About once a week, someone—usually a marketer in Patagonia fleece or a VC with a vocabulary built entirely out of Gartner terms—leans in with a smile and asks:
“Pesach, why are you so obsessed with attribution? Shouldn’t we just be happy if the numbers go up?”

And I smile back, the kind of smile you give when someone asks if OJ might still be innocent.

Here’s the thing:
Attribution is not some esoteric backroom concern for marketing nerds.
It’s not a footnote.
It’s the whole damn foot.

It determines who gets paid, who gets promoted, who gets fired. It decides what channels get more budget, which agencies stay on the roster, and which teams suddenly find themselves explaining why “brand awareness” doesn’t show up in a spreadsheet.

Attribution isn’t just data. It’s survival.

And if you think it’s just a clean, logical process of tracking clicks and conversions—bless your heart. You're probably still using Google Analytics 4 like it tells the truth.

Let’s rewind. Picture it: the early 1900s. No TikTok. No cookies. Just department stores, broadsheets, and John Wanamaker, who accidentally became the patron saint of marketing insecurity when he said:“Half the money I spend on advertising is wasted; the trouble is I don't know which half.”

It was a good line—so good it got tattooed onto every pitch deck from 1999 to 2015.
But what Wanamaker really gave us was a neurosis:
the constant, gut-wrenching need to justify our existence.

Enter digital attribution—the answer to all our problems. Finally, we were told, there’s a way to track every impression, click, and conversion down to the millisecond.
Modern tools like Google Analytics, CRMs, and marketing automation platforms sold us a dream:
perfect clarity.

Every campaign would be traceable.
Every conversion would have a breadcrumb trail.
Every marketer would become a data wizard, equal parts Don Draper and Nate Silver.

Spoiler alert: that didn’t happen.

At first, attribution felt like magic.
You could actually show your boss that your webinar drove 37 demo requests.
You could optimize campaigns in real-time.
You could defend your budget with more than vague phrases like “brand halo” or “impression lift.”

For a glorious moment, marketing got a seat at the grown-up table.
CMOs became CFOs’ best friends.
Dashboards became gospel.

But like any tool with too much power, attribution started to warp the culture around it.

Instead of making better decisions, teams started building campaigns for the dashboard.
Instead of long-term brand building, we got lead spam and gated PDFs.
Instead of collaboration, we got scorekeeping and turf wars.

We stopped asking, “What’s working?”
And started asking, “Who can claim the win?”

And here’s where the wheels really fall off.

You see, platforms figured out the game.
They’re not just neutral pipes—they’re your ad salesmen, your judge, jury, and executioner.

Google says it drove the conversion.
Meta says it did.
Amazon claims it.
Your affiliate software is raising its hand, too.

Four sources. One sale. All of them taking full credit.

If your marketing report looks like a kindergarten play where everyone gets a trophy, congrats—you’re doing attribution the “modern” way.

And this isn’t just accidental. These platforms design their attribution models to make themselves look good. Their dashboards are basically LinkedIn bios for ad performance: inflated, self-serving, and allergic to nuance.

Now let’s talk tech stacks. Or as I like to call them: marketing’s Tower of Babel.

You’ve got Salesforce, HubSpot, Segment, Marketo, GA4, 47 Slack integrations, and a haunted Excel sheet someone made in 2018 that still runs most of your quarterly board updates.

None of them talk to each other. All of them use different data definitions.
And half your customer journey? Happens in places you can’t even see.

Buyers listen to a podcast at the gym.
They read a LinkedIn post at 11 PM.
They hear your brand mentioned in a Discord thread you’ll never find.
Then they Google you and fill out a demo form, and Google waltzes in like it deserves a medal.

Your attribution model? It’s trying to stitch together this chaos with a handful of UTMs, an overworked pixel, and hope.

Here’s the final kick in the attribution teeth: it’s not just about data—it’s about culture.

Attribution broke GTM alignment.
Instead of unifying sales, marketing, and finance, it turned everyone into frenemies with competing spreadsheets.

Sales doesn’t trust marketing’s “influence.”
Marketing doesn’t trust sales to log their activities.
Finance doesn’t trust either of them and just wants to see an ROI over 3:1 so they don’t have to talk to investors about “soft metrics.”

Attribution should be the thing that builds trust. Instead, it’s the thing everyone uses to win arguments and dodge blame.

When everyone’s working off different models, when every team is using attribution as a shield rather than a compass, you don’t get strategy—you get chaos.

Because attribution, for all its flaws, still matters.
Because marketing without some form of measurement is just shouting into a void and hoping the void has budget authority.
Because if we don’t fix it, we’ll keep optimizing for the wrong things: clicks over impact, MQLs over actual leads, dashboards over decisions.

This isn’t about perfection. It’s about sanity.
It’s about making attribution a tool for insight, not a game for liars.

And in Part Two?
We’ll torch the sacred cows of attribution modeling.
First-touch, last-touch, multi-touch, magic-touch—we’re naming names.

Stay bold.
Stay curious.
And for heaven’s sake, stop trusting your attribution model like it’s Torah miSinai.

Let’s get one thing straight: if you're using attribution to play Hunger Games with your sales team over who gets credit for a deal, you're already doing it wrong. Drew Smith isn’t here for your dashboard drama or your CMO-vs-CFO cage match. He’s here to fix your marketing measurement problem—one painful truth at a time.

Drew’s core rule? If your attribution sucks, it’s probably because you’re asking dumb questions. No model can save you from confusion dressed as data. Instead of installing yet another shiny MarTech toy, start by figuring out what you actually need to know. Tools follow strategy—not the other way around.

Let’s be real: most marketers haven't touched a regression model since the SATs. And Drew’s not judging. He just thinks you need a little statistical therapy. Coaching, training, enablement. Less panic, more logic. Because “creative instincts” won’t help you untangle a marketing funnel that looks like a Jackson Pollock painting.

First touch. Last touch. Multi-touch. Drew’s verdict? None of it matters unless it fits your business. If you’re closing deals faster than a Tinder date gone right, congrats—first touch will do just fine. But if you’ve got 12-month cycles, enterprise buyers ghosting you mid-funnel, and a graveyard of “not ready” leads—yeah, you’ll need the big guns.

This is where Drew goes full savage. If you're using attribution to prove marketing “deserves” more credit than sales, you’re digging your own grave. “You will lose,” he warns. And once sales starts poking holes in your data (hello, ‘that was my cousin!’), it’s all downhill. Attribution is for making smarter decisions—not winning office popularity contests.

People are still acting like “dark funnel” is some mysterious force conjured by AI. Drew’s like: it’s just word of mouth, people. Want to track it? Ask “Where’d you hear about us?” Simple. It won’t be perfect—humans are garbage narrators—but it’s the only shot at real causality.

That’s Drew’s mantra for any tracking strategy. Whether it’s multi-touch, self-reported, or some Frankenstack of both—it better drive real insights. Not just more dashboards. Not just more politics. If you’re not getting to that sweet, satisfying “aha,” you’re wasting everyone’s time.

Bottom Line:
Attribution isn’t magic. It’s messy, contextual, and 100% political if you let it be. But if you treat it like the sharp decision-making tool it’s meant to be—not a budget-saving weapon—you might actually start doing better marketing.

In marketing boardrooms from New York to San Francisco, attribution remains one of the most polarizing words in the business lexicon. It masquerades as a neutral accounting function—a clinical analysis of which advertising or marketing effort contributed most to a sale. But underneath its spreadsheets and dashboards lies something much more consequential: a battleground for resources, recognition, and strategic direction.

Marketing attribution has become, paradoxically, both over-engineered and under-effective. While the tools, models, and platforms used to measure performance have grown more sophisticated, the conclusions drawn from them have not. In many organizations, attribution models are not being used to illuminate decision-making but to validate decisions already made—sometimes based on flawed assumptions, platform bias, or incomplete data.

In this installment of our five-part series on attribution, we turn a critical eye to the prevailing models that continue to dominate marketing analytics—and the structural failures that render them insufficient at best, and misleading at worst.

Let’s begin with the usual suspects.

First-Touch Attribution assigns all credit to the very first marketing interaction a prospect has with a brand. It is often used in top-of-funnel analyses or demand generation contexts. The idea is to understand what initiated interest. That’s useful—until it’s misapplied as a full-funnel measurement tool.

Last-Touch Attribution does the opposite. It awards 100% of the credit to the final interaction before conversion. It has long been the default model for many digital platforms, including Google Ads, where the point of conversion is easily tracked.

While both are simple to implement and easy to explain, their accuracy is questionable. Most customer journeys today are non-linear, multi-device, and riddled with off-platform interactions. Assigning full credit to a single touchpoint—early or late—is analogous to declaring the final play of a football game the sole reason for victory.

In response to these limitations, more nuanced models have emerged:

Each model has its advocates. Each has its flaws.

The linear model assumes every interaction matters equally, which rarely aligns with how influence actually works. The U- and W-shaped approaches assume that first contact, lead capture, and opportunity stages are inherently more valuable, which may not be true in all journeys. Time decay heavily favors short buying cycles, potentially undervaluing the crucial early-stage content or brand awareness that may have made the later conversion possible. And while custom models offer flexibility, they often rely on proprietary assumptions or limited datasets that are no more “scientific” than the rule-based models they replace.

Perhaps the most dangerous aspect of these models is not their inaccuracy, but their presentation. Attribution models are presented with mathematical confidence—percentages, pie charts, weighted formulas—that suggest objective truth. The danger lies in the perceived authority of these numbers.

But the models are only as good as the data feeding them. And that data is often fragmented, siloed, or riddled with gaps. Offline interactions are rarely captured. Cross-device behavior is inferred rather than observed. Third-party cookies are disappearing. Messaging platforms, dark social, podcasts, Slack groups, and YouTube rabbit holes—all vital parts of the modern buyer journey—are invisible to most attribution systems.

A clean pie chart may reflect little more than what’s easiest to track, not what’s most influential.

Consider the following buyer journey:

Depending on the model—and the internal politics—any of those five touchpoints might be deemed “the reason” the sale happened. But the reality is more nuanced. Each touchpoint played a role. Some initiated awareness. Some reinforced interest. One served as the final nudge.

Yet attribution models are rarely configured to reflect collaborative causality. Instead, each platform—from Google to Meta to the webinar vendor—claims 100% of the credit for that same conversion. This multiplicity of claims not only leads to internal disputes but also encourages double-counting in reporting and inflated perceptions of effectiveness.

It’s not just a technical issue. It’s a behavioral one.

Attribution models are used to allocate budget and measure team performance. And as with any measurement system, what gets measured gets managed—sometimes in counterproductive ways.

A model that heavily favors last-touch attribution, for example, will incentivize marketers to pour spend into retargeting campaigns aimed at people already in the funnel. The result? Campaigns optimized to capture credit, not to create demand.

This explains why we see disproportionate investment in tactics like cart abandonment emails, brand search, and bottom-funnel display ads—channels that are efficient at capturing existing demand but contribute little to creating new demand.

Over time, this misalignment leads to a hollow funnel: strong-looking performance on paper, but eroding pipeline quality and stagnant growth in reality.

The problem isn’t that we have models. It’s that we insist on choosing just one—and then using it as gospel.

What’s needed is attribution pluralism: an acknowledgment that no single model offers a complete view of buyer behavior. Instead of debating whether first-touch or time-decay is superior, teams should use multiple models in parallel, not to find one “truth,” but to triangulate insights.

Pair this with directional analysis of velocity, engagement depth, win rate trends, and qualitative sales feedback, and you begin to approximate a more accurate picture of what's working.

This isn’t easy. It requires cross-functional collaboration. It requires transparency around the limitations of each approach. And it requires that executives understand that marketing effectiveness is not a math problem with one answer—it’s a set of signals that must be interpreted with context.

In a digital world where visibility is increasingly constrained and buyer behavior is increasingly opaque, attribution models need to evolve—or at the very least, be humbled. We must move away from treating them as definitive and start using them as diagnostic inputs in a broader strategy conversation.

Attribution is not a scoreboard. It is a signal. And like all signals, it is susceptible to noise, distortion, and misinterpretation.

Until then, measure with curiosity, not certainty. And remember: if your attribution model gives everyone 100% credit, it’s not a model—it’s a mirage.

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