Tag: Marketing Attribution

  • 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.


    crushpress.ai community screenshot
  • Why MMM Still Demands Clean Data and Human Judgment

    Why MMM Still Demands Clean Data and Human Judgment

    I see marketing mix modeling (MMM) becoming easier to access, but I do not think it has become easy to get right.

    After several conversations about MMM adoption, I keep hearing the same concern: “We believe in MMM, but we do not know how to get started.”

    My answer is that open-source platforms have lowered the barrier to entry in a meaningful way. What they have not lowered is the level of expertise required to produce results that are trustworthy, explainable, and useful for decision-making.

    Open-source MMM has changed the starting point

    I am seeing MMM adoption accelerate because marketers need more durable measurement methods. Almost half of U.S. marketers expect to invest more in MMM over the next year, and many now rank it as one of the most reliable measurement approaches available.

    The open-source shift is real. Three production-grade libraries now give teams a practical way to approach MMM across a wide methodological spectrum.

    • Robyn (Meta, R): I see this as the most approachable starting point because it includes automated hyperparameter search through Nevergrad, Pareto frontier model selection, decomposition, and response curve plots. It is also the one I use most often because it is highly customizable.
    • Meridian (Google, Python/TensorFlow): I view Meridian as a more rigorous option, especially because it uses Bayesian inference, geo-level priors, and principled uncertainty quantification. The tradeoff is a steeper learning curve.
    • PyMC-Marketing (PyMC Labs, Python): I consider this the most flexible path. It offers a full probabilistic model that comes closest to academic-grade Bayesian MMM, but it also demands the most statistical fluency.

    This generation of tools has removed the old $150,000 to $500,000 consulting gate that used to be the primary path into MMM. A team with R or Python expertise and reasonably clean historical data can now run a model in-house.

    Chart showing marketing mix modeling costs dropping from a $150k-$500k consulting gate to near-zero open-source tools while expertise needs stay high.
    Open-source R and Python tools have lowered the cost of starting with marketing mix modeling, but the expertise needed to produce trustworthy, actionable MMM remains the real ceiling.

    The caveat I always make explicit is this: “free tool” does not mean “free model.” The software may be free, but the domain expertise needed to configure it correctly is not. That expertise is a major part of the value.

    The vendor landscape is crowded and complicated

    I also see a fast-growing SaaS layer built on top of open-source MMM. To evaluate it clearly, I find it helpful to separate vendors into a few practical groups.

    Data-layer-first vendors

    Platforms like Rockerbox and Northbeam started with attribution and data collection, then added MMM. Their advantage is usually pipeline speed and data access, not deep modeling flexibility or customization.

    Measurement-first vendors

    Platforms such as Measured, Analytic Partners, Ekimetrics, and Nielsen Gracenote tend to offer more rigorous modeling and enterprise-grade capabilities, usually at a higher price point.

    Google Meridian and GA360

    I think Google’s decision to open-source Meridian is both a generous contribution to the field and a strategic move. When a walled garden funds and packages a measurement methodology that can be used to evaluate its own channels, I believe it is worth maintaining healthy skepticism about priors, defaults, and assumptions, even when the code is transparent.

    Chart comparing open-source marketing mix modeling libraries Robyn, Meridian, and PyMC-Marketing along a spectrum from approachable to statistically rigorous.
    Open-source MMM tools now span a clear trade-off: Robyn offers the most approachable starting point, Meridian adds Bayesian rigor, and PyMC-Marketing pushes deepest into statistical flexibility.

    The practical vendor question I keep coming back to is simple: who owns the data layer, and does that ownership create conflicts in the modeling layer?

    Challenge 1: Data access can quietly break MMM

    I think data access is the most underappreciated MMM implementation blocker. A well-specified model needs more than a quick export from a dashboard.

    • I usually want two to three years of weekly data as a baseline, so the model can capture at least two full seasonality cycles and enough spend variation to learn from.
    • I need consistent channel-level spend granularity, not just a broad “digital” bucket. Search, social, display, video, and other channels need to be separated.
    • I need offline channels such as TV, OOH, radio, events, and direct mail, even though they often live in different systems, belong to different teams, and use incompatible time periods.
    • I need external covariates, including macro indicators, competitor activity, pricing data, and product launch calendars.
    • For B2B, I often need even more history because longer sales cycles and lower conversion volumes make the data requirements more demanding.

    In practice, I often find that the real blocker is the six-week data archaeology project that happens before modeling begins. Finance owns revenue. The brand team owns TV. The agency owns digital spend. A spreadsheet from 2021 may be the only record of trade promotions.

    The model is only as good as the data archaeology behind it, and that is rarely the part anyone highlights in a vendor demo.

    Challenge 2: I still have to roll up my sleeves

    AI assistants have lowered the syntax barrier. They can scaffold a Robyn run, generate a Meridian configuration, or help debug a PyMC model. What they cannot reliably do yet is make the judgment calls that determine whether an MMM is credible.

    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 still have to decide where to land on a Pareto frontier across hundreds of model solutions, balancing NRMSE against DECOMP.RSSD tradeoffs.
    • I still have to know whether Nevergrad’s optimizer has meaningfully converged or simply landed in a local minimum.
    • I still have to configure adstock transformation parameters, including Weibull shape and scale or geometric decay, so they reflect realistic channel behavior.
    • I still have to diagnose why a model gives a channel an implausible contribution and decide whether the fix is a prior, a data correction, or a variable exclusion.

    In other words, if I try to vibe code my way into MMM, I may end up with a model that appears to work but is wrong in ways I will not catch. The scripting is not the hardest part. The real work is validating the output, including using channel-specific incrementality experiments to calibrate the model.

    Challenge 3: Human expertise is not optional

    Even if the tools mature enough for AI to run a competent default MMM, I still see human expertise as essential. The irreplaceable work is encoding business context that no model can infer from the data alone.

    • Adstock and carryover context: I need to know whether a TV buy carries over for four weeks, paid search carries over for three days, or a brand awareness campaign decays over months. That knowledge usually lives with channel experts, not inside the dataset.
    • Saturation curve shape: I need to recognize when a channel is probably approaching diminishing returns before the model says so, and I need to question the model when it suggests something implausible.
    • Guardrails and anomaly handling: I need to explicitly model or flag COVID troughs, product launches, pricing shifts, and macro disruptions as structural breaks. AI does not automatically know a client had a pricing crisis in Q3 2022.
    • Interpretation sanity checks: If a model assigns 40% of contribution to TV for a brand spending $2 million on TV, I need the experience to say, “That feels wrong,” and investigate. That intuition is earned, not computed.
    • Organizational translation: A technically correct model has little value if I cannot explain why it recommends moving 15% of search budget to CTV in language a CMO and CFO will act on.

    I start with the groundwork before the model

    The best place to begin is not the model itself. I start by understanding what data is needed, who owns it, and who can help interpret it in the context of real marketing decisions.

    None of that is quick or easy, but it is essential if I want meaningful insight from MMM, whether I choose an open-source library or a subscription-based platform.

    As a practical first step, I would download Robyn’s demo script and experiment with sample data before applying MMM to my own business data. That kind of hands-on testing makes the strengths, limits, and judgment calls much clearer.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • 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
  • Why I’m Watching Google’s New YouTube Measurement Tools

    Why I’m Watching Google’s New YouTube Measurement Tools

    I’m seeing Google expand its measurement capabilities for YouTube brand campaigns, and the goal is clear: advertisers are getting better visibility into how video ads influence engagement, brand interest, and downstream business outcomes.

    What’s new: I’m paying attention to two updates in particular: Shorts Ad Actions for Video View Campaigns and Attributed Branded Searches.

    Shorts Ad Actions for Video View Campaigns: When advertisers run Video View Campaigns that are opted into YouTube Shorts, they will now automatically benefit from Shorts Ad Actions in budget optimization. Google is also adding new reporting columns so advertisers can measure these interactions more clearly.

    Attributed Branded Searches: Now available globally in Google Ads, this reporting metric measures branded Google searches that happen after someone sees or views a YouTube ad. I see this as a useful way to understand how awareness campaigns may influence purchase intent before a direct conversion takes place.

    Why I care: It has always been difficult to connect upper-funnel YouTube campaigns with measurable business outcomes. These updates give marketers stronger signals that link brand advertising to engagement and search intent, which can make it easier to justify brand investment and improve campaign decisions.

    By the numbers: According to Google, YouTube Shorts ads that generated more than 10 seconds of watch time and a like delivered 15% higher brand consideration and 20% higher brand favourability.

    Google also says every additional branded search generated is associated with an average $31 increase in sales, which gives advertisers another way to connect brand activity with business impact.

    Between the lines: I see Google continuing to blur the distinction between brand and performance marketing by introducing metrics that connect awareness campaigns with downstream actions. Attributed Branded Searches, especially, gives advertisers another way to show that YouTube campaigns can influence high-intent behaviour before a conversion happens.

    The bottom line: Google’s latest measurement updates help advertisers better prove the value of YouTube brand campaigns by linking video engagement and branded search activity to business outcomes. For me, the bigger story is that upper-funnel advertising is becoming easier to measure in ways that matter to performance-focused teams.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • How I Measure AI Search Leads Before Optimizing

    How I Measure AI Search Leads Before Optimizing

    For the past two years, I have heard marketers ask the same urgent question: How do I show up in AI search?

    I have seen plenty of conversation around AI optimization, visibility, and the way large language models decide which businesses to recommend. But I believe the more practical question is now becoming harder to ignore: How do I measure whether AI search is actually sending customers my way?

    That is the challenge I wanted to understand more clearly.

    After analyzing nearly 30 million inbound leads, I found that AI platforms are already shaping how customers discover businesses and decide to make contact. AI-generated leads still represent a small share of total volume, but they are growing steadily enough that I think marketers should start watching this channel closely.

    In other words, the conversation is moving from visibility to measurement.

    AI search is becoming a new attribution challenge

    Traditional attribution models were built for channels like organic search, paid search, direct traffic, and referrals. AI search introduces a different discovery path, and I do not think most reporting systems are fully prepared for it yet.

    A customer might ask ChatGPT for the best local HVAC company, use Perplexity to compare law firms, or ask Gemini to recommend a nearby dentist before picking up the phone.

    From a marketer’s perspective, those customers may show up as direct traffic, or they may not be attributed at all. That creates a real blind spot.

    If AI platforms are influencing customer discovery, I need a way to measure whether those recommendations are turning into real business outcomes.

    What 30 million leads tell me

    The data shows me that AI platforms are already generating measurable inbound leads for businesses. It also shows that this activity is growing over time and appearing across multiple industries, not just one category or use case.

    One platform currently accounts for most AI-attributed calls, while other platforms contribute smaller shares that continue to change as customer behavior evolves. The data also reveals which industries are receiving more AI-driven calls than others.

    At the same time, I have to be clear about what this dataset can and cannot measure. It does not explain why customers chose one AI platform over another, what prompts they used, or why a specific business was recommended. What it does measure is more concrete: when customers identify an AI platform as part of the journey that led them to contact a business.

    That distinction matters. There is no shortage of opinion about AI search. What I need now is evidence that it is influencing customer acquisition.

    Measurement should come before optimization

    I understand why marketers are eager to optimize for AI search. But before investing in new tactics, I think it is worth answering a simpler question first: Is AI already driving customers to my business?

    Without measurement, it is difficult to know whether greater visibility is translating into meaningful business results.

    As AI search becomes another customer acquisition channel, I want to measure it the same way I measure other demand sources, including paid search, organic search, referrals, and social.

    The goal is not to replace existing attribution models. The goal is to make sure those models evolve as customer behavior changes.

    From visibility to measurement

    The first wave of AI search focused on visibility. I believe the next wave will focus on proving business impact.

    For marketers, that means moving beyond questions like, “Can customers find us?” and toward more outcome-focused questions like, “How many leads did AI actually generate?”

    The businesses that answer those questions first will be better positioned to understand how AI fits into their marketing mix and where to invest as customer discovery continues to evolve.

    Don’t just watch the shift. Start measuring it.

    As AI search keeps evolving, I am focused on giving marketers the attribution they need to connect AI discovery with real customer conversations.

    Try CallRail free at CallRail.com.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • How I Measure Paid Social’s Real Impact on Paid Search

    How I Measure Paid Social’s Real Impact on Paid Search

    I’ve learned that generating demand is one of the hardest jobs in digital marketing. Measuring where that demand actually started can be even harder.

    For years, I’ve seen paid search and paid social treated like separate worlds. Paid search usually gets evaluated through clicks, conversions, and ROAS, while paid social is often judged by platform-reported metrics and attributed conversions.

    The challenge is that people don’t move through the buying journey in neat, channel-by-channel steps.

    Someone might first discover a brand through a Meta ad, ignore it, see another ad a few days later, and eventually search for the brand or product on Google before adding something to the cart and converting. In most reports, paid search gets the credit because it captured the last click. But I don’t think that tells the full story if search didn’t create the demand in the first place.

    As privacy rules, platform tracking, and attribution limits keep changing, I need better ways to understand how paid social influences search behavior. These are the practical signals and measurement methods I use to connect the two.

    Signs I Look For When Paid Social Influences Search

    Paid social’s impact on search is not always obvious inside attribution reports. I usually see it show up first in performance trends. These indicators help me understand whether social campaigns are building awareness that later turns into search activity and conversions.

    Branded Search Volume Starts Rising

    One of the clearest signs I watch for is an increase in branded search queries.

    When people see a relevant, compelling social ad on Meta, TikTok, LinkedIn, or another platform, they often do not click right away. Instead, they may come back later and search for the brand name, product name, founder, or another branded term.

    For example, after launching a new Meta Ads campaign, I might look for increases in searches like these:

    • Brand name.
    • Brand + product category.
    • Brand + reviews.
    • Brand + pricing.
    • Brand + competitor comparisons.

    I monitor these branded searches over time because they can reveal whether paid social is creating awareness that later becomes search behavior.

    To do that, I review data from Google Ads, Microsoft Advertising, Google Analytics, Google Search Console, Google Trends, and any third-party SEO tools available.

    I also compare trends before, during, and after major paid social launches or budget changes. If branded search volume keeps rising as paid social investment increases, I take that as a strong directional sign that social is helping generate demand.

    That does not mean every increase in branded search comes from paid social. My goal is not to prove perfect causation. My goal is to find a meaningful relationship I can use to make better decisions.

    Image

    I also account for other factors that can lift branded search volume, including:

    • Influencer partnerships.
    • Email campaigns.
    • Public relations coverage.
    • Seasonal demand.
    • Product launches.
    • Highly engaging organic social activity.

    Search CTR Improves

    Another signal I watch closely is click-through rate. If paid social is increasing brand familiarity, people may be more likely to click a search ad from that brand instead of choosing a competitor.

    For example, someone might see Instagram video ads for two weeks and later search for a related topic on Google. When several ads appear, they may be more inclined to click the brand they already recognize.

    I see the same concept reflected in brand recognition surveys that Meta and LinkedIn sometimes show in user feeds. I often find myself recognizing brands I have never purchased from simply because I have seen their ads repeatedly on social media.

    That basic familiarity can still matter. It can help lift CTR on branded search campaigns, improve CTR on non-branded campaigns, and potentially lower CPCs over time.

    Whenever I launch a new paid social campaign or make a significant adjustment, I compare paid search CTR before and after the change to see whether search engagement improves.

    Search Conversion Rates Improve

    Brand familiarity can also affect conversion rates. When people have already seen or engaged with a brand, they may arrive on the website with more trust and confidence than a completely cold visitor.

    Because of that, I look for improvements in search conversion rate, lead quality, search CPA, and revenue per visitor after periods of strong paid social activity. This effect can be especially noticeable for products or services with longer consideration cycles and multiple touchpoints before purchase.

    For me, conversion efficiency is one of the most useful signs that paid social is influencing downstream search behavior.

    How I Validate Paid Social’s Impact on Search

    The signals above give me directional insight. When I need stronger evidence, I use more structured measurement methods to evaluate whether paid social activity is actually influencing paid search performance.

    Pre- and Post-Campaign Analysis

    One of the simplest ways I evaluate the relationship is with a pre- and post-campaign analysis.

    Before a paid social campaign launches, I benchmark key paid search metrics. Then I compare those numbers with performance after the campaign goes live.

    Image

    The metrics I usually measure include:

    • Branded search impressions.
    • Branded search clicks.
    • Search CTR.
    • Search CVR.
    • CPA.
    • Total search conversions.

    This analysis will not prove causation on its own, but it can show whether increased social activity may be influencing search performance. When I run this type of analysis, I account for seasonality, compare similar time periods, and watch for changes in competitor activity.

    Geotargeted Holdout Testing

    When I need stronger evidence, I consider a geotargeted holdout test. In this setup, I run paid social in selected geographic markets while withholding it from comparable control markets. Then I compare paid search performance across both groups.

    For example, instead of running paid social everywhere, a nationwide advertiser could split markets into two groups:

    • Test market(s): Paid social campaigns are active.
    • Control market(s): Paid social campaigns are paused or excluded.

    I would run the test for several weeks and monitor the same core metrics in both groups:

    • Branded search volume.
    • Search CTR.
    • Search CVR.
    • Leads.
    • Revenue.

    If the test markets show meaningfully stronger search performance than the control markets, I have a better basis for isolating the impact of paid social.

    I like geotargeted tests because they reduce attribution bias. They let me evaluate business outcomes across similar populations instead of relying only on platform-reported conversions, which can be limited by privacy changes and tracking gaps.

    If I run a holdout test, I choose comparable markets, set aside enough budget, and give the test enough time to produce statistically meaningful results. This approach usually works best for larger advertisers running regional or national campaigns. For smaller brands, I would usually start with pre- and post-campaign analysis.

    Why I Measure Influence Across Channels

    The relationship between paid search and paid social is often stronger than reporting platforms make it appear. I try not to evaluate these channels in isolation because they often play different roles in the same customer journey. Search captures demand, while paid social can help create it.

    By digging into the data, I can find better ways to invest, build future demand, and drive conversions across platforms. Monitoring branded search, CTR, conversion rates, and structured test results gives me a clearer view of how paid social contributes to business growth.

    Attribution will never be perfect. But when I measure influence across channels, I can make smarter budget decisions and build a more accurate picture of what is actually driving performance.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • 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
  • Meta’s New Attribution Updates: Enhancing Ad Insights

    Meta’s New Attribution Updates: Enhancing Ad Insights

    Hey there! Meta has recently rolled out some exciting updates to their ad measurement framework, designed to simplify attribution in our ever-evolving “social-first” advertising landscape. I’m here to break it all down for you.

    What’s new? Meta is redefining how click-through attributions work for both website and in-store conversions. From now on, only link clicks will contribute to click-through attribution, while other interactions like likes, shares, and saves won’t count. This shift aims to align Meta Ads Manager better with tools like Google Analytics, reducing discrepancies.

    The shift in focus. WARC reports that social media has now overtaken search as the world’s largest ad channel. But many of our current attribution models were designed with search behavior in mind. Unlike in the past where every type of click was tallied, this update recognizes the unique engagement patterns on social platforms, historically leading to reporting misalignment.

    What’s evolving? Conversions attributed to actions other than link clicks will now be categorized under a new term, “engage-through attribution,” which replaces the old “engaged-view attribution.” Additionally, Meta is shortening the video engaged-view window from 10 seconds to just 5 seconds. This change reflects faster conversion activity, especially noticeable in Reels, where 46% of purchase conversions happen within the first two seconds.

    Why should we care? These updates provide clarity by distinguishing link-driven conversions from other social interactions. This distinction will help marketers better understand campaign performance, boosting confidence through more precise data analysis. The new engage-through attribution process highlights the impact of likes, saves, and shares.

    With these changes, advertisers can trust their data more and make more informed, impactful decisions.

    ```json
{
  "alt": "Diagram showing click-through, engage-through, and view-through metrics with icons.",
  "caption": "Explore digital marketing metrics with this diagram, illustrating the flow from click-through to engage-through and view-through using intuitive icons.",
  "description": "This image visually represents key digital marketing metrics: click-through with a link click icon, engage-through with icons for like, comment, save, and share, and view-through with engaged-view and impression icons. The diagram highlights the progression from user interaction with content through various stages, helping analyze engagement and view metrics. Keywords: digital marketing, click-through, engage-through, view-through, metrics."
}
```

    Collaborations in the pipeline. To offer advertisers a more comprehensive view of performance, Meta is collaborating with analytics providers like Northbeam and Triple Whale to integrate both clicks and views into their attribution models.

    Rollout details. These changes are slated to begin later this month for campaigns focusing on website or in-store conversions. While billing methods remain unchanged, you might notice shifts in reporting as these new attribution definitions are implemented in Ads Manager.

    The bottom line: Meta is striving to combine clearer click reporting similar to search engines with insightful data on social interactions. This balance offers advertisers a cleaner, broader comparison across platforms while focusing on the unique contributions of engagement-driven actions.

    Dig deeper. For more information, you can check out Meta’s detailed explanation in their Simplifying Ad Measurement for a Social-First World.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Google Enhances App Conversions with Install Date Attribution

    Google Enhances App Conversions with Install Date Attribution

    I recently discovered that Google is changing how it attributes app campaign conversions. Instead of relying on the date when someone clicks on an ad, Google now ties the conversion to the actual install date of the app.

    What’s Changing: Previously, Google linked conversions to the ad interaction date. Now, they’ll match the day of the app installation, aligning more closely with Mobile Measurement Partners (MMPs) like AppsFlyer and Adjust.

    Why This Helps:

    – This change reduces discrepancies between Google Ads and MMP dashboards, making life easier for mobile marketers who often deal with mismatched data.

    – With Google’s old 30-day attribution window, many conversions were reported too late, hindering Smart Bidding’s access to the timely signals necessary for effective learning.

    – By using the install date for attribution, Google’s algorithms will receive fresher, more accurate data, which could speed up optimization cycles and stabilize performance.

    ```json
{
  "alt": "Google Ads email about updates to app campaign attribution and post-install conversion window.",
  "caption": "Google Ads announces updates to app campaign attribution, focusing on improved measurement for post-install conversion events, aligning with industry standards.",
  "description": "This image shows an email from Google Ads detailing improvements to app campaign attribution. It announces an update to the calculation logic for the post-install conversion window (PIE), aiming for more accurate attribution. The changes intend to start the calculation from the 'App Install' or 'First Open' rather than initial ad interaction, keeping up with industry standards."
}
```

    Why We Care: While it might seem technical, this change significantly affects how Google’s machine learning optimizes campaigns. The previous 30-day gap between ad clicks and conversion credit was a bottleneck. Now, Google’s machine learning gets the conversion data just when it needs it—right with the app install.

    This shift should lead to smarter bidding and faster campaign optimization, helping to resolve the frustrating discrepancies between Google Ads and MMP reports. If you’ve ever been puzzled by inconsistencies between Google and platforms like AppsFlyer or Adjust, this update directly addresses that problem.

    Between the Lines: Most advertisers don’t adjust their attribution window settings, leaving Google’s default 30-day window as is. Unfortunately, this was delaying crucial conversion signals that machine learning needs for improved bidding.

    The Bottom Line: This seemingly minor tweak in attribution logic could have a significant impact on app campaign performance. I encourage mobile advertisers to monitor their data in the coming weeks for any shifts in conversion reports and optimization behaviors.

    First Spotted: This update was first noticed by David Vargas, who shared a message about it on LinkedIn.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Master AI Search Attribution: Boost Visibility & Revenue

    Master AI Search Attribution: Boost Visibility & Revenue

    As I delve into the world of AI search attribution, I’m eager to share practical insights on measuring brand visibility and influence. AI is transforming the way decisions are made, often eliminating the traditional click, which can make tracking impacts on revenue more challenging yet fascinating.

    In this guide, I’ll explore how AI-driven decision-making affects our brand’s visibility without relying on direct clicks. By understanding these dynamics, we can craft strategies that enhance our brand’s influence in an AI-dominated landscape.

    Join me on this journey to uncover the methods of interpreting AI search impacts. Together, we’ll look at ways to quantify these effects, providing clear evidence of our marketing efforts’ return on investment and long-term impact.


    Inspired by this post on HiGoodie Blog.


    crushpress.ai community screenshot