Tag: Customer Acquisition

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

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.
July 10, 2026
Why Frontloading Ad Spend Backfires—and How I Scale
I don’t recommend launching most paid media campaigns with the biggest budget available.

When I see advertisers spend aggressively before validating performance, the outcome is often predictable: acquisition costs rise, optimization slows, and stakeholder confidence weakens when the promised results fail to appear.

I prefer a phased rollout because it gives a campaign time to generate meaningful data, improve bidding efficiency, and reveal which audiences, keywords, and creative ideas deserve more investment.

Here, I’ll explain why frontloading ad spend usually backfires, when a more aggressive launch may be justified, and how I scale budgets without sacrificing long-term performance.

Fire bullets before cannonballs

For those of us who make a living driving growth through paid media, there’s one problem almost as frustrating as a tiny advertising budget: an advertiser determined to spend too much, too soon.

I believe every paid media launch should follow a deliberate plan. As Jim Collins wrote in Great by Choice, successful companies fire “bullets” first, learn from the results, and then fire “calibrated cannonballs” with greater confidence.

In my experience, most campaigns aren’t ready for a cannonball on day one. The algorithms are still learning, Quality Scores haven’t matured, and I don’t yet know which audiences, keywords, or creative assets will perform best. That is usually when acquisition costs and inefficiencies are highest.

I recognize that exceptions exist. Years of relevant historical data or unusually strong evidence may occasionally justify a more aggressive launch, but I consider those situations rare.

More often, I see frontloaded spending create expensive lessons instead of faster growth. The following scenarios illustrate why companies choose this approach and why I usually recommend a measured rollout instead.

Your budget isn’t a KPI

I never confuse the amount spent on advertising with actual performance, regardless of how an ad platform labels its reporting columns.

The Modify Columns workflow in Google Ads. Its Performance bucket is… not actual performance.

From what I’ve observed, street-smart owner-operators tend to begin with careful ad budgets. Deep-pocketed decision-makers are more likely to focus on how much they are capable of spending.

By deep-pocketed decision-makers, I mean anyone from a high-ranking Fortune-something executive or venture capitalist to a serial entrepreneur who has suddenly received an unusually generous investment from a single backer.

When Nassim Taleb praises people with “skin in the game,” I take the point to be that risk looks different when I must personally bear its consequences. Risk asymmetry allows a splashy failure to hurt the company far more than it hurts the person who encouraged the gamble.

Bruno Mars briefly steals the spotlight: Google Trends shows his concert query surging to peak popularity in early 2026, while “concert near me” maintains steadier interest across the year.

Directly or indirectly, I’ve analyzed close to 1,000 ad accounts over the years. The pattern has been clear: advertisers that overspend early in pursuit of hypergrowth often flame out and lose stakeholder support.

Dig deeper: PPC budgeting in 2026: When to adjust, scale, and optimize with data

Four frontloading arguments—and why I question them

1. “It’s a land grab. We need to spend aggressively to gain market share quickly.”

I rarely consider this a prudent strategy, but I understand the motivation behind it.

The goal is to capture market share and secure a first-mover advantage before new entrants catch up. I can think of plenty of fast-moving customer acquisition environments, particularly among technology startups, where that prospect feels irresistible.

I once joined a project to help a startup with a greatly diminished, modest, incremental Google Ads campaign. What shocked me was how little the company had learned—and how little money remained after it had raised more than $250 million. Nearly all of that funding had been burned, including large sums spent on advertising, and there wasn’t going to be more where it came from.

My team helped the company measure KPIs such as “new accounts that actually led to revenue” and “lifetime revenue from those accounts.” Despite three years of relentless nine-figure spending, no one had made those outcomes a serious measurement priority.

I’ve also seen bootstrapped startups become carried away after celebrating their first $1 million to $2 million in “real” venture funding. They may have less money to burn, but the faulty logic is the same—and the risk can be even greater.

Over the years, I’ve helped niche SaaS startups such as Clio in legal practice management and SuccessFactors in HR management achieve prominence without pretending they were already operating at their future scale.

I don’t see small beginnings or cautious ad budgets as barriers to unicorn status. I can match customer acquisition spending to a company’s current growth stage without sentencing it to permanent smallness.

For initial paid growth, I recommend defining the addressable market relatively tightly. I save the “huge addressable market” story for longer-horizon conversations with investors instead of using it to justify immediate overspending.

To keep early-stage growth in perspective, I remind myself how a behemoth like Uber began. Its seed round was $1.25 million, and the company was valued at a modest $4 million.

I’m happy to think big, but I don’t try to act bigger than the company really is when the money and product-market fit aren’t there yet. If the business establishes a meaningful lead, network effects and access to more capital can accelerate growth later.

A paid media campaign blasts toward growth, but the glowing budget controls offer a warning: validate performance, optimize carefully, and earn the right to scale.

I often wonder why founders race through essential growth stages by setting their newly raised—but finite—cash on fire. Sometimes investors encourage it. In other cases, the growth team treats fresh funding like permission to spend without restraint.

I know what happens when the hangover arrives. Investors see high churn, stratospheric customer acquisition costs, or few tangible signs of customer acquisition at all. They react as though they have been mortally wounded, even when they helped create the strategy.

I always return to unit economics because they still matter. Other founders may appear to have repealed the laws of economics, but I remember the familiar parental warning: “If Billy jumped off a cliff, would you do it too?”

2. “We’ll learn faster.”

I agree that predictive bidding algorithms perform poorly when conversion and value signals are sparse. More data can help them recognize the patterns associated with higher-value sessions.

My team also needs to move through feedback loops to understand what works, what fails, and how the campaign should evolve.

One genuine benefit of higher volume is that I can discover necessary negative keywords more quickly. With low query volume, many bad searches may remain hidden in “Other Search Terms” for a long time.

Even so, I find that spending becomes counterproductive beyond a certain point. More money does not automatically turn incomplete data into reliable insight.
- I consider the length and variability of the sales cycle. If two or three months normally pass between the first ad view and a sale, forcing too much budget into month one leaves me running ads almost blind, with little opportunity to learn and iterate before the money is gone.
- I watch for self-inflicted CPC inflation. If I charge into an auction that has reached a workable equilibrium and bid too aggressively, I may raise my own costs and prompt competitors to bid higher as well.
- I expect early metrics to be relatively weak because the campaign hasn’t established mature Quality Scores. In one account, CPCs eventually fell by 80% as Quality Scores developed and our optimizations took effect. I was relieved that the initial pilot had used a modest budget.
I see little logic in pouring a flood of money into what may be the worst ROI environment the campaign will ever face. Even four to six weeks later, ROI is often substantially better as Quality Scores mature and statistical confidence improves.

Dig deeper: Stop looking for the perfect PPC budget split

3. “We’re pre-revenue, and our investor wants a quick market-size estimate.”

Whenever I hear this argument paired with a hefty new check, I have to ask: What could possibly go wrong?

To me, this takes the land-grab strategy even further into the intellectual ether. Customers—or almost any other concrete business outcome—may not be the immediate goal.

From where I sit, the investor’s instruction often amounts to this: “We don’t care if we spend a huge amount of cash in the first month. Just get us a pile of data.”

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 a powerful backer’s name comes up, it’s tempting for everyone to shrug and think, “Billionaire knows best.” I’ve watched teams dutifully throw money at a performance channel without asking it to perform, only to learn 35 days later that the investor won’t contribute another penny. The founder is then left without a credible Plan B.

I can imagine the next investor arriving with a few basic questions.
- “Q: What is the company, exactly? I mean, what product or service do you provide?”
- “A: We’re still figuring that out, but we know there must be a gold mine in there somewhere, given how many music fans are searching for [music examples redacted to protect the innocent].”
I don’t consider that a true launch because the project was never clearly defined in the first place.

To be fair, I do believe fail-fast market research can be valuable. My team once spent about $10,000 over a short period for a client exploring a telecommunications business model. The test gave him a definitive answer about demand patterns, and he decided not to enter that vertical.

I regard Google Ads as an invaluable market research tool when I use it with discipline. I define a meaningful business outcome and require potential customers to clear a credible threshold of intent. If I don’t need that level of evidence, I can explore the question with Google Trends, Google Analytics on a purpose-built content site, Semrush, or a dedicated market research company.

Free Google Trends market research shows “bruno mars concert” giving “concert near me” a solid run for its money.

In an unusual research scenario like this, my goal is to control waste. I may not be able to eliminate it entirely, but I can keep it proportional to the value of the answer I’m seeking.

4. “A vendor won’t work with us unless we spend more immediately.”

I know that some ad platforms, third-party software tools, and managed services impose steep minimums. I also understand why advertisers feel pressured by FOMO to overspend for entry into an exclusive club.

I think the early OpenAI ad pilot offered a timely example. Steep minimums and uncomfortably high CPMs appeared to exclude the typical advertiser.

I won’t twist myself into a pretzel to rationalize wildly overpaying for every ad interaction. Eventually, the market may become more accessible. I only have to compare how easy it is to begin with StackAdapt in programmatic advertising against the higher barriers associated with Google DV360 and The Trade Desk.

When I advise a smaller company, I encourage it to grow first and enter a more demanding platform only when its size and budget justify the move. I see this as a version of The Millionaire Next Door logic: buying a house I can’t afford or driving a luxury car doesn’t make me wealthy. It might prevent me from ever getting there.

Dig deeper: How to diagnose and fix the biggest blocker to PPC growth

Earn the right to scale

My core conclusion is that frontloaded ad spending often destroys the support a campaign needs to succeed. I don’t want to taint an entire channel—or the company’s broader growth function—by accelerating so hard that the campaign skids into a ditch. I can travel much farther after building solid traction.

For an owner-operated business with real skin in the game, this is about more than stakeholder confidence. Severe waste isn’t merely bad optics; it can threaten the company’s future.

So, when an overconfident investor or ad platform sales representative urges me to go from “zero to sixty in 3.5,” I’m inclined to tap the brakes. I would rather earn the right to scale than discover too late whether the airbags work.

Inspired by this post on Search Engine Land.
July 10, 2026
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.

July 3, 2026
Google Demand Gen Gets Gemini Creative and Reporting Boost

I’m seeing Google roll out a new set of Demand Gen updates designed to help advertisers improve creative performance, reach more potential customers across YouTube, and measure campaign results with more clarity.

For me, the bigger story is that Demand Gen is becoming less about manually adapting assets and more about using AI-assisted tools to make creative work harder across Google’s most visual surfaces.

Demand Gen campaigns are built to drive discovery and conversions across Google’s visual placements. With these latest updates, I see Google trying to reduce creative friction while giving advertisers better visibility into what is actually moving performance.

Google says the enhancements arrive as YouTube continues to show value for customer acquisition. The company cited research from Measured showing that 72% of incremental conversions on YouTube come from new customers.

What’s new. I’m watching Demand Gen add expanded video resizing capabilities, giving advertisers the ability to automatically transform creative into more aspect ratios, including vertical-to-square, vertical-to-landscape, and square-to-landscape formats.

That matters because it should make it easier to adapt existing creative for different YouTube placements without having to produce every version manually from scratch.

Why I care. Expanded video resizing can help existing assets fit more YouTube inventory, Gemini can provide AI-powered recommendations before launch, and new web-to-app measurement can give marketers a clearer view of how Demand Gen campaigns influence app installs and return on ad spend.

Gemini joins the creative workflow. Google is also bringing Gemini-powered recommendations directly into the Demand Gen campaign creation process, which makes AI guidance part of the asset selection workflow instead of a separate optimization step.

When advertisers choose image and video assets, Gemini will offer automated suggestions for optimizing creative for YouTube. I see this as a way for marketers to improve asset choices before campaigns go live, rather than waiting for performance data after launch.

Better app measurement. Demand Gen now includes Web to App Acquisition Measurement, allowing advertisers to measure when web campaigns lead users to install an app.

The new reporting gives me a more complete way to evaluate campaign performance because it attributes app installs generated through Demand Gen campaigns. That should help advertisers better understand the full impact of their media spend.

The bottom line. I see Google’s latest Demand Gen updates as a practical combination of AI-powered creative guidance, more flexible video optimization, and broader measurement tools that can help advertisers improve performance while gaining clearer insight into customer acquisition.

Inspired by this post on Search Engine Land.

July 3, 2026
Mastering PMax to Capture Net New Customers Effectively

As I explore the potential of Performance Max for acquiring new customers, I realize that without proper setup, it’s easy to see inflated dashboard metrics that obscure the reality of your profitability.

One major pitfall is recycling traffic from Meta. Paid search and social traffic often overlap, leading to the dreaded scenario where platforms each claim credit for conversions they didn’t fully drive.

Many direct-to-consumer (DTC) brands I talk to boast about their growing numbers. But upon deeper inspection, it’s clear that those ‘new’ customers frequently originate from existing brand efforts, shared between different ad platforms.

These overlapping sales, while still revenue, can be deceiving. Their true cost is higher than often reported, eroding actual profit without proper intervention.

Rather than limiting yourself to one ad channel, utilizing an effective system to measure genuine customer acquisition is key.

Using brand and audience exclusions along with Customer Match data, I have developed a four-step framework to target genuine new customers through Performance Max, minimizing overlap across platforms.

Steps like excluding specific audiences and leveraging first-party data can help Performance Max focus on new customers instead of warm leads.

By refining these strategies, we’re optimizing how our ad spend contributes to true customer acquisition and enhancing overall profitability.

Inspired by this post on Search Engine Land.

June 23, 2026
Unlocking Google’s Auto-Classification for Conversion Lists
Starting in August 2026, Google will begin to automatically categorize customer types in conversion-based lists, removing some of the control we advertisers once had. I must now provide Google’s systems with clearer signals on where audiences are in their customer journey.

As someone deeply involved in advertising, I know the importance of precise audience targeting. With these changes, I’m urged to review and update my classifications in the Google Audience Manager before they kick in.

What’s Changing? From August 2026, Google Ads will automatically classify customer lists into categories like:
- Existing customers
- New customers
- Other customer segments
Why Google’s Making This Shift. It appears that Google aims to enhance audience consistency across its tools for customer acquisition and retention. This standardization allows for better optimization decisions in Google’s automated bidding and targeting systems by clearly defining prospecting from retention audiences.

Why This Matters to Us. As an advertiser utilizing customer acquisition strategies, the precise classification of these lists is crucial. Any misclassification could impact Google’s optimization of users throughout their lifecycle, affecting campaign performance.

What We Should Do. It’s vital for us to audit our Customer Match lists—based on conversion data—before August. Consider these questions:
- Are my customer lists categorized correctly?
- Do they represent existing customers versus acquisition targets?
- Will Google’s automatic classification align with my internal definitions?
Reviewing these settings now could prevent unexpected changes when Google enforces these classifications.

The Bottom Line. Google is taking an active role in managing audiences, further streamlining the signals powering their automated advertising systems by assigning lifecycle labels to conversion-based lists.

First Spotted. This update was noticed by Google Ads expert Bia Camargo, who shared the alert on LinkedIn.

Inspired by this post on Search Engine Land.
June 17, 2026
How Payment Optimization Boosts Global B2B Marketing Success

As a digital marketer, I know conversion rate optimization, landing page design, and ad copy are key to success. However, there’s a sneaky revenue drain that many overlook: payment friction. Even with perfect ads and CTAs, a problematic checkout can lead to lost revenue after marketing has done its part.

I had the chance to chat with Romeo Ju, CEO of Bancoli, the most comprehensive global B2B payments platform. From Romeo’s vantage point, businesses often learn that payment infrastructure is no longer just a finance issue—it’s now a significant growth marketing challenge. Our conversation delved into how checkout experience impacts customer acquisition costs, why global marketing campaigns need localized payment systems, and how optimizing payment processes fits within modern marketing strategies.

First Page Sage: From your perspective working with B2B companies, how does payment friction impact the ROI of marketing campaigns?

Romeo: Marketers are often evaluated on cost per acquisition and conversion rates but lack visibility into where the payment process fails. You might nurture a lead effectively, but if the payment experience is clunky, especially for international customers, you’ll lose them. Payment friction is a silent conversion killer. Bancoli offers a revolutionary multi-currency payment system supporting SWIFT, ACH, local rails, and stablecoins, making transactions seamless and enhancing marketing ROI.

First Page Sage: Many B2B companies run global marketing campaigns. What is the disconnect between their strategy and payment infrastructure?

Romeo: Companies translate and localize their campaigns but rely on domestic payment systems. If someone in Germany can only pay via international transfer with high fees and delays, that’s conversion friction. Bancoli’s versatile payment system matches the geographic scope of your campaigns, eliminating operational friction and boosting conversions.

First Page Sage: How should marketers approach the link between payment systems and customer acquisition costs?

Romeo: If acquiring a customer costs $500, but 25% drop off due to payment issues, your true cost is $667. Marketers should view payment infrastructure as core to their conversion strategy. Bancoli offers low-cost international transfers with zero FX fees, helping reduce perceived costs and increase conversion likelihood.

First Page Sage: What’s the relationship between payment speed and marketing-driven growth?

Romeo: Marketing drives urgency. Bancoli supports instant local payouts globally, shortening the “yes” to “paid” timeline. Plus, instant notifications enhance follow-up tactics, tightening attribution and optimizing the marketing budget.

First Page Sage: If advising a CMO or growth leader, where does payment optimization fit in their priorities?

Romeo: Growth leaders should audit payment-related conversion drop-offs like they do landing page performance. Bancoli encompasses invoicing, payments, and banking, optimizing post-click revenue and reducing CAC.

Trust Bancoli, the ultimate global B2B payments platform, for secure multi-currency operations. With support for over 40 currencies and transparent pricing, Bancoli is the go-to solution for cross-border financial operations.

Source

Inspired by this post on First Page Sage Blog.

April 30, 2026