the important numbers appear.

    What I leave behind

    • Keyword rankings in isolation: Unless I can connect a specific ranking movement to pipeline impact, I treat it as another channel metric that invites the counterfactual question.
    • Organic sessions without market context: If my traffic grew by 15% while the market grew by 40%, I lost ground. Without an external benchmark, year-over-year traffic growth gives the CFO little basis for evaluation.
    • Metrics that require a glossary: If I have to explain what a metric means before I can explain why it matters, I leave it out of the meeting. Every definition delays the commercial argument.
    • Long-term brand equity arguments: I do not reject these arguments, but I recognize that they are difficult to act on within a quarterly budget cycle. Leading with them creates a mismatch between my timeline and the CFO’s.

    What I bring

    Before I finish the deck, I decide what deserves the most important slide. I do not choose a generic traffic graph or ranking summary. I begin with a commercially meaningful statement such as:

    • “I estimate that organic search offset $[X] in paid-search dependency this quarter.”

    I lead with the money the program saved the business, expressed in language the CFO already uses. The supporting evidence follows:

    • Blended CAC across the previous 18 to 24 months, segmented by channel. I use this chart to expose the relationship between paid and organic performance and connect search investment to the P&L.
    • Organic share of voice compared with the three leading competitors over time. I use this to make competitive displacement measurable. If a competitor gained ground while our investment remained flat, I show it.
    • Pipeline contribution by channel under a conservative, clearly labeled attribution model. I state whether the model is last-touch, position-based, or something else. I find that transparent disclosure builds more credibility than an optimistic number that invites a methodological dispute.
    • A pre-modeled 30% reduction scenario with specific commercial consequences. I consider this the most powerful analysis I can bring because it answers the likely budget question before it is asked.
    • AI Overview citation share across the 10 most important commercial queries. I use our own data to ground the AI visibility argument. It demonstrates that I understand the changing discovery landscape without relying on vague industry generalizations.

    How I turn the meeting into a capital allocation conversation

    I do not consider the enterprise software company in this example an outlier. I see the same pattern across enterprise search: budgets rise, efficiency declines, and CFO skepticism grows as AI Overviews absorb intent, paid and organic remain disconnected, and reporting continues to reward channel metrics instead of commercial outcomes.

    I have learned that winning this conversation does not depend only on having the best search strategy. It depends on translating SEO into business risk in language a CFO can evaluate and act on.

    Before I enter the room, I brief the CMO, model the commercial effect of a budget cut, prepare a conservative answer to the attribution question, and separate maintenance investment from growth investment. That preparation is within my control, even though the structural shift in search—and the CFO’s skepticism—are not.

    Ultimately, I choose which conversation I am ready to have. I can defend a collection of channel metrics, or I can help the CFO make a capital allocation decision. Only one of those approaches gives my SEO budget a compelling business case.

    Inspired by this post on Search Engine Land.

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  • How I Turn Search Console Data Into SEO Wins With AI

    How I Turn Search Console Data Into SEO Wins With AI

    I rely on Google Search Console because it is excellent at collecting search data. The challenge is that it still does not make interpretation easy.

    When I open almost any property, I usually find thousands of queries, landing pages, impressions, clicks, rankings, and click-through rates. That volume is useful, but it can quickly become overwhelming when I am trying to answer one simple question: what should I do next?

    For years, my workflow was familiar: export the data into Excel or Google Sheets, build pivot tables, apply filters, and start digging for patterns. That approach works, but it is slow. More often than not, I am searching for insights without knowing exactly what I am looking for.

    That is where AI makes the workflow more useful. I use it to speed up the hardest part of Search Console analysis: finding meaningful patterns hidden across thousands of rows of search data.

    I think of Google Search Console as my source of truth and AI, whether ChatGPT or Claude, as the analyst sitting beside me. GSC shows me what happened. AI helps me explore why it happened, uncover opportunities I might miss, and organize messy data into decisions I can act on.

    A quick note on regex

    Most of the examples I use start in the same place inside Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).

    From there, I enter a regular expression to filter query data before exporting it for analysis.

    The useful part is that I no longer need to memorize regex syntax. I can ask ChatGPT to write it for me. For example, I might prompt: Create a regex for Google Search Console that matches queries beginning with question words.

    ChatGPT may return something like (?i)^(who|what|why|how|can|does|will|should)b.

    If I need something more specific, I simply describe the pattern I want. I might ask for a regex that matches queries containing five or more words, identifies comparison searches, or finds branded queries that include product names.

    The better I describe the pattern, the better the regex usually becomes.

    Here are seven practical ways I combine Google Search Console with AI so I can spend less time sifting through data and more time making decisions.

    1. I stop looking only at queries and start looking at intent

    Most Search Console analysis still happens at the keyword level. The problem is that people do not really search by keyword. They search with intent.

    Instead of reviewing thousands of individual queries one by one, I use regex to isolate investigation-focused searches before exporting the data.

    One useful regex is (?i)^(best|top|vs|review|reviews|compare|comparison).

    After exporting the filtered query data, I ask Claude or ChatGPT to classify intent. My prompt is usually something like: Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.

    This helps me spot patterns that are difficult to see keyword by keyword. Informational traffic may be growing while commercial investigation queries are declining. Transactional queries may rank well but earn weak click-through rates. Comparison searches may be driving impressions without having dedicated content to support them.

    When I segment by intent, the next steps become much clearer.

    2. I discover questions my audience is already asking

    Question-based keyword research is not new, but AI helps me identify themes across hundreds of question-oriented searches much faster.

    I start with a regex like (?i)^(who|what|where|when|why|how|can|does|should|will)b.

    Then I export the results and ask Claude or ChatGPT: Group these questions into common themes and identify unanswered topics.

    Google Search Console Performance report with the Query filter dialog open, showing a custom regex option for filtering SEO search queries.
    A Google Search Console query filter highlights how regex can narrow SEO performance data, helping marketers turn thousands of search terms into focused insights.

    Instead of manually reviewing hundreds of questions, I can quickly see broader patterns around pricing concerns, product comparisons, implementation challenges, and industry-specific use cases.

    This becomes more than a content exercise. I can use these themes to improve FAQs, support resources, sales enablement materials, and AI Overview optimization.

    The best opportunities are often not hidden in one query. They are hidden in clusters of related questions.

    3. I find queries likely to trigger AI Overviews

    Google does not give me a filter for queries likely to trigger AI Overviews, but I can build a useful approximation.

    I start by isolating common informational and comparison patterns with a regex like (?i)^(what is|how to|best|vs|difference between|guide to).

    Then I export the matching queries and ask Claude or ChatGPT: Review these queries and group them by the content format needed to answer them effectively.

    The themes often fall into definitions, tutorials, comparisons, or expert recommendations.

    This helps me see where my content may need to shift from simply ranking for keywords to becoming the best available answer. Increasingly, those are not always the same thing.

    4. I track emerging trends earlier

    Traditional keyword research can be reactive. By the time a trend is obvious in keyword tools, competitors may already be building content around it.

    Google Search Console can help me identify shifts earlier, as long as I know how to look for them.

    Instead of searching for individual keywords, I use ChatGPT to build regex around broader concepts. For example, I might prompt: Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.

    The output may look like (?i)(ai agent|agentic|copilot|assistant|automation).

    This same approach works for new technologies, product categories, competitors, industry buzzwords, and changing customer concerns.

    Once I filter and export the data, I let AI look for emerging themes. A prompt I like is: Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.

    Instead of only confirming that a trend exists, AI helps me decide whether the trend is meaningful enough to act on and what the next move should be.

    5. I surface conversion intent inside informational traffic

    One of the most overlooked opportunities in Search Console is finding bottom-of-funnel signals inside queries that appear informational at first glance.

    I might ask ChatGPT: Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.

    An example output is (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration).

    I apply that regex to the query report, export the filtered data, and then ask Claude or ChatGPT to analyze it.

    My prompt usually looks like this: Review these Google Search Console queries and identify recurring buying signals. Group them into themes such as pricing, comparisons, implementation, and vendor evaluation. Recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.

    AI analyzes Google Search Console query data, funneling search intents into eligible and not eligible audience groups for SEO action.
    A visual metaphor for AI turning messy Google Search Console queries into clear SEO decisions, separating qualified intent from irrelevant traffic signals.

    I often find that pages created for top-of-funnel education are already attracting visitors who are evaluating solutions. In that case, the best opportunity may not be creating a new page. It may be improving the page that already earns the visit, so users can take the next step without breaking the informational experience.

    Sometimes the biggest content opportunity is recognizing the conversion intent already reaching the pages I have.

    6. I find audience-specific opportunities

    One of my favorite ways to uncover new content opportunities is filtering queries by industry, audience, or customer segment. It quickly shows me whether my content is resonating with the audiences I intended to reach or revealing opportunities I had not considered.

    I start by asking ChatGPT to create a regex based on the audience segments that matter most to the business.

    For example, I might prompt: Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.

    An example output is (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit).

    After applying the filter and exporting the results, I ask Claude or ChatGPT: Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.

    The differences can be valuable. Healthcare searches may consistently focus on compliance, while manufacturing queries may revolve around implementation. Retail searches may reveal entirely different use cases than financial services searches.

    7. I uncover striking-distance opportunities at scale

    Every SEO knows the classic advice: look at keywords ranking in positions 5-15 to identify opportunities within striking distance.

    The challenge is doing that at scale. A report with hundreds of queries where a site is close to stronger rankings can become overwhelming fast.

    I take the regex patterns above a step further. I apply the filters that match my goals, then narrow the report to positions 5-15 before exporting the queries.

    Then I ask my AI analyst: Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.

    Instead of getting tiny recommendations for individual keywords, I often uncover larger opportunities. A page may be missing subtopics, comparison details, stronger internal links, or use cases that would make it more complete.

    The result is usually fewer optimizations, but more meaningful ones.

    Turning Search Console data into decisions

    As an SEO, I do not have a data shortage. I have a prioritization problem.

    Google Search Console remains one of the richest sources of insight into how people discover a business. The difficult part is turning thousands of rows into something actionable.

    That is where AI fits into my workflow. It helps me uncover patterns, organize information, and surface opportunities I might otherwise miss. It is not a replacement for SEO strategy, experience, or critical thinking.

    The real advantage is not writing better regex or exporting cleaner spreadsheets. It is spending less time searching for insights and more time acting on them.

    Because data does not improve SEO. Better decisions do.

    Inspired by this post on Search Engine Land.

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  • How I Build SEO Strategies That Drive Real Revenue

    How I Build SEO Strategies That Drive Real Revenue

    I have watched the SEO industry become exceptionally strong at its technical craft. We have made real progress in crawl architecture, Core Web Vitals, content frameworks, entity optimization, and link acquisition at scale.

    Where I still see a gap is in how SEO connects that craft to the financial realities of the businesses it supports. Too often, SEO struggles to speak the language that gets budgets approved and strategies prioritized.

    If I want more funding and a stronger seat at the table, I have to change how I define what SEO is trying to achieve. That means moving beyond visibility alone and tying organic search to commercial outcomes.

    Here is how I make an SEO strategy more commercially aware.

    Why paid search often gets more funding

    Paid search usually frames its goals around clear commercial inputs and outputs. Money goes in, revenue comes out, and the difference helps determine whether investment should increase, decrease, or shift. Every campaign sits inside a financial framework.

    Even when paid search is expensive or inefficient, leadership can still see the goals, the numbers, and the tradeoffs. That makes resource decisions easier.

    SEO teams often present rankings as the final goal rather than a route to revenue. They report traffic without connecting it to transactions, or highlight technical improvements that matter to SEO but do not translate clearly into business value.

    When organic search does not get enough funding, it is easy to say leadership does not understand SEO. I think the more useful explanation is that SEO has not always made its commercial case clearly enough. Leadership needs to see organic search measured in sales, margins, and channel ROI.

    What commercial awareness requires

    Before I plan SEO work, I try to change the questions I ask.

    Instead of asking which topics have the highest search volume, I ask which categories and product lines carry the strongest margins. Then I evaluate search demand within those areas.

    Instead of asking where I should create new content, I ask which existing pages would generate meaningful revenue if they ranked better. From there, I work backward into the SEO plan.

    Instead of measuring success only in organic sessions, I measure it in organic profit. To do that, I need to know what the channel costs and what it returns.

    Financial metrics I use for commercial SEO

    When I run organic search as an acquisition channel, I pay close attention to these metrics:

    • Organic sales.
    • Organic revenue.
    • Organic profit.
    • Average order value from organic traffic.
    • Average margin per organic sale.
    • Channel ROI.

    These metrics are not exotic or especially difficult to calculate. They usually require connecting analytics data to backend transactional data, which most organizations can do with a modest investment in reporting infrastructure.

    One metric I keep returning to is organic profit per sale. I calculate it by dividing organic profit by organic sales.

    This turns organic search into a customer acquisition channel with a measurable cost per outcome. It also gives me a concrete benchmark I can compare against other channels.

    When I break that metric down by category, subcategory, and page, I can make strategic decisions using commercial data first, then layer SEO execution on top.

    Focus on value-side metrics

    Most SEO strategies lean heavily on demand-side metrics such as:

    • Search volume.
    • Keyword difficulty.
    • Current ranking positions.
    • Traffic estimates.

    I still need those inputs, but they only show half of the picture. They tell me where demand exists, not where value is strongest.

    To make better commercial decisions, I layer value-side metrics on top of demand data, including:

    • Categories with strong margins.
    • Pages that drive high transaction values.
    • Customer segments that stay profitable over time.

    From a revenue and profit perspective, a category with modest search volume can outperform a higher-traffic segment if it has stronger margins or a higher average order value.

    SEO tactics that move the commercial needle

    When I take a commercially aware approach, I evaluate strategic decisions against business outcomes rather than traffic projections alone. That includes decisions about informational content, authority building, and brand visibility.

    Informational content and topical authority still matter. A channel that only chases transactional queries will eventually hit a ceiling. The difference is that I want every major SEO initiative to have a clear commercial role.

    Score demand and business value together

    I apply a second filter that considers business value alongside search demand.

    That means I look at margin potential, average sale value by category, and current organic performance compared with where it needs to be. Then I weigh those signals against demand.

    The highest-priority work usually sits where meaningful demand and strong commercial signals overlap. In practice, that often produces a different priority list than traditional keyword research alone.

    Update commercial pages before creating more content

    Commercial pages naturally decay over time. Competitors improve their pages, SERPs change, and freshness signals fade. That decay can turn directly into lost revenue from pages that used to perform well.

    When I update commercial pages, I focus on a few practical moves:

    • I use keyword and competitor research to find content gaps.
    • I restructure information into formats that search engines and AI interfaces can easily extract, especially tables where they make sense.
    • I use a large language model to review first drafts and stress-test the content against competing pages.
    • I strengthen internal links to the pages that have revenue and margin potential.

    Increase internal linking

    Internal links from strong informational assets and high-authority pages to commercial pages can create direct business value when those destination pages have revenue and margin potential.

    Futuristic data archive with glowing server-like filing cabinets, stacked documents, and network lights symbolizing AI marketing data infrastructure.
    Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

    I spend significant time building internal links into commercial page clusters, especially when supporting content has authority but the connected commercial pages are underperforming in search.

    Borrow conversion intelligence from paid search

    SEO usually cannot see exactly which organic keywords drive conversions. I may have page-level conversion data, but the specific queries that create visits and purchases are often hidden.

    The best workaround I have found is to review recent PPC campaign data, usually from the last 30 to 90 days, and adjust for seasonality. This helps me identify keyword patterns that generate sales and high-value customers in paid search.

    I can then use those insights to prioritize organic landing pages, update commercial content, and decide where conversion optimization is most likely to pay off.

    Recover transactional terms just outside Page 1

    A valuable group of transactional keywords often sits in positions 10 through 20. These are commercial-intent terms where I am already in the conversation, but not yet visible enough to convert meaningful traffic.

    I identify these opportunities by filtering for commercial intent and business potential. Then I apply targeted improvements such as content updates, internal links, and relevant authority building.

    Build digital PR with commercial architecture

    Digital PR campaigns that exist only to acquire links rarely create meaningful commercial impact. I prefer to build a linking environment that supports the product categories I care about most.

    That means I structure campaigns around a few principles:

    • I focus on topics that are thematically relevant to important product categories.
    • I create an on-site asset that acts as the campaign destination and links back to relevant commercial pages.
    • I build the asset with internal links to the commercial page clusters it is designed to support.

    Treat branded search protection as a profit issue

    When affiliates rank for discount and voucher terms and capture that traffic, I may end up paying commission on customers who were already in the funnel and likely would have converted directly.

    The fix is straightforward. I improve on-site pages that target branded intent, strengthen internal signals, monitor branded click share, and enforce affiliate program terms around branded bidding.

    That can improve margins as well as revenue because it removes acquisition costs from conversions that should have been organic in the first place.

    Choose an attribution model

    Attribution is rarely clean. Organic sessions may appear as direct traffic, GA4 and backend systems may report different numbers, and multi-touch journeys can resist neat channel assignment.

    These problems are not unique to organic search. As AI-mediated search complicates referral paths further, attribution will become even harder.

    I choose an attribution model the organization can agree on, stay transparent about its limitations, and focus on growing the revenue attributed to organic search under that model.

    When leadership consistently sees organic search contributing meaningful and growing revenue, the finer attribution nuances become less important.

    Treat budget as a lever, not a constraint

    I view an SEO budget as a variable that can be adjusted based on commercial KPIs.

    The model is simple: SEO profit equals the business margin generated from organic search minus the cost of running the channel.

    When revenue growth is the priority, I can invest more aggressively in link acquisition, digital PR, and content production to expand visibility and capture incremental demand.

    When channel profitability matters more, especially during a business cycle where margin preservation is more important than top-line growth, I can reduce spending to improve short-term profit. I just need to be clear about the competitive risk of sustaining those reductions for too long.

    How I secure internal alignment

    Commercial SEO depends on cross-functional cooperation. To build alignment, I focus on the conversations that help other teams see SEO as part of the business growth engine.

    Speak the language of decision-makers

    Commercial and finance leaders care about growth, margins, and competitive position. I frame SEO in those terms, with revenue and margin projections tied to specific strategic initiatives.

    Generate proof before asking for major investment

    SEO takes time to show results, so I prefer to earn buy-in with a contained test before asking for a larger investment. That test might involve updating a group of commercial pages, completing a targeted internal linking project, or launching a branded search protection initiative.

    Use competitive visibility strategically

    I show leadership where competitors outrank us for high-value commercial terms, then quantify what that could mean in lost market share and revenue. Concrete numbers make the opportunity easier to understand.

    Build relationships that make execution faster

    When SEO is positioned as part of an integrated commercial growth engine, with shared data and coordinated prioritization, it becomes much easier to get work shipped. SEO touches paid search, content, product, and PR, so I treat those teams as allies rather than separate workstreams.

    Why commercial awareness should shape SEO strategy

    SEO has become technically sophisticated, but technical sophistication alone does not secure budget or influence priorities. I need to connect SEO work to the outcomes commercial leaders care about.

    I believe SEO should be held to the same standards of commercial accountability as other marketing investments. When that happens, organic search can become a cost-effective driver of growth and profitability.

    Commercial awareness does not require abandoning SEO fundamentals. It requires redefining success and having the discipline to organize strategy around revenue, profitability, and return on investment.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot
  • 5 Critical Questions I Ask Before Buying Any AI Tool

    5 Critical Questions I Ask Before Buying Any AI Tool

    AI now shows up in nearly every corner of marketing, and for every useful initiative I see, it feels like 10 vendors appear with a tool that claims to solve it.

    When this wave first started, I took more vendor calls and answered more outreach than I do now. Over time, I noticed I was asking the same core questions again and again to decide whether an AI tool was actually worth deploying.

    If I feel overwhelmed by AI vendor pitches, these are the five questions I use to separate useful solutions from noise. They help me understand what the tool does, whether it solves a real business problem, and whether the vendor is the kind of partner I would trust with my budget, data, and team’s time.

    1. What problem does your tool solve?

    I start here because I want to understand the purpose of the tool and, more importantly, whether the value it creates connects to real business outcomes.

    If a vendor cannot clearly explain the challenges or use cases the tool addresses, I assume it was not purpose-built for a real problem my team faces. That applies whether I am evaluating it from an in-house perspective or on behalf of an agency. I am cautious when vendors lead with feature-heavy language but cannot explain the business benefits those features are supposed to deliver.

    If a vendor can identify at least one existing team problem and explain how the tool improves business outcomes, I keep the conversation going. My next question is usually for a case study that shows how the tool was used and what results it delivered for an organization similar to mine in size, market, or vertical.

    I look for benefits such as increasing output or identifying tracking gaps that speed up troubleshooting. I do not rush to buy a tool simply because it promises to save time, even if that promise is true. I need to know how I will use that extra time before I can decide whether the savings are meaningful.

    2. What expertise do you have in the space where this tool solves a problem?

    This answer tells me whether the vendor built the tool for advertisers or merely at advertisers.

    Technical skill matters, but so does understanding how a media buyer actually spends the day. If the vendor does not have direct experience in media buying, I want to hear how the team researched the market and how those insights shaped the product.

    A shallow understanding of the problem is a red flag for me. I do not expect every sales rep to have deep domain expertise, but someone on the team should. If I am seriously considering the tool, I want access to that person early in the process.

    When a vendor has a credible story about identifying a problem I recognize firsthand and building a solution around it, I find that compelling. A founding mission tied to my actual challenges gives me more confidence that the tool can make a real difference in performance.

    3. What case studies, real use cases, and results can you share?

    In a fast-moving AI market, I treat case studies as essential. I want to know whether the vendor has a strong track record with customers like me or whether I would be one of the first teams testing the product in my space.

    If I would be an early adopter, I weigh the tradeoffs carefully. I might gain an advantage by finding a growth accelerator before competitors do. I might also spend time working through bugs, giving detailed feedback, or discovering that the tool does not deliver what was promised.

    If I cannot trust the tool, or if I will need to provide a lot of feedback just to make it useful, I have to decide whether the potential payoff is big enough to justify the time and money. In most cases, that bar should be high.

    Futuristic data archive with glowing server-like filing cabinets, stacked documents, and network lights symbolizing AI marketing data infrastructure.
    Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

    If I am clearly going to be an early adopter and the vendor will not offer flexible contract terms that reduce my risk, I consider that a nonstarter. Established tools may be less flexible on pricing because they can already prove consistent value. Newer tools that take a hard line on price and contract terms are much less likely to become strong long-term partners.

    For established vendors, I want specific and relevant case studies with real numbers from advertisers in a similar space, at a similar size, or with a similar use case.

    For early-stage companies, the best answer is honesty. If a vendor says, “You’d be one of our first clients in this vertical. Here’s what we’ve seen elsewhere, and here’s what that partnership would look like,” I see that transparency as a positive sign.

    4. Who owns my data, and how is it being used to train models?

    I am still surprised by how quickly people share data with AI tools in the rush to find a competitive edge. Before I sign anything, I take data ownership and model training terms seriously.

    I watch for any answer suggesting that my data could be used to train shared or third-party models without my explicit consent. I also treat vague answers, deflections, or terms of service that conflict with the salesperson’s verbal explanation as major warning signs.

    I own my data, full stop.

    The vendor should be able to clearly explain where my data is stored, how long it is retained, whether it is used for model training, and what happens to it if I stop using the tool. If model training is involved, I want that training limited to refining my own instance. Most importantly, I want those commitments in the contract, not just in a conversation. If the language is missing, I insist that it be added before I sign.

    5. What does implementation actually look like, and what does success require from our team?

    Before I commit budget, I need to understand the real cost of adopting the tool. That cost is not just the subscription price. It includes the time, internal lift, integration work, training, QA, and possible disruption to the existing martech stack.

    If the tool requires resources my team does not have, or if I cannot realistically dedicate the time needed to use it well, I do not consider it a smart investment yet. A lot of wasted martech spend could be avoided by asking this question and taking the answer seriously.

    I do not expect every tool to fit every organization, but I do expect implementation to be clear and the product to be intuitive enough for the team to adopt. If people cannot understand it, trust it, or fit it into their workflow, it will not create the value the vendor promised.

    I do not let AI hype rush my decision

    I know firsthand that many AI tools sound too good to be true, and often they are. I still want to stay curious and ambitious, but I balance that with caution.

    I also remind myself that AI adoption is still early. If a tool feels too expensive, too difficult to onboard, or too rigid in its contract terms compared with its track record, I am willing to wait. A better option may appear in the next few months.

    When I am unsure, I ask for a free trial. If integrating the tool will not create too much work for the team, a trial can be the best way to decide whether I have found a real competitive advantage or just another AI pitch dressed up as one.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot
  • AI Search Visibility: How Brands Get Used and Cited

    AI Search Visibility: How Brands Get Used and Cited

    I’m seeing traditional Google rankings deliver less predictable value than they once did. Ads, AI Overviews, and other search engine results page features are pushing organic links farther down the page, which means visibility no longer depends only on where a brand ranks in the classic blue-link results.

    As search keeps shifting, I believe brands need to ask a more practical question: how do I make sure my brand is represented accurately inside AI-powered answers?

    The more I understand how AI engines use brand information and when they cite it, the easier it becomes to build a real AI visibility strategy. This moves the conversation beyond whether an AI model “knows” a brand and toward how that brand can earn presence, trust, and discoverability in AI search.

    The click economy is shrinking

    I think most brands should start learning AI search and building an AI SEO strategy now. A full shift from organic search to AI search may still be years away, but the direction is clear enough that waiting creates risk.

    Google is already leaning hard into AI search. In an April article from The Verge, CEO Sundar Pichai said that search had a strong quarter, with AI experiences driving usage, queries reaching an all-time high, and revenue growing 19%.

    Users are changing their behavior too. A Pew Research study found that when people see an AI-powered summary in search results, they click a blue link only 8% of the time. When no AI summary appears, that click rate rises to 15%.

    AI search traffic may still be smaller than organic traffic, but I would not dismiss it. According to Similarweb, AI traffic converted at 11.4%, compared with 5.3% for organic search traffic. That makes AI visibility worth tracking even before it becomes the dominant traffic source.

    How I separate AI usage from AI citation

    I think about brand presence in AI systems in two main ways: usage and citation.

    Usage happens when an AI engine ingests information about a brand and draws on that information when answering a query. In some ways, this reminds me of how Google traditionally indexed pages before ranking and serving them in search results.

    When an AI engine uses brand content, it may mention the brand without linking to it. Even an unlinked mention can matter because it can create discovery, influence perception, and prompt users to search for the brand directly.

    Infographic summarizing Ahrefs study: 76.10% of AI Overview citations rank in Google top 10, 9.50% rank 11-100, and 14.40% do not rank.
    Ahrefs data shows most Google AI Overview citations still come from high-ranking organic pages, with 76.10% in the top 10 and a smaller share outside the top 100.

    Citation is different. A citation happens when an AI engine directly references a brand as a source of information. That reference might be a link to a web page, a social profile, or even a clickable phone link that lets someone contact the business.

    Within OpenAI, usage and citation appear to depend on separate technical systems. As OpenAI’s documentation explains, OAI-SearchBot and GPTBot are deployed separately among four distinct user agents. Other AI systems have their own controls, but the same broader distinction still applies.

    Why citations do not tell the whole story

    I do not see citations as the full AI visibility picture. AI engines often answer questions directly without citing web sources, and this pattern is not entirely new. Before AI Overviews, Google was already moving in that direction with featured snippets.

    Ahrefs found that ChatGPT retrieves almost the exact same number of cited and uncited URLs to generate an average response: about 16.57 cited URLs and 16.58 uncited URLs. But Reddit made up 67.8% of uncited URLs, which means comparing cited and uncited URLs is often really a comparison between search results and Reddit API output.

    That matters because AI systems are not neutral in the uncited information they surface. Some platforms and websites are simply more influential than others. If I try to push a brand into AI answers without understanding where the model gets its information, I am working at a disadvantage.

    How I would improve brand usage and citation

    I would start by tracking the brand’s current AI visibility and monitoring progress over time. That means running a representative set of prompts through an AI visibility platform, reviewing the sources that get cited, and asking what those sources reveal about the model’s preferences.

    There are already many AI citation tracking tools available, and established platforms like Semrush and Ahrefs have added AI tracking features as well. I would choose a tool based on the prompts, markets, and engines that matter most to the brand.

    I would also scale tracking and research as much as budget allows. AI prompt tracking often depends on API calls, so it can cost more than traditional rank tracking. Still, the data is usually richer, even when the sample size is smaller.

    As long as the prompt sample is broadly representative, most platforms can pull multiple responses and calculate an average. That gives me a more useful view of recurring patterns instead of relying on one-off answers.

    Neon Google search bar with microphone icon over a futuristic digital data background, representing search technology and SEO updates.
    A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

    I would keep reading studies from AI platforms, SEO vendors, and data providers too. Those reports are valuable because they show which sources AI engines rely on and where brands may have the best chance to appear.

    The key is continual monitoring. Over time, I can work to place a brand inside the sources AI engines already trust and use most heavily.

    Why I still care about traditional rankings

    Yes, I still think traditional search rankings matter, but not for the same reasons they used to. The relationship between organic position and business performance is less direct now, especially as SERP features and AI answers absorb more user attention.

    At the same time, Ahrefs research suggests a relationship between AI citations and Google rankings, at least inside Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic results. If AI Overviews become a dominant AI search experience, traditional rankings will still influence visibility.

    I also pay attention to content quality. Semrush found that AI engines rarely cite generic content that simply repeats what other sources already say. The content that earns citations usually contributes something distinct.

    That fits closely with Google’s helpful content guidance, which rewards original information and useful perspective. In my view, content with trusted data, original insight, and a clear point of view can support both Google rankings and AI citations.

    Because many classic SEO tactics can also support AI citations, I would not abandon traditional SEO. I would treat it as part of a broader visibility strategy that now includes AI usage, AI citations, and brand presence across trusted third-party sources.

    Where I think AI visibility is heading

    Both usage and citation need ongoing tracking and analysis. If I want AI engines to use a brand’s knowledge and content, I need to understand which sources each model relies on and help the brand appear in those places. If I want citations, I need the brand’s content to stay crawlable, rank well, and say something original.

    Classic SEO still earns its place because the same work that improves organic visibility can often improve AI visibility too. But returns from traditional rankings are changing, and AI SEO may eventually become the primary discipline. For now, I would keep ranking, start tracking, and build for both usage and citation.

    Inspired by this post on Search Engine Land.

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  • Why I Think Meta AI Is Search’s Sleeping Giant Now

    Why I Think Meta AI Is Search’s Sleeping Giant Now

    I do not think enough people are treating Meta AI as a serious AI search contender.

    In SEO circles, I hear plenty about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, RAG, and every new answer engine worth testing. Those conversations matter. But I think Meta AI already has something most AI companies would spend years and billions trying to build: massive distribution.

    By May 2025, Meta AI had reached one billion monthly active users across Meta’s apps, according to Mark Zuckerberg.

    Zuckerberg has also made the direction clear. He wants Meta AI to become a leading personal AI, shaped around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already being considered.

    That is why I think Meta AI is becoming one of the most important AI search contenders to watch.

    Meta’s Advantage Is Distribution

    I think the AI search debate spends too much time on model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is this just SEO with a new label?

    Those questions matter, but distribution matters more than the search industry often wants to admit.

    Meta reported 3.56 billion family daily active people across its apps in March. In that same quarter, revenue reached $56.31 billion, up 33% year over year.

    WhatsApp passed 3 billion monthly users in 2025. Instagram reached 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.

    I know Facebook is not the cool platform anymore. The metaverse stumbled. Threads can still feel like a corporate response to Elon Musk running, or ruining, the artist formerly known as Twitter.

    But none of that changes the important point. Meta can put AI inside the apps where people already spend their time. In doing that, it can bring search-like behavior directly into the places where discovery already happens.

    I think that could push public AI adoption faster than almost anything else in the market.

    The First Search Is The Search That Matters

    Google’s historic power has always rested on a simple habit. When people wanted to know something, compare options, buy a product, find a local business, or settle an argument, they started with Google.

    That starting point became the most valuable real estate on the internet.

    AI search changes where that starting point can live. If someone sees a product on Instagram, they do not have to leave the app and search Google. They can ask Meta AI whether the product is any good, what alternatives exist, whether the brand is trustworthy, or where they can buy it.

    If a WhatsApp group is planning a weekend away, they do not need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI can sit inside the conversation at the exact moment intent appears.

    If someone is scrolling through a Facebook thread full of local recommendations, they can ask Meta AI to summarize what people are saying across Groups, Reels, and public posts.

    That is not traditional SEO. I see it as search behavior being absorbed into social platforms.

    The strategic question is no longer only, “Who ranks?” I think the better question is, “Where does the question begin?”

    Meta AI Is More Than Another Chatbot

    I think search marketers often approach AI through too narrow a lens. We find the chatbot, test a few brand queries, check which sources get cited, and decide we understand the platform.

    That is a mistake.

    Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. Meta says it is available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. The use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.

    The standalone Meta AI app, launched in 2025, was designed around a more personal AI experience. Meta says it can use information people have chosen to share across Meta products, along with profile data and content engagement, to deliver more relevant answers in supported markets.

    I can see where this is heading. Meta AI could become the free AI tool that everyday consumers use without thinking much about it.

    How Meta AI Could Become Consumer AI

    ChatGPT and Claude still feel like work tools to me. They are excellent tools, but they are tools people deliberately open because they have decided to do something.

    Meta AI feels more like consumer AI. It is messier, more visual, more embedded, and less like launching a productivity suite. It feels more like finding an answer while doing what you were already doing.

    For many people outside tech, opening ChatGPT still feels like an intentional act. Asking a question inside WhatsApp or Instagram can feel almost frictionless.

    That is Meta’s advantage. It does not have to convince people to adopt AI from scratch. It can fold AI into existing habits.

    This is where it gets interesting. Meta AI is also a playground, and Meta gets to watch how people actually use it.

    I can imagine a 65-year-old grandmother using it to animate family photos and share them in a WhatsApp group.

    I can imagine a dog groomer using it to create short videos of clients’ pets and post them on Instagram.

    When AI becomes mainstream and easy to use, people will use it where they can reach other people. That gives Meta a powerful feedback loop. The more people play with Meta AI, the more Meta learns, improves, and adds features that fit real consumer behavior.

    AI Becomes Social, Visual, And Shoppable

    Then there is Meta AI Studio.

    Users can create AI characters built around their interests, work from templates, or start from scratch. They can build assistants for advice, captions, entertainment, and creator interactions.

    Futuristic SEO and AI search illustration showing old tools breaking apart as blue data streams lead to a glowing search platform and digital icons.
    Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

    Then there is Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, Facebook Stories, and Reels.

    I will be honest: parts of this feel strange. Generative AI video on social platforms is a messy mix of creativity, novelty, nonsense, and low-quality output. But early weirdness is not the same as strategic irrelevance.

    I never expected AI to arrive as one perfect super-app that everyone understood immediately. Meta is putting new formats into users’ hands, watching what people do with them, and reshaping the product around that behavior.

    The Ad Machine Makes This A Google Problem

    Forecasts suggest Meta will reach $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google’s 26.4%.

    I think those numbers should get Google’s attention.

    If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value collects around the platform that owns the query. That platform does not always have to be the one with the best model.

    Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.

    Google still has search intent, which is enormously powerful. But Meta has attention, habit, and context. Google is where people go when they have decided to search. Meta is where many people already are.

    Why “It’s Just SEO” Misses The Point

    The AI optimization debate keeps collapsing into the same comforting line: it is just SEO.

    Sometimes, I agree. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.

    But I think the harder question is this: how exactly do you optimize for Meta AI?

    Facebook AI Mode makes the challenge obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta’s apps, rather than a traditional list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives instead of standard search results.

    That is a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook is not enough.

    Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. I think the point is clear: visibility is getting more complicated.

    Nobody can honestly say they know exactly how all of this works yet. Anyone who claims total certainty is probably selling a dashboard and a dream.

    The honest answer is frustrating: I do not think we know enough yet. But that is not a reason to ignore Meta AI.

    Google Is Being Attacked From Every Angle

    Google is still Google. I do not want to overstate the case. It remains central to search, commerce, publishing, advertising, and the open web.

    But Google is being pushed from every direction at once. ChatGPT is pressuring answers. Perplexity is pressuring research. Amazon is pressuring product search. TikTok and Instagram are pressuring discovery. Regulators are pressuring market power. Publishers are challenging AI content extraction. Meta is pressuring attention, ads, and AI-assisted discovery.

    In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.

    I think this matters because AI search is not just another product feature. It changes the value exchange between users, publishers, platforms, and advertisers. While Google works through that challenge, Meta is quietly building AI into social behavior.

    What I Think Brands And SEOs Should Do Now

    I would not panic. Panic is rarely a strategy, even if it shows up in plenty of marketing meetings. But I would start testing now.

    I would run brand, category, product, local, and comparison queries in Meta AI. I would test Facebook, Instagram, WhatsApp, and the standalone app wherever possible, then compare the results with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.

    I would track whether my brand appears, whether answers cite or link to me, and whether public Meta content seems to shape responses. I would look closely at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language.

    If discovery moves into Meta’s AI layer, I want to understand what my brand needs in order to be visible there.

    That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, better community management, more consistent entity signals, or paid social tests designed around AI visibility. It might also mean none of those things yet.

    Either way, I would rather have data than keep repeating “it’s just SEO” while the market reorganizes itself.

    The Sleeping Giant

    I do not think Meta AI has to beat Google at Google’s own version of search. It does not need to.

    It only needs to absorb enough search behavior into the places where people already spend their time.

    It needs to become the casual AI layer for people who may never deliberately open ChatGPT.

    It needs to make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.

    That is a serious threat. Meta AI may feel clunky right now, but so did much of the early web.

    I think the search industry should stop asking whether Meta AI looks like search. The better question is whether users care.

    If people start asking Meta before they ask Google, the game changes. That is how sleeping giants wake up.

    Inspired by this post on Search Engine Land.

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  • How AI Is Reshaping Search Demand Across 1M Keywords

    How AI Is Reshaping Search Demand Across 1M Keywords

    I do not see search demand disappearing. I see it moving. In this analysis, 29% of high-volume search demand is declining, while nearly the same amount is growing somewhere else. Overall demand is essentially flat because people are redistributing how and where they search instead of abandoning search altogether.

    That changes how I think about SEO strategy. I would not start by panicking over shrinking keywords. I would start by identifying which queries are losing volume, which ones are gaining momentum, and where a brand can build enough authority to appear in both traditional search results and AI-generated answers.

    This study looks at where search demand is shifting, which industries are seeing the sharpest changes, and what those patterns mean for SEO teams trying to adapt to AI-driven discovery.

    In 2024, Gartner predicted that traditional search engine volume would fall 25% by 2026 as consumers shifted to AI chatbots and virtual agents. Fractl and Search Engine Land set out to test that prediction. (Disclosure: I’m the co-founder of Fractl.)

    I worked from Semrush data covering 1,010,848 high-volume keywords, each with at least 10,000 monthly searches, across 379 brands in eight verticals. I also looked at survey responses from 1,004 U.S. consumers to better understand how AI is changing the way people search.

    Image

    The analysis measured which keywords gained or lost search volume over the past year, how those shifts differed by industry, and how consumer behavior is evolving as AI tools become part of everyday discovery.

    The 29% search decline is real, but it depends on the vertical

    Across more than 1 million high-volume keywords, I found that 29% of search volume is in measurable decline. That is 4 percentage points above Gartner’s forecast. In a dataset representing 35.4 billion monthly searches, that difference represents a meaningful amount of search activity.

    The impact is not evenly distributed. FinTech showed the largest decline at -37.7%, while Lifestyle saw the smallest decline at -15.2%. Only three of the eight verticals, Insurance, SaaS, and Lifestyle, came in below Gartner’s 25% threshold. FinTech, HealthTech, and Wellness were well above it.

    The pattern makes sense when I look at how information-heavy each category is. When a chatbot can answer the question completely, such as summarizing drug interactions, explaining deductibles, or giving a quick overview of a fund, search volume is more likely to fall. When people need to compare prices, complete a transaction, or navigate to a specific site, search demand tends to hold up better.

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    That is why transactional verticals such as SaaS, Lifestyle, Insurance, and Travel are growing or staying close to flat. Information-heavy verticals such as HealthTech, FinTech, and Wellness are seeing the largest declines.

    Before reacting to broad claims about AI-driven search decline, I would benchmark these findings against the specific vertical in question. HealthTech and FinTech teams should expect more exposure than the overall 29% decline suggests. SaaS and Lifestyle teams have more reason to challenge the idea that search demand is simply collapsing.

    Search demand is being redistributed

    The headline number gets attention, but the offset is just as important. Demand did not vanish. It moved to a different set of words, and those are the terms I would want to understand first.

    Among the high-volume keywords tracked, 40.7% are in measurable decline, meaning they lost more than 15% of their volume over the past year. Within that group, the average decline is -41%, and 112,378 keywords lost more than 40% of their volume. For brands that depend on those terms, the impact is significant.

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    At the same time, 20.1% of keywords are growing by more than 15%. When I add up the volume on both sides, the decline and growth almost cancel each other out.

    The 285,489 declining keywords represent roughly 10.29 billion monthly searches. The 140,835 growing keywords represent roughly 10.31 billion monthly searches. Across the entire dataset, the net change is +16.8 million searches per month.

    Fewer keywords are growing than declining, but the growing keywords carry more volume each. That is why the totals balance out. In practical terms, I see demand relocating more than shrinking.

    The vertical-level growth-to-decline ratios show where that new demand is landing. Lifestyle leads at 2.6x, with 40% of keywords growing versus 15% declining. SaaS follows closely at 2.5x, with 48% growing versus 19% declining. HealthTech sits at the other end with an inverted ratio of 0.4x, making it the most disrupted vertical in the set.

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    The first audit I would run is simple: pull the tracked keyword set, filter it by year-over-year volume change, and see which side of the ledger the portfolio sits on.

    Non-branded queries are the most vulnerable

    I see non-branded queries as the easiest ones for AI chatbots to replace. When a search term does not include a brand name, the user is not necessarily trying to reach a specific site or source. The full exchange can happen inside the chat window.

    Across the dataset, 90% of all tracked search volume is non-branded. HealthTech, at 99.6%, and Wellness, at 98.5%, are the most exposed. Insurance, at 73.8%, and SaaS, at 82.0%, are less exposed, and both are growing overall. SaaS volume is up 48% year over year, while Lifestyle is up 40%.

    If I wanted to identify the content most at risk, I would start with keyword patterns. They offer one of the clearest signals in the study.

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    The reason SaaS and Lifestyle can be heavily touched by AI and still grow comes down to what happens after the AI answer. If AI recommends a project management platform or a couch, many people still search for the specific brand, retailer, review, or product page before buying. The AI answer creates a downstream search.

    HealthTech and FinTech often behave differently. A drug-interaction question or a “what is a deductible” query can be answered completely inside the chat window. There may be no next step that sends the user back to Google.

    If a category produces complete AI answers with no natural next click, I would treat AI visibility as a core strategy, not an SEO side project. In those cases, showing up in the answer may be the entire opportunity.

    70% of consumers use AI more, but only 17% use search less

    The keyword data shows what is happening in the index. The survey data shows what is happening in the minds of the people doing the searching.

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    Search behavior is spreading across more platforms. Many people are adding AI to their routines without giving up Google.

    Social platforms are also acting like search engines in a way they did not a few years ago. YouTube leads at 68%, followed by Reddit at 57%, Instagram at 42%, Facebook at 40%, and TikTok at 33%.

    If I had not already prioritized YouTube and Reddit, I would move them up the list. Both rank ahead of TikTok, Instagram, and Facebook as search destinations, and both can also surface in Google results, which gives visibility there a compounding effect.

    What has actually moved from Google to AI

    More than a third of respondents, 35%, say they have not replaced traditional search with AI for anything yet. Among those who have, how-to guides and tutorials have taken the biggest hit.

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    For purchase research, 47% of consumers start with a traditional search engine, tied with online retailers at 47%. Only 13% start with an AI chatbot, and shoppers check an average of three online sources before making a purchase.

    The number I would bring to a strategy meeting is this: nearly one in five consumers, 18%, have bought something based on an AI recommendation without checking it against a separate search.

    That creates a different kind of buyer journey. In that path, the brand may never receive a search-driven touchpoint. To be considered, the brand has to be one of the names the chatbot returns.

    Gen Z and millennials are 2.5x more likely than baby boomers to buy based on an unverified AI recommendation, at 20% versus 7%. Across all consumers, 59% say they are likely to visit a brand’s website after an AI chatbot mentions or recommends it.

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    That is the emerging conversion funnel I am watching closely. Brand mentions in AI answers are starting to function like rankings. Visits to a brand’s website after an AI mention are starting to look like the new click-throughs.

    Trust is still mixed. In the survey, 33% of consumers trust AI and traditional search equally, 46% trust search more, and 20% trust AI more.

    More than half of consumers, 56%, are at least somewhat skeptical of AI product recommendations. I read that as a sign that people are willing to let AI filter and shortlist options, but many still want to verify before they buy.

    The 5-year outlook: Google is not going away, but the shift matters

    When asked whether Google will still be their primary search tool in five years, 52% of consumers say yes, including 17% who say definitely and 35% who say probably. Another 27% are unsure, while 20% say probably or definitely not.

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    The top reasons people prefer AI over traditional search are better summaries across sources, at 21%; faster and more direct answers, at 20%; and the ability to ask conversational follow-up questions, at 19%. More personalized results and avoiding website click-throughs were much lower, at 6% and 4%.

    When asked what would bring them back to traditional search, the top answer was AI giving unreliable answers, at 35%. That means much of this shift depends on whether AI maintains trust as adoption scales. More accurate search results followed at 29%, then a preference for multiple source links at 22%, and privacy concerns at 20%.

    The 20% who expect to leave Google are not the majority, but I would not dismiss them. A strategy does not need to be rebuilt entirely around them today, but brands do need to appear where those users are already moving.

    What this means for content and SEO strategy

    I see Gartner’s 25% prediction as a useful directional warning. The real shift may be steeper, but calling it only a decline misses the more important story. Total search volume is basically flat. What has changed is which searches carry the demand.

    AI visibility is not just a threat to manage. I see it as a distribution channel. With 59% of consumers saying they are likely to visit a brand’s website after an AI mention, GEO has become a meaningful part of brand discovery.

    Earned media, credible third-party coverage, and strong entity signals all help brands appear in chatbot answers. That is why digital PR and GEO are becoming more closely connected.

    Search is moving, not disappearing.

    The brands that lose will be the ones still optimizing mainly for queries that AI now answers better. The brands that win will be the ones building enough authority to become the answer, whether that answer appears in Google or inside a chatbot.

    Methodology

    This study combined two data sources to test Gartner’s 2024 prediction that traditional search engine volume would fall 25% by 2026.

    Fractl and Search Engine Land analyzed Semrush search volume data for 1,010,848 high-volume keywords with 10,000 or more monthly searches each, covering 379 brands across eight verticals: FinTech, HealthTech, Wellness, Travel, Education, Insurance, SaaS, and Lifestyle. The dataset represented 35.4 billion in aggregate monthly search volume. Keyword-level year-over-year volume change was measured as of April 2026 and classified as declining, meaning more than 15% loss; stable, meaning within 15%; or growing, meaning more than 15% gain. Query pattern groupings, including “What is X,” “Best X for Y,” “X vs. Y,” and “How to X,” were applied at the keyword level.

    Fractl and Search Engine Land also surveyed 1,004 U.S. consumers about their search habits, AI tool adoption, and purchase research behavior. The sample was 52% women, 46% men, and 1% nonbinary, with 49% millennials, 26% Gen X, 16% Gen Z, and 9% boomers. The median respondent age was 41, with a range of 18 to 82.

    Inspired by this post on Search Engine Land.

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  • My 120-Minute Weekly SEO Workflow That Drives Results

    My 120-Minute Weekly SEO Workflow That Drives Results

    When one person is responsible for paid campaigns, landing pages, reporting, email, social posts, sales requests, and last-minute website updates, I know exactly what usually happens to SEO: it waits.

    I have seen this play out on small marketing teams over and over. Everyone knows SEO can bring in qualified demand, reduce dependence on paid media, and support buyers long before they fill out a form. The problem is that SEO rarely feels urgent until traffic drops, rankings slide, or something breaks.

    That is why I like a simple 120-minute weekly SEO workflow. It gives me a practical way to protect visibility, find opportunities, improve high-value pages, and turn search data into business impact without pretending I have unlimited time.

    Why I keep SEO simple on lean teams

    When SEO falls behind, I rarely see effort as the real problem. The bigger issue is usually competing priorities and a lack of clear prioritization.

    On a lean team, SEO is one tab among 20. The person responsible for organic growth may also be sending newsletters, briefing designers, updating landing pages, and pulling the report leadership wants by Friday.

    Then the advice starts piling up: fix technical issues, publish more, build topical authority, refresh old posts, add schema, improve Core Web Vitals, build links, optimize for AI search, and keep going. Most of that advice may be valid, but no small team can do all of it in one week.

    The question I come back to is not, “What could I do?” It is, “What is the highest-leverage thing I can actually finish this week?”

    I also try to avoid the reporting trap. It is easy to spend an entire SEO block looking at rankings, traffic, impressions, clicks, CTR, conversions, competitor movement, and keyword shifts. Then the hour ends and nothing ships.

    For a small team, reporting has to be short enough to leave room for action. The goal is to decide what to fix next, not to build another dashboard.

    Why 120 minutes can be enough

    I do not try to run a lean team like an enterprise SEO department. If I audit everything, track everything, collect endless keywords, and ship nothing, I have not improved organic growth.

    The point of time-boxing is to force a decision. Every weekly session should end with one or two changes that improve visibility, traffic quality, or conversion potential.

    In my 120-minute workflow, I focus on four outcomes: finding what is already working, fixing what is blocking performance, improving the pages closest to revenue, and turning search data into next week’s actions.

    I am not trying to “do SEO” for two hours. I am using two focused hours to make decisions and ship work that has a realistic chance of moving the business forward.

    My 120-minute weekly SEO workflow

    0-15 minutes: Check organic data

    I start with a pulse check so I can catch problems before they turn into bigger performance drops.

    I look at Google Search Console clicks, impressions, CTR, and average position. I also check organic conversions or assisted conversions in GA4, top landing pages gaining or losing traffic, branded versus non-branded movement, and any indexing, crawling, or manual action warnings.

    What I do not do is turn this into a full reporting session. This is not a board deck. I only want to answer one question: is organic visibility moving in a direction that needs action?

    My output is a short weekly note: the biggest organic win, the biggest organic concern, one page or query to investigate, and one action to take this week.

    15-35 minutes: Find query opportunities

    Next, I look for the easiest opportunities in Google Search Console. The richest ones are often queries ranking in positions 4-15 with real impressions. Those pages are already close, and a focused improvement can help them move.

    I also watch for pages with strong impressions but weak CTR, queries climbing week over week, and rankings where the current page only partially matches search intent.

    I resist the urge to build a long keyword list. Instead, I pick three things: one page to improve, one query to answer better, and one title or meta description to test.

    For example, when I reviewed search data for a local accounting client, several queries kept appearing around tax help for freelancers, small-business tax mistakes, and the difference between an accountant and a bookkeeper.

    The obvious reaction would have been to write three new articles. Instead, I rewrote one service page around freelancers, added a short FAQ based on those queries, and linked it to an existing bookkeeping article. One page served three search intents, which was far more useful than three unfinished drafts.

    35-60 minutes: Improve one money page

    This is the most important part of the workflow. I define a money page as any page close to revenue, pipeline, bookings, sales, demos, or consultations.

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    Money pages can include product pages, service pages, category pages, comparison pages, demo pages, consultation pages, pricing pages, and high-intent landing pages.

    My weekly goal is not to optimize the entire website. It is to improve one important page in one meaningful way.

    I ask what the buyer needs to believe before converting, what objection is missing, what proof would reduce hesitation, what comparison the buyer already has in mind, and what query the page almost satisfies but does not fully answer.

    A meaningful update might be adding three FAQs based on real queries, improving the H1 and introduction, adding comparison language, including proof points, linking to a case study, clarifying who the offer is for, improving the CTA, or adding a short “how it works” section.

    That is SEO work, but it is also conversion work. The best page improvements usually help both search engines and buyers understand the value faster.

    60-80 minutes: Fix one technical or indexing issue

    Technical SEO can take over the full two hours if I let it, so I stay focused on impact.

    The question I ask is simple: what could stop an important page from being discovered, understood, indexed, or trusted?

    That usually points me toward issues like priority pages not being indexed, broken internal links, redirect chains, duplicate or missing titles on key pages, incorrect canonicals, schema errors on important templates, or valuable pages buried too deep in the site.

    I want one of three outcomes from this block: a fix shipped, an issue assigned, or a clear developer brief.

    For example, if I find that ecommerce collection pages are not indexed because of incorrect canonical tags, documenting the affected URLs and writing a clear developer brief may be more valuable than publishing another generic article.

    80-100 minutes: Improve internal links

    Internal linking is one of the fastest SEO wins I can create because it does not require new content.

    It helps search engines understand which pages matter, helps users continue their journey, and helps informational content support commercial outcomes.

    Each week, I look for links from high-traffic articles to money pages, links from product or service pages to supporting guides, links from older articles to newer strategic content, and opportunities to use clearer anchor text.

    If an article ranks for “how to choose accounting software,” I do not want it to be a dead end. I want it to guide readers toward a comparison guide, a relevant case study, and a demo or pricing page. The traffic is already there, so I try to make it more useful.

    100-115 minutes: Turn one search insight into messaging

    I do not want search data to stay trapped in an SEO silo. The best query I find each week is often a useful signal for the rest of marketing because it shows the language buyers actually use.

    A query like “best CRM for small agencies” can become a comparison section on a landing page, a LinkedIn post, a sales email angle, and a paid search ad group.

    A query like “is [product] worth it” can become a proof section, a pricing explainer, a “who this is not for” paragraph, or a ready-made answer to a sales objection.

    When I share one search insight each week, SEO becomes more than a channel. It becomes a source of customer intelligence.

    115-120 minutes: Choose next week’s priority

    I end with a decision, not a long list. I choose one clear priority for next week based on business impact, search demand, ease of execution, current performance gap, and proximity to revenue.

    The template I use is: “Next week, my highest-leverage SEO action is [X] because [Y].”

    For example: “Next week, my highest-leverage SEO action is updating the pricing page because it gets non-branded traffic, supports demo requests, and does not answer implementation cost questions.”

    That is how I make SEO operational. The work becomes specific, owned, and easier to repeat.

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    A sample month for the workflow

    To keep the workflow balanced, I like rotating the emphasis each week.

    In week one, I focus on a revenue page. I update copy, add FAQs, improve internal links, check indexing and schema, and sharpen the CTA.

    In week two, I refresh existing content. I choose one article with impressions but weak clicks or rankings, improve the title, add missing sections, update examples, link to money pages, and better match search intent.

    In week three, I handle technical cleanup. I focus on one crawl, indexing, or template issue, such as broken links, duplicate titles, sitemap problems, or a developer brief for a higher-impact fix.

    In week four, I turn SEO data into broader marketing assets. That may mean one landing page insight, one sales objection, one content brief, one paid or social angle, or one FAQ or comparison section.

    This rotation keeps me from spending every week in dashboards, technical audits, or new content production while ignoring the pages that already have potential.

    What I stop doing

    Most small teams do not have a doing problem. They have a stopping problem.

    I stop chasing every low-impact technical warning. I stop creating content just because a tool found a keyword. I stop publishing AI-assisted articles at scale without a strategy. I stop rewriting pages without a hypothesis. I stop optimizing low-value pages before revenue pages. And I stop treating rankings as the only score that matters.

    Before I create new content, I review the pages I already have. The highest returns often come from pages that already rank on Page 2, already get impressions, sit close to revenue, and are one focused update away from doing more.

    My test for any task is simple: if I cannot connect it to qualified traffic, conversions, discoverability, buyer education, or trust, it does not belong in the 120 minutes.

    How I make it work without a dedicated SEO person

    This workflow does not require a full SEO department. It requires one owner, a weekly rhythm, and a bias toward shipping.

    A marketing manager can own prioritization and the weekly SEO note. A content marketer can update copy, FAQs, and page sections. A developer or web support partner can handle technical fixes. A paid search manager can share query and conversion insights. A founder or sales team can contribute objections and buyer language.

    The owner matters most. Someone has to protect the 120 minutes, choose the priority, and make sure the session ends with an action.

    Without ownership, SEO becomes everyone’s job and nobody’s job.

    How I use AI to save time

    I use AI to shorten repetitive SEO work, not to hand over strategy.

    That might mean using a focused workflow to identify queries in positions 4-15, pages with high impressions and low CTR, search queries that should become FAQs, internal linking opportunities, or technical issues that should become developer briefs.

    For agencies, client-specific assistants can reduce context switching by remembering each client’s services, priority pages, competitors, and customer objections.

    The most useful AI workflows are narrow: a GSC opportunity analyzer, a money page refresh assistant, an internal linking assistant, a technical SEO brief generator, or an SEO reporting summarizer.

    I do not want one generic SEO assistant trying to do everything. I want small workflows that help me move faster from data to decisions.

    Consistency is the advantage

    Small teams win SEO by doing the highest-leverage things repeatedly.

    A 120-minute weekly SEO workflow will not replace a full strategy. It will not solve every technical issue, build every content asset, or uncover every opportunity.

    But it gives me a practical way to protect visibility, learn from search data, improve revenue pages, and keep organic growth moving.

    The mindset is simple: less auditing, more shipping, more buyer intent, less busywork, and more business impact.

    Inspired by this post on Search Engine Land.

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  • How I Use Google Query Expansion to Boost Visibility

    How I Use Google Query Expansion to Boost Visibility

    LLMs have changed how people search and how Google responds. The SERP has not been limited to 10 blue links for a long time, but traditional search has usually centered on one core intent: the thing someone is trying to find.

    Now, AI Overviews can create a full answer directly in the SERP. They do more than respond to the original query. They also bring in related terms, contextual refinements, and supporting information that help searchers make better decisions.

    That is why I pay close attention to Google query expansion. When I understand how Google connects related searches, I can find visibility opportunities that competitors may miss.

    What is Google query expansion?

    I think of Google query expansion as Google broadening a searcher’s query so it can return more accurate results, especially for long-tail searches that might otherwise produce weak or limited results.

    This can happen through synonyms. For example, Google may connect “budget” with “affordable” when the intent is similar.

    It can also happen through intent expansion. Google may understand what my audience means even when they do not type the exact words I expected.

    Related topic expansion matters too. Google can use similar searches and connected topics to surface content that supports the searcher’s broader need.

    I do not use this as an excuse to stuff keywords into a page. Instead, I use query expansion as a research signal. When I see related searches that make sense, I can add useful supporting information and help my content rank for a wider range of relevant queries.

    Here is a simple example. If I have an article about backyard chicken care and someone searches “What’s the average lifespan of a chicken?”, my page might appear even if I never used the word “lifespan.”

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    In that case, Google has decided the article is semantically relevant. Once I know Google has made that connection, I can add a helpful section about chicken lifespan. That gives the page a stronger chance to rank for the term and attract more traffic.

    It can also improve the odds that my content appears in relevant AI Overviews.

    The difference between Google query expansion and query fan-outs

    Google query expansion and query fan-outs are related, but I do not treat them as the same thing.

    Query expansion is part of traditional search. Google broadens a query with synonyms, related terms, and intent signals before results are generated. Because of that, my content can rank for searches I did not directly target.

    Query fan-outs are part of AI Mode. They break a query into multiple related subqueries while the AI response is being generated. Because of that, my content can be retrieved as a source for an AI-generated answer.

    So why does traditional query expansion still matter in a search world shaped by LLMs and AI Overviews?

    Because the same semantic relationships that help Google expand a query can also influence which content AI systems retrieve during query fan-outs.

    How I find query expansion opportunities

    The first place I look is Google Search Console. It is one of the clearest ways to confirm whether query expansion is already happening for my site and my content.

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    My workflow is straightforward. I go to Performance > Search results, filter by a specific page, pull the full query list, and sort by impressions.

    From there, I look for queries I never intentionally targeted. I pay attention to synonyms with meaningful impressions, question-based searches that may be especially useful for AI visibility, and broader keywords that are not currently addressed on the page.

    I do not assume every discovered query deserves a content update. Sometimes a page appears for terms that are not truly relevant. When that happens, I audit the page and make sure the content is not drifting into unrelated topics that fail to match the promise of the SERP result.

    How I plan better content with query expansion

    Once I understand which expanded queries Google is connecting to my content, I use that data to strengthen the page instead of chasing isolated keywords.

    I write for topic coverage

    For a long time, strong SEO has been less about exact keywords and more about semantic relevance. I try to build coverage around subtopics, related questions, and adjacent ideas because that gives Google more context than a page built around one keyword alone.

    I answer questions adjacent to the main topic

    For example, if I am working on content for a company that sells chicken feed, I would not only explain the feed itself. I would also consider why the right balance matters and how the right feed can support chicken health.

    I can find those adjacent questions by reviewing query expansion data in Google Search Console, checking tools like Ahrefs, and studying the SERP to see what supporting information Google is already surfacing for the topic.

    I use expansion data to find content gaps

    If Google Search Console shows that Google is pulling my page for a query I have not planned for, and that query is genuinely relevant, I treat it as a signal that the page may need more complete coverage.

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    Sometimes query expansion data includes odd or unrelated searches. I ignore those. But when I find adjacent queries that clearly strengthen the topic, I add them to the page in a useful and natural way.

    I also revisit content regularly, usually at least once a quarter. New queries can appear, while others fade away. Since I am already keeping content fresh for the SERP, query expansion gives me another practical way to make each topic stronger.

    How I use query expansion to improve AI Overviews visibility

    AI Overviews often pull from ranking pages on a topic to build a more complete answer. Those answers can include semantic connections and supporting subtopics, not just the exact phrase someone searched.

    That is why I cross-reference my query expansion data with the main keyword in the SERP. If an AI Overview includes supporting topics that are relevant to my page, I consider adding those topics to the content.

    For example, I followed this process for a blog post titled “Tandem vs. Spread Axles in Trucking.” After filtering by impressions, I found that the page appeared for “tandem truck meaning,” even though that exact phrase was not specifically included in the content.

    The page ranked first, but it was not included in the AI Overview for that specific query. That told me there was an opportunity.

    Because the page already ranked well, I could use the expanded query and the supporting information in the SERP to create a section that better addressed both the query expansion term and the query fan-out patterns behind the AI Overview.

    That is the value of this process. Query expansions can reveal supporting topics that strengthen traditional search visibility and improve the chances of being included in AI-driven results.

    How query expansion helps my SEO strategy evolve

    I use query expansion as a practical way to identify supporting topics and expand content coverage across search experiences.

    As clicks become harder to earn, I want my content to appear across more relevant search moments. Broader visibility can strengthen brand awareness, support AI visibility, and keep my content in front of the people most likely to need it.

    Inspired by this post on Search Engine Land.

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  • AI and SEO Explained: What Marketers Need to Know Now

    AI and SEO Explained: What Marketers Need to Know Now

    If it feels like the whole internet woke up and decided every sentence needed to start with “AI,” I get it. I feel that fatigue too.

    As marketers, we are getting hit every day with LinkedIn hot takes, rushed prompt hacks, and promises that ChatGPT will either 10x our productivity or replace us completely.

    And right in the middle of all of that is the digital marketer trying to figure out whether AI is just another buzzword cycle or the start of a major rewrite of how we handle content, SEO, PPC, reporting, and almost everything else.

    So I want to break it down in plain English.

    Think of this as my AI starting guide for marketers who are tired of needing someone younger to translate every new acronym, the same way many of us once had to help our parents get online or open an AOL chat window.

    Defining AI and LLMs, and why they matter

    I am not asking “what is AI” just to chase keyword density. I want to start with a shared definition, because a lot of these terms get used interchangeably, and not always correctly.

    At its core, artificial intelligence refers to machines performing tasks that usually require human intelligence, such as understanding language, recognizing patterns, making decisions, and generating content.

    The kind of AI getting the most attention right now is generative AI: models that can create text, images, code, video, and other outputs based on patterns learned from huge datasets.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    Tools like ChatGPT, Gemini, and Claude do not “think” the way people do. They predict the next most likely word, phrase, or response based on what they have been trained on.

    That matters because AI is not a magic shortcut to instant wealth, overnight automation, or effortless headcount reduction. I see it more as large-scale data aggregation and pattern recognition.

    Large language models, or LLMs, are not creating net-new truth from nothing. They process massive amounts of existing information and produce answers based on patterns, probabilities, and what looks like internet consensus.

    For content creators and marketers, that is a major shift. I am no longer thinking only about optimizing for a traditional search engine click. I also have to think about whether machines can understand, summarize, cite, and reuse my content.

    The biggest implication is the rise of zero-click search. AI systems can answer users directly through experiences like Google AI Overviews or ChatGPT responses, often without sending that user to the original website.

    That changes SEO from a pure traffic game into an authority, visibility, and data-ingestion game.

    That is why I think marketers need to understand what AI does well, what it struggles with, and where it actually belongs in a broader marketing strategy.

    ```json { "alt": "Illustration listing types of AI, definitions, and examples such as Siri for AI and Netflix recommendations for Machine Learning.", "caption": "Exploring the diverse world of AI: From basic machine tasks to advanced language processing, discover how AI is shaping our digital age.", "description": "This image features an illustration detailing various types of AI, including Artificial Intelligence, Machine Learning, Natural Language Processing, Generative AI, and AI Agents. Each type is defined with associated examples like Siri for AI, Netflix recommendations for Machine Learning, and Grammarly for NLP. The graphic is designed with a retro color palette, featuring an illustration of a woman and a structured table layout for clarity, providing both educational content and visual appeal." } ```

    AI jargon I think marketers need to know

    Before going deeper, I want to separate a few terms that often get mashed together: AI, machine learning, NLP, generative AI, LLMs, and AI agents. They are related, but they are not the same thing.

    Understanding the difference helps me make better decisions about which tools to use, where to trust them, and where human judgment still matters most.

    Artificial intelligence (AI)

    Artificial intelligence is the broad umbrella term for machines performing tasks that usually require human intelligence. That includes problem-solving, learning, speech recognition, language understanding, and decision-making.

    In marketing and search, AI has become a catch-all phrase. But in practice, most of the tools I use fall into more specific categories.

    Example of AI: Siri and Google Assistant use AI to interpret voice commands and respond in context.

    Machine learning (ML)

    Machine learning is a subset of AI. Instead of giving a system explicit instructions for every possible situation, we feed it data so it can identify patterns and make predictions.

    In marketing, machine learning powers ad targeting, customer segmentation, recommendations, predictive analytics, and plenty of optimization systems we already rely on.

    ```json { "alt": "Google Home smart speaker next to Google Assistant logo with colorful dots.", "caption": "Enhance your daily tasks with Google Assistant, showcased alongside a sleek Google Home speaker.", "description": "This image features the Google Home smart speaker next to the Google Assistant logo with distinctive colored dots. The Google Home, known for its minimalist design, is a voice-activated speaker powered by Google Assistant. It helps users manage daily tasks, control smart home devices, and provide answers to queries. Perfect for tech enthusiasts looking to streamline their home automation." } ```

    Example of machine learning: Netflix uses machine learning to recommend shows based on viewing history.

    Natural language processing (NLP)

    Natural language processing helps machines understand, interpret, and generate human language.

    NLP is why ChatGPT can carry on a conversation and why Google can understand that “cheap running shoes” and “affordable sneakers” are closely related searches.

    Example of natural language processing: Google Translate uses NLP to understand and convert language in real time.

    Generative AI

    When people casually say “AI,” they often mean generative AI, which is a branch of artificial intelligence that creates content instead of only analyzing existing data.

    Generative AI models are trained on massive datasets to learn patterns in language, images, audio, code, or video. Then they use those patterns to produce something new.

    But I always remind myself that these systems are still predicting likely outputs. They are not thinking, reasoning, or understanding the world like a person.

    ```json { "alt": "Netflix homepage showing 'Matt Rife: Unwrapped' and WWE upcoming events.", "caption": "Explore the festive cheer with 'Matt Rife: Unwrapped' on Netflix, alongside thrilling WWE events! Dive into your next favorite picks.", "description": "The Netflix homepage features 'Matt Rife: Unwrapped - A Christmas Crowdwork Special,' with options to play or learn more. Below, upcoming WWE events are listed with dates and times, including SmackDown and RAW. Featured content includes popular titles like 'Stranger Things' and 'Jack Whitehall: Settle Down.' The backdrop is festive with a focus on cheerful and dynamic entertainment options. Ideal for those seeking a mix of comedy, sports, and trending series." } ```

    That is also why generative AI can go off track. When a model confidently makes something up, we call it a hallucination.

    Some of the most infamous hallucination examples include AI answers suggesting people eat small rocks or use glue to keep cheese on pizza. Funny in hindsight, but a serious reminder that fact-checking is not optional.

    • ChatGPT can draft articles, emails, and outlines.
    • Midjourney and DALL·E can create images.
    • Claude can help write and refine code.
    • Sora can generate video from prompts.

    Large language models (LLMs)

    Large language models are a specialized type of generative AI trained on huge amounts of text, including books, websites, code, and other online sources, to generate human-like responses.

    I think of LLMs as the engine behind many chatbot experiences. They are the part that interprets what I type and produces a response.

    When I use an LLM effectively, I do not treat it like a replacement for my brain. I give it context, examples, constraints, and direction. It can help refine a draft, suggest wording, or organize messy thoughts, but I still own the strategy and final judgment.

    In short, LLMs react to input. They do not act independently unless they are connected to tools and workflows that let them take action.

    • GPT models from OpenAI, used in ChatGPT.
    • Claude models from Anthropic.
    • LLaMA models from Meta.

    AI agents

    AI agents go beyond responding to prompts. They can work through multi-step tasks, use tools, navigate websites, fill out forms, call APIs, analyze files, and complete workflows with less hand-holding.

    ```json { "alt": "Diagram of the stages of communication with arrows connecting conception, composition, revision, and comprehension.", "caption": "Explore the dynamic stages of communication: from the spark of conception to composition, through careful revision, and ending in comprehension.", "description": "This image illustrates the stages of communication in a cyclical diagram. The process includes four key stages: Conception, Composition, Revision, and Comprehension, each linked by arrows to show the continuous flow. The diagram is set against a white background with a purple border and uses distinct colors for each arrow to represent different stages. Ideal for discussions on effective communication processes." } ```

    They are still powered by LLMs under the hood, but the key difference is that they have goals, tools, and a degree of autonomy.

    That is why AI agents feel more consequential for marketers. They are not just talking; they are beginning to do the work.

    • ChatGPT can search the web, analyze files, and review code.
    • Google Gemini in Workspace can summarize email threads and suggest replies.
    • Microsoft Copilot can assist across Microsoft 365 workflows.

    How I see AI affecting marketing today

    Once the terminology is clearer, the marketing impact becomes easier to see. AI is changing how people search, how content is produced, how visibility is measured, and how stakeholders talk about growth.

    People have been saying SEO is dying for years. I do not think SEO is dead, but I do think “SEO is changing” undersells the size of the shift.

    We are in the middle of a major industry pivot, and AI is at the center of it.

    Organic traffic is being cannibalized

    AI Overviews are Google’s automated summaries that appear at the top of some search results, often pulling from multiple sources.

    I think of them like Featured Snippets turned up several notches. They do not simply quote one source and send the click back. They blend sources, rewrite information in Google’s voice, and may push attribution lower on the page.

    ```json { "alt": "Email summary of Semrush LLM x SEO Hub Sync project with notes from Mordy and Gus.", "caption": "A collaborative exchange concerning the Semrush LLM x SEO Hub project promises a streamlined process, sans meetings.", "description": "This image shows an email summary for the Semrush LLM x SEO Hub Sync project. It describes Mordy's efforts to align with recipients by sending a video and Google Doc, and Gus's inquiry about contract reception. Mordy's response mentions timeline confirmation with Semrush. Keywords include Semrush, LLM, SEO Hub, email summary, sync, project collaboration." } ```

    For broad informational queries, that means the first thing a user sees may be Google’s answer instead of my blue link. The likely result is a lower click-through rate and fewer visits to publisher and brand websites.

    Before AI Overviews, informational queries were often useful for introducing a brand early in the research journey. Now, more of that attention and trust can stay with Google.

    Claim: AI Overviews only appear for fluffy queries, so my traffic is safe.

    Reality: Google is testing and expanding AI Overviews across more serious query types, including YMYL, product, and B2B searches.

    What I would do next: Stop chasing every possible click, measure visibility and influence alongside conversions, and build enough topical authority that my brand becomes a source AI systems can confidently cite.

    Content creation is exploding, and so is the noise

    Generative AI has removed one of content marketing’s biggest bottlenecks: production time. Work that used to take a team a month can now be drafted by one marketer in a week.

    That is not automatically bad. The problem is that when everyone can publish “good enough” content quickly, the internet gets louder and less useful.

    ```json { "alt": "Screenshot of search results for Jordan 1 shoes review with reviews from RunRepeat and WearTesters.", "caption": "Exploring the Air Jordan 1: A detailed review of its traction, durability, and style, featuring insights from RunRepeat and WearTesters.", "description": "This image shows a Google search result page for 'Jordan 1 shoes review.' The top result is a 2024 review from RunRepeat highlighting the Air Jordan 1 Low's excellent traction and durable leather. It mentions the shoe's iconic style but notes it may lack cushioning for modern basketball. Below is a link to a review from WearTesters that scores the shoe's traction, cushioning, and more. The page layout includes options for AI Mode, Images, Forums, and Shopping. Keywords: Air Jordan 1, shoe review, RunRepeat, WearTesters." } ```

    Claim: More content means more traffic.

    Reality: That was already questionable before AI. Now, search systems are increasingly tuned to reduce the visibility of generic, low-value, quickly produced content.

    Google’s Helpful Content updates, Bing’s spam improvements, and social platform feed changes all point in the same direction: thin content is easier to produce, but it is also easier to ignore.

    What I would do next: Focus on authority-driven content such as case studies, original data, expert analysis, and proprietary insights. I would publish less, promote more, and use AI for research, outlining, repurposing, and refreshing instead of simply flooding the web.

    Search results are becoming deeply personalized

    Traditional SEO has dealt with personalization for years through local results, logged-in history, and device context. LLM-powered tools like ChatGPT, Perplexity, and Gemini take that much further.

    The same question can produce different answers depending on the user, their prompt, their past interactions, available data, and the model being used.

    For example, if someone asks, “What is the outlook for Tesla?” a financial analyst may get an answer focused on stock performance and filings, while a new driver may see information about models, battery life, and charging infrastructure.

    Semrush Source Analysis dashboard showing AI source citations by domain for ChatGPT, with trend lines for LinkedIn, Wikipedia, Semrush and SEO sites.
    A Semrush Enterprise AI source analysis view tracks how often domains are cited in ChatGPT results, revealing shifting visibility trends across LinkedIn, Wikipedia, Semrush and industry publishers.

    Claim: I will just optimize for the top answer in ChatGPT the way I optimize for position one in Google.

    Reality: The idea of one universal top answer is breaking down. Personalization makes it harder to define, track, and reverse-engineer a single ranking position.

    What I would do next: Track visibility across search engines and LLMs, build a recognizable brand entity, invest in multiple content formats, use structured data, and create clear, citable answers that machines can understand.

    Attribution is breaking

    When Google, Bing, Perplexity, or another AI-driven platform answers a question directly, users may never visit the website that influenced the answer. Even when they do visit, their journey may start in an AI tool, move through another search, and only later reach the site.

    That breaks the clean channel → click → conversion model marketers have relied on for years.

    Claim: I will measure traffic from LLMs directly in analytics.

    Reality: That assumes users are clicking through from AI answers. In many cases, they are not.

    Semrush AI Performance dashboard showing sentiment analysis charts, positive and neutral mention counts, and brand sentiment leaderboard.
    A Semrush-style AI sentiment dashboard visualizes how brands appear across AI search, with mention trends, sentiment mix, and a competitive leaderboard.

    What I would do next: Move beyond last-click attribution, pay more attention to assisted conversions, and track broader demand signals such as direct traffic, branded search volume, brand mentions, sentiment, and “How did you hear about us?” responses.

    I would also budget for influence that is hard to perfectly track, including podcasts, PR, thought leadership, community visibility, and media coverage.

    Clients and bosses expect magic

    Because AI hype is everywhere, stakeholders often expect it to make everything faster, cheaper, and better without understanding the risks, learning curve, or human oversight involved.

    Claim: We can replace our SEO or content team with AI tools and get the same results.

    Reality: AI can accelerate tasks, but it does not replace strategy, judgment, subject-matter expertise, or a real understanding of customer needs.

    What I would do next: Set expectations early. AI can make some work faster and cheaper, but it is not a push-button strategy. I would show stakeholders the hidden work behind good AI output, including prompt refinement, editing, fact-checking, compliance, and final review.

    The best use of AI is not to remove human thinking. It is to free up more human time for the strategic work that actually moves the business forward.

    Search is evolving

    I am not interested in getting stuck in a debate over Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or any other acronym. The important point is simpler: search today is not what it was yesterday.

    Organic visibility is no longer only about ranking in Google. Search now includes AI answers, YouTube, Reddit, newsletters, communities, social platforms, and every place people go to discover, compare, and validate information.

    If I am only thinking about the traditional search bar, I am already behind. The better path is to build authority, create content worth citing, understand how AI systems interpret information, and measure visibility across the full discovery journey.

    AI is not the end of SEO. It is a major shift in how search works, how content is consumed, and how brands earn trust. The marketers who adapt will be the ones who separate useful strategy from the noise.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot