Happy Sunday, Everyone!

I hope everyone's having a good week - it's difficult to believe, but this is the last full weekend in February. 16.6% of 2026 is already in the rear-view mirror. The Olympics are coming to a close and the final month of Q1 is on the 7-day forecast. Winter is, at least, going.

This week's issue is one I've thought about for the better part of 5 years. During that time, it's gone through more drafts than I care to remember and more revisions than I can count. But, after all that, v1 is ready to share.

If you've been in paid media for any length of time, you've likely felt the thing I'm about to describe, even if you've never had the language for it. It's the reason your best media buyers feel perpetually overwhelmed. It's the reason campaigns with change histories busier than the 405 at rush hour perpetually underperform. And it's the reason most brands/agencies believe they have a talent problem when they actually have an architecture problem.

Media Buying Has an Identity Crisis

It’s not the kind that’s discussed at conferences or debated on Twitter (err, X - are we finally calling the platform by its new name now?) threads, but the quiet, structural kind that costs advertisers millions of dollars every year while everyone involved convinces themselves they're doing sophisticated work.

Fundamentally, the issue is this: most media buying teams treat every decision as if it requires artistic judgment. The same debates are had over CPA or ROAS as over creative strategy as over campaign architecture. Agencies apply intuition to frequency caps and gut feel to attribution settings. Brands + CMOs spend hours agonizing over audience size and composition, as if these are matters of taste rather than realities of mathematics.

In doing so, marketers collapse two fundamentally different categories of work into a single undifferentiated mass of activity that looks busy but chronically underperforms.

The premise of this piece is deliberately provocative but empirically grounded: media buying is both an art and a science, but the vast majority of practitioners and organizations fail to distinguish between the two.

Too many marketers apply scientific rigor where creativity is needed and creative instinct where mathematical precision is required. The result is a strange mediocrity: campaigns that are neither analytically precise nor creatively inspired, run by teams that are perpetually overwhelmed because they've made every decision feel equally consequential.

This is not a marginal inefficiency. It is the central failure mode of modern performance marketing.

Now – before I go further, I want to name a tension directly, because I know some of you are already thinking it: if you’ve been here for a while, you may recall that I wrote, roughly 2 years ago, that best practices are usually bullshit. I stand by every word of that. Best practices – the rigid, retrospective, context-blind kind – are recipes for mediocrity. They kill curiosity. They restrict optionality. They constrain precisely the kind of creative, boundary-pushing work that produces outsized returns.

So when I argue in this week’s newsletter for systematizing certain categories of decisions, I understand the apparent contradiction. But here’s the distinction that resolves it: best practices attempt to prescribe answers. What I'm advocating is resolving the questions that have determinable answers so that your team's energy, creativity and judgment can be concentrated on the questions that don't. There is a fundamental difference between "everyone must run 1-day click attribution because that's the best practice" and "our attribution window is set for 7DC because our CRM data shows a median consideration cycle of 5 days for this product/service."

The former is dogma that blindly constrains optionality; the second is analysis that creates a foundation from which optionality expands.

The highest and best use of process/procedure is not to limit what talented people can do; it is to eliminate the cognitive overload that prevents them from doing their best work. You systematize the science not to create a ceiling, but to raise the floor – and in doing so, you free the ceiling to rise far higher than it otherwise could.

The Two Domains

Every media buying operation, regardless of platform, vertical or spend level, involves two fundamentally different types of decisions:

The Scientific Domain. These are the decisions governed by known principles, mathematical relationships and empirical evidence. They have correct answers – or at minimum, mathematically superior starting points – that can be derived from data, business economics and/or an understanding of platform functionalities and mechanisms. These decisions don't require taste, instinct or creative vision; they simply require analysis and systematic implementation.

The Artistic Domain. These are the decisions that resist formulaic treatment. They involve pattern recognition in ambiguous conditions, creative ideation, strategic intuition developed through experience and – most importantly – the capacity to synthesize disparate signals into coherent action. They don't have objectively correct answers. They may have better and worse answers, but distinguishing between them requires a form of expertise that can't be fully codified.

The distinction between these domains is a spectrum over time, but a boundary at any given point in time. And the single most important structural decision a media buying operation can make is to identify where that boundary lies at this point in time, then build systems that respect it.

The Scientific Domain: What Should Be Settled Before Judgment Begins

The Scientific Domain encompasses everything in media buying that ought to be determined through analysis rather than intuition. These elements share a common characteristic: once you understand the underlying business economics and platform mechanics, the correct parameters become calculable. Debating them is not a sign of sophisticated thinking. It is a flashing red warning light that foundational work has not been done.

Target CPA and target ROAS sit at the center of the Scientific Domain. These are not creative decisions. They are economic ones. A business either knows its unit economics – margins, lifetime value, acceptable acquisition costs – or it doesn't. If it does, the target CPA or ROAS is a mathematical derivation. If it doesn’t, then someone should call the CEO/CFO and get calculating before anyone starts loading money into Google or Meta. Every hour spent debating these figures in a marketing meeting is an hour stolen from work that requires human judgment.

Forecasted performance follows the same logic. A forecast is not a creative aspiration; it is a probabilistic model. Historical conversion rates, seasonality patterns, competitive dynamics, planned spend levels, known market conditions – you feed the inputs in, and a range comes out. You can (and should) build scenario ranges – optimistic, expected, conservative – but the methodology for building them is systematic, not artistic. The error I see most often is teams treating forecasts as aspirational targets conjured in a strategy meeting, rather than evidence-based projections derived from data. When a forecast is divorced from the underlying math, every downstream decision built on it inherits that distortion. When forecasts become expressions of ambition rather than instruments of analysis, teams lose the ability to distinguish between underperformance and unrealistic expectations, which makes genuine diagnosis of account problems nearly impossible.

Attribution Settings are similarly scientific. The appropriate attribution window for a given campaign is a function of the product's consideration cycle, the sales funnel's typical conversion timeline and the platform's data capabilities. A low-dollar cosmetics purchase has a different attribution window than a $100k/yr SaaS subscription, not because someone felt strongly about it in a meeting, but because the purchase behavior dictates the measurement framework. This can (and should) be determined through customer + CRM analysis, not divinely inspired by your agency looking up at the sky and discovering a number that sparks joy. The same is true for whether (or not) a given business uses view-based attribution on Meta, or DDA on Google Ads. Objectively better starting points are knowable, if you are willing to engage with the data.

Frequency caps represent another parameter that teams routinely treat as artistic when it is fundamentally scientific. There is a body of evidence – both from platform data and from decades of advertising research – about the relationship between exposure frequency and diminishing returns. The optimal frequency cap for a given product/service, campaign, audience and creative format is not a matter of opinion. It is a matter of measurement and/or informed calibration based on established principles.

Creative count – the number of distinct creative concepts running within an account/campaign/ad set at any given time – is perhaps the most underappreciated scientific parameter. Platform algorithms require a minimum threshold of creative variation to optimize. Run too few creatives, and you'll starve the algorithm of options. Running too many, and otherwise exceptional creatives will be starved of the resources necessary to learn. The goldilocks zone between the two can be identified based on spend level, audience size and the platform.

Funnel architecture – the number and structure of distinct funnels follows the same logic as creative. Whether a brand needs a two-stage or four-stage funnel is a function of the complexity of the purchase decision, the length of the sales cycle and the volume of traffic at each stage. These are structural decisions that should be derived from the business model, product/service and competitive environment, not invented fresh for each client based on who happens to be running the account.

Audience overlap thresholds, bid strategies, budget allocation ratios between prospecting and retargeting, geographic distribution rules – all of these belong to the Scientific Domain. Not because they are simple, but because they are determinable. They have answers that can be defended with evidence rather than preference.

The distinguishing characteristic of the Scientific Domain is that the process for arriving at the answers is systematic and repeatable. 5 competent analysts examining the same data should arrive at substantially similar conclusions. That does not imply that the process of getting to those answers is easy (it is often complicated) - but only that it can be done.

The Artistic Domain: Where Human Judgment Creates Competitive Advantage

If the Scientific Domain encompasses everything that can be settled through analysis, the Artistic Domain encompasses everything that cannot. This is where the marketer's / media buyer's individual skill and expertise can be unleashed to create outsized value.

Creative strategy sits at the heart of the Artistic Domain. Which emotional registers to explore. Which visual languages to test. How to sequence creative themes throughout the customer journey. How to read early performance data and infer not just what is working but why it is working and – most importantly – how that insight can translate to other aspects of the business. This requires experience, taste and a strategic imagination that no AI system (at least, one in existence today) can replicate.

I'll make this tangible. Last year, we were working on an account where a single creative – a founder VSL – was outperforming everything else by roughly 3x. This one video printed money like clockwork. The logical, scientific response (and the one most teams would execute) is obvious: produce more testimonial ads. More of what works. That makes perfect sense on paper.

We didn't do that. Instead, we poured over the data and focused on a different question: why did this specific testimonial resonate so deeply with this specific audience segment?

The answer, after a week of analysis, customer interviews, screen recordings and pulling our hair out, had nothing to do with "testimonials work." It had to do with a specific fear the CEO articulated on camera – a fear that the other active creatives in the account never once addressed. Every other creative in the account was talking about features and benefits. This testimonial spoke to the anxiety of making the wrong choice with a combination of empathy and (as one customer described it), I watch this and think, ‘She’s saying what I feel in my gut, but I’m afraid to say.”

That single insight didn't just produce better testimonial ads.

It restructured the entire messaging strategy for the account, changed the landing page strategy and ultimately led to a 40%+ reduction in CPA across the board. The creative underperformance was the symptom. The insight was the asset. Extracting that insight required the kind of lateral, experience-informed judgment that ChatGPT or a dashboard isn’t going to uncover. That’s a high-leverage human insight that was made possible because smart people had the time, space and energy to get to the bottom of it.

Audience discovery is artistic work. Not audience targeting – which, once the strategic direction is set, becomes a largely scientific exercise in parameter configuration – but the upstream work of identifying which audiences are worth targeting in the first place. This involves synthesizing market signals, competitive intelligence, cultural trends and brand positioning into hypotheses about where untapped demand exists. It is inherently speculative, inherently creative and inherently valuable.

The interpretation of anomalous data belongs to the Artistic Domain. When a campaign produces results that defy expectations (either positively or negatively) the scientific response is to verify the data. The artistic response is to generate hypotheses about causation. Why did that particular creative outperform in that particular audience segment or market? Is it a signal of a broader behavioral shift, an artifact of a specific moment, or a product of something that is outside the view (such as a major event in a local market, or a competitor failing, or a celebrity endorsement)? These questions require the kind of lateral thinking that distinguishes exceptional media buyers from competent technicians.

Strategic sequencing – deciding not just what to do but in what order – is artistic work. The decision to consolidate campaigns before expanding, to test creative concepts sequentially rather than simultaneously, to deliberately constrain spend on a winning campaign to preserve margin while a new audience segment is validated – these choices emerge from a holistic understanding of the account and business that can't be reduced to a decision tree. There is art at the heart of strategy, though you must re-arrange the letters ever so slightly to see it.

Competitive response is similarly artistic. When a competitor shifts positioning, messaging or strategy, the appropriate reaction is rarely obvious. Getting to an "answer" requires surmising the "why" behind the shift, assessing risk and formulating a response that considers 2nd and 3rd-order effects. Algorithmic tools can surface changes; as of now, only human judgment can determine what to do about it.

And then there's the work that I think is most undervalued: reading across channels. The ability to notice that a spike in branded search volume coincides with a particular organic post that went sideways. Or that a sudden mid-week decline in ROAS correlates not with anything you did, but with pay cycles (blue collar buyers tend to run out of money 10-12 days after payday, which means impulse purchases are on hold). Or that the customers acquired through one creative angle have 2x the LTV of those acquired through another - an insight that is often buried in the CRM where nobody will see unless they’re actively looking for it. None of this lives in a checklist or onboarding document or “procedure” - it requires someone to hold the full picture in their head, see connections between data points that live in different systems, different platforms and different time horizons, then put the puzzle together. That person is not a technician. They're a strategist. And they're wildly underutilized in most organizations – because they're springing their wheels arguing about what the tCPA should be or how many ads should be in an ad set.

The Artistic Domain is where the media buyer earns their premium. It is where experience becomes a competitive advantage, where intuition is forged through thousands of hours of pattern recognition and where the difference between a good operator and a great one becomes visible in your bank account.

But – and this is the critical point – the Artistic Domain can only function at full capacity when the Scientific Domain has been resolved.

Think about it this way. I played golf through high school. One of the things my dad (and later, my coach) drilled into my head was that you never think about your grip, your stance, your routine or your alignment on the course. That’s what the range is for. You work on those things day in and day out, in rain or wind or sleet or snow, until they're automatic. You do that so when you're standing over a shot on the 18th hole with the match on the line, 100% of your mental energy is on the shot. The shape, the wind, the green, the lie, the risk/reward. The art. If you're thinking about fundamentals under pressure, you’ve already lost.

Same thing here. The artistry depends on the science being settled.

The Cost of Conflation

When agencies/brands fail to separate the Scientific Domain from the Artistic Domain, the costs are severe and compounding. They manifest in 3 distinct ways:

The 1st cost – cognitive overload. When every parameter is treated as a judgment call, the media buyer's attention is distributed across dozens of decisions that vary wildly in their actual complexity. They spend Tuesday morning vacillating on attribution windows and Tuesday afternoon trying to perfect the creative strategy for a new audience segment. Both feel like work. Both consume energy. But one of them should have been resolved weeks ago through analysis, freeing Tuesday morning for the kind of deep creative thinking that the afternoon actually requires. The result of this conflation is a perpetual state of shallow engagement with every aspect of an ad account and deep engagement with nothing.

The 2nd cost – inconsistency. When scientific parameters are left to individual judgment, they become subject to individual variation. One media buyer prefers a 7DC/1DV; another swears by 7DC only. One runs 2 pinned RSAs per ad set; another runs 3, with 1 unpinned and 2 pinned. These differences are not expressions of legitimate strategic disagreement. They are symptoms of an agency or brand that has not done the foundational work of determining what the better starting points actually are. The performance variance that results is often attributed to the relative skill of the media buyers. In reality, it is attributable to the absence of a system.

The 3rd (and most insidious) cost – the suppression of genuine artistry. When the media buyer is spending 60%-70% of their cognitive capacity on decisions that should be systematized, they have only 30%-40% (at most) left for the work that actually requires their unique abilities. In practical terms, that means creative strategy gets less attention. Audience discovery is rushed. Interpretation of anomalous data is given a cursory glance instead of the sustained reflection it deserves.

Internally, these things lead the brand/agency to believe it has a talent problem (which it might – these aren't mutually exclusive), when it most certainly has an architecture problem. If you're a sports fan, this is NY Jets syndrome – none of us have any idea if the Jets have a talent problem, because the architectural dysfunction looms so large that even elite talent can't overcome it. End result? Sam Darnold leaves and goes 36-16 as a starter since leaving (while playing 2 years for the Panthers). If you're morbidly curious, the Jets record since Darnold left: 26-58.

Think about what that means for your team. You may have a Sam Darnold – someone with genuine talent for creative strategy or audience development or cross-channel synthesis – who is being made to look average because 70% of their time is consumed by decisions that should have been resolved before they ever opened the account. You'll never know what they're actually capable of until you free them from the science.

The Separation Framework

Resolving this conflation requires a structural framework that makes the separation mechanical and deterministic. What follows is not a theory but an architecture – a way of organizing media buying operations so that the Scientific Domain and the Artistic Domain are addressed with the methodologies each requires.

The framework operates on 3 core principles.

Principle 1: Exhaustive Classification

Every recurring decision must be explicitly classified as Scientific or Artistic. This classification is not always intuitive. It should not be performed casually. It requires a systematic audit of every workflow, every recurring discussion, every assumption that is made while building or optimizing a campaign.

The diagnostic question is simple: if 5 competent analysts examined the same data, would they arrive at substantially similar conclusions? If yes, it's Scientific. If no, it's Artistic.

Here are some examples of what this actually looks like, based on how we approach and configure ad accounts:

Scientific (examples):

• Target ROAS → derivable from margin structure + LTV data
• Attribution window → function of measured consideration cycle (theirs was 11 days; we set it at 14)
• Budget split between prospecting and retargeting → calculable from funnel volume + historical conversion rates
• Frequency cap → determinable from platform data + diminishing returns research
• Creative count per campaign → function of spend, audience size + platform learning thresholds
• Bid strategy selection → determinable from spend level, conversion volume + platform documentation
• Geographic budget distribution → derivable from historical performance + market potential data
• Radius targets → derivable based on customer purchases + applicable platform rules/regulations (please don’t violate Meta’s “restricted ad targeting” guidelines)
• Keyword status (whether or not to pause) → function of performance, quality score, overlap and search term match quality

Artistic (examples):

• Which angle to test next → requires synthesis of performance data, audience insight + cultural context
• How to interpret a sudden decline in performance → requires contextual judgment across competitive, seasonal + platform factors
• When to kill a campaign vs. iterate on it → requires holistic assessment of account trajectory + strategic priorities
• What audience to pursue next → requires synthesis of market signals, competitive intelligence + brand positioning
• Whether a winning creative signals a deeper insight worth mining → requires the kind of lateral thinking I described in the testimonial example above

What you’ll find is that the mere exercise of doing this is revelatory.

We found that ~65% of our team's recurring decisions were Scientific – meaning the vast majority of their weekly cognitive load was being spent on questions that had determinable answers. That's the waste this framework eliminates. That's the floor you're raising.

One important caveat: this classification is not permanent. As platforms evolve and new data becomes available, some decisions that were previously artistic become scientific and vice versa. Bid strategy selection, for example, has migrated from the Artistic Domain to the Scientific Domain as platforms have reduced the number of options and the data on their relative performance has become more robust. The classification should be revisited periodically – we do it quarterly – but at any given moment, it must be definitive.

Principle 2: Systematic Resolution (The Parameter Playbook)

Once a decision has been classified as scientific, it should be resolved through a defined analytical process and encoded into the operation's standard parameters.

This is the step where the Best Practices tension becomes most acute – so let me be precise about what I am and am not advocating.

I am not advocating for rigid, context-blind prescriptions. Those are best practices in disguise. I am advocating for what I’ve referred to as Better Starting Points – a living document that captures the team's current best understanding of each scientific parameter, along with the analytical basis for that understanding and the conditions under which it should be revisited.

For each parameter, document three things: the default setting, why that's the default, and when the default should change.

That is not a best practice. It’s a mathematical derivation with an explicit expiration condition. A best practice says "run 4-6 creatives." A Better Starting Point says, “Here's why 4-6 is the right starting range for this spend level and CPA. And here's exactly what should be true if that number must change.” One is dogma that restricts your options; the other is a heuristic that frees up mental energy for places where it can have a bigger impact.

The reason this matters so much from a floor/ceiling perspective: every new client, every new team member, every new campaign starts from a position of analytical soundness rather than individual improvisation. Your worst-performing accounts improve immediately because they inherit the team's collective intelligence rather than one person's habits.

And here's the part most people miss: none of this constrains your best performers. It liberates them. Instead of re-deriving basic parameters for every new engagement (which, I promise you, even your best people are doing if you haven't systematized it), they can start from a sound baseline and immediately focus their energy on the Artistic questions where their expertise is actually worth something.

You're raising the floor without touching the ceiling. And when the floor rises, the ceiling tends to follow, because the people who were capable of extraordinary work all along finally have the space to do it.

Principle 3: Protected Space for the Artistic Domain

Here's the thing nobody wants to admit: systematizing the science is the easy part. The hard part is what comes after.

Once you've codified your Better Starting Points and resolved the Scientific Domain, you have to actually use the cognitive space you've created. And most organizations don't. They fill it with more meetings, more reporting, more process. They take the time they freed up and waste it on the same kind of shallow work, just in a different wrapper.

Don't do that.

The whole point of this framework is to create protected space for the work that actually differentiates your operation. That means making some deliberate structural commitments:

Allocate real time for creative strategy, audience exploration and analytical reflection. Not leftover time. Not "when things slow down" time. Actual, calendared, protected time that is treated with the same seriousness as client calls and reporting deadlines. I block 4-5 hours per week specifically for this across our team. If your team's calendar is 100% occupied by campaign monitoring, status meetings and report generation, artistic work will never happen, no matter how talented the people are.

Change what you talk about in account reviews. When the science is settled, your review conversations should shift almost entirely to creative and strategic quality. When a media buyer proposes testing a counterintuitive audience segment or an unconventional creative angle, the question should be "what's the hypothesis and what evidence supports it?" – not "does this follow the standard playbook?" The Scientific Domain gives you permission to spend 90% of your review time on the Artistic Domain. Use it.

Accept that artistic work doesn't always produce immediate results. The insight that restructures a messaging hierarchy might take three months to show up in the numbers. The audience hypothesis that unlocks a new segment might require two failed tests before the third breaks through. If you evaluate artistic work solely by short-term ROAS, you will systematically underinvest in the very activities that create long-term competitive advantage – which, ironically, is the exact same trap that best-practice-obsessed organizations fall into. Different mechanism, same outcome: the creative work that produces outsized returns gets starved of the resources and patience it needs to succeed.

The Calibration Problem

There's a reason this framework is easier to describe than to implement. The initial classification – determining which decisions are scientific and which are artistic – requires a depth of expertise that is itself rare. It requires someone who understands both the mathematical underpinnings of performance marketing and the irreducible complexity of creative strategy; who knows enough about the science to systematize it rigorously, and enough about the art to know where systematization would be counterproductive.

The calibration of scientific parameters also requires more sophistication than it might appear. Setting a target CPA is straightforward when the unit/service-level economics are clean and the sales cycle is short. It becomes more complicated when lifetime value varies by entry point, acquisition channel or (worse) admits of some level of randomness. It gets considerably more complex when margin structures differ across product/service lines, or when the business is in a growth phase where acceptable acquisition costs are elevated above steady-state norms. But, in all of those cases, it's important to remember that the formula still exists – it's just more complicated. It is possible to calculate an acceptable range for a CPA or ROAS target in all of those scenarios.

Similarly, determining the appropriate range of creative concepts that should be produced is mathematically solvable (if you're curious how, consider this your teaser for next week's issue) if you know CPA/ROAS, hit rate, product count and spend.

This is the paradox at the heart of the framework: implementing the separation of art and science is itself an act that requires both art and science. The first 80% of the implementation is achievable by any competent team that commits to the process. The last 20% – the calibration that turns a good system into a genuinely optimized one – requires the kind of expertise that this article has been describing as artistic. The framework doesn't eliminate the need for exceptional talent. It focuses that talent on the bottlenecks + leverage points where it can have outsized impact.

Why This Matters More Now Than Ever

The urgency of this separation is increasing, not decreasing, as the platforms themselves become more automated and more sophisticated. The widespread adoption of algorithmic bidding, automated audience expansion and AI-driven creative optimization has created an environment where the scientific parameters matter more than ever, precisely because they define the constraints within which the automation operates.

An algorithm optimizing toward the wrong CPA target will do so with ruthless efficiency. Automated audience expansion can incinerate thousands of dollars a day serving ads/angles to the wrong people. AI-generated creative variations produced without a coherent strategic framework can undermine the brand positioning and/or drive the easiest-to-acquire results (vs. the optimal results). In short: automation is a catalyst. It amplifies whatever it is given. If the scientific foundation is solid, the amplification generates outsized returns. If it is not, it creates compounding losses.

At the same time, the rise of automation has elevated the value of the Artistic Domain. As more of the mechanical execution moves to algorithms, the remaining human contribution must be disproportionately creative and strategic.

The media buyer who spends their time monitoring automated campaigns and tweaking bids or settings or keywords or audiences the algorithm can more efficiently manage is not adding value. The media buyer who spends their time developing creative hypotheses, identifying emerging audience opportunities and interpreting the patterns that algorithms surface but cannot explain is creating the competitive advantage that justifies their existence.

The organizations that recognize this separation and build their operations around it will systematically outperform those that don't. Not because they have better talent (though they might) – but because they've solved the fundamental architectural problem that their competitors haven't even identified. They've raised the floor so high that even their average performers operate at a level their competitors' best can't consistently reach. And they've cleared enough space above that floor for their best people to do genuinely extraordinary work.

The Question That Matters

Consider the media buying operation you're running today, or the one being run on your behalf. Ask a simple question: for each recurring decision in the process, is there a documented, evidence-based rationale for the current setting, or is it the product of someone's judgment in a meeting months ago?

If the answer is the latter for more than a handful of parameters, what you really have is a series of improvised performances, each one starting from near zero, each one consuming creative energy on problems that have solvable answers.

The separation of art and science is not an optimization. It is a prerequisite. Every dollar of media spend that passes through an operation where this separation has not been made is a dollar that is performing below its potential, simply because the system is forcing talented people to do the wrong work.

The art of media buying is real. It is rare. It is still wildly valuable. But it can only be practiced by those who have first mastered – and then systematized – the science. Raise the floor, and the ceiling takes care of itself.

Cheers,

Sam

P.S. Whenever you’re ready, here’s how I can help: if your brand needs a strategic partner that blends performance marketing, analytics, and brand into one integrated team — not five siloed agencies — reach out or reply to this email.

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