How to Talk to Your CFO About Marketing Measurement

How to Talk to Your CFO About Marketing Measurement

by | Aug 6, 2025 | Marketing, Measurement

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How to Talk to Your CFO About Marketing Measurement

peq brian pozeskyBy Brian Pozesky

You’re in the boardroom. The CMO asks for more budget to measure marketing performance. The CFO’s face goes blank. The tension is palpable. It’s the classic showdown: CMO vs. CFO.

In one corner, we have the CMO, eager to prove that marketing isn’t just a “cost center” but a growth engine. In the other, the CFO, armed with a calculator, laser-focused on the bottom line, asking the age-old question: “Show me the money!”

Sound familiar? Yeah, we thought so. But here’s the thing: marketing measurement doesn’t need to be a battle. In fact, with the right approach (and the right tools), you can speak the CFO’s language and win them over. Let’s dive into how.

1. Speak Their Language: Numbers, Not Feelings

The first step in talking to your CFO about marketing measurement is understanding their language. While the CMO is thinking in terms of brand awareness, engagement, and customer loyalty, the CFO is thinking in terms of ROI, incrementality, and data-driven decisions.

This is where Pēq comes in. With Pēq’s standardized measurement framework, you can easily show your CFO real, actionable data—not just fluffy metrics. The beauty of Pēq’s platform is that it connects the dots across all channels and retailers, providing a unified view of performance that’s comparable, transparent, and easy to digest. No more arguing over whether Amazon’s iROAS is comparable to Walmart’s. Pēq brings clarity.

2. Show Them the Impact (Not Just the Spend)

The CFO’s job is to keep the company’s financial health in check. When the CMO asks for more budget to measure marketing, the CFO wants to know: “What’s the return on this investment?”

Here’s where you get to bring out the big guns: incrementality. It’s not just about tracking clicks and impressions—it’s about proving that your marketing efforts are driving real business outcomes.

With Pēq, you can show how every dollar spent on media contributes to actual incremental revenue.

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Imagine this: you’re sitting across from the CFO, confidently saying, “Thanks to our new marketing measurement framework from Pēq, we’ve identified a 20% increase in revenue from our last campaign—across multiple channels, with data you can trust.” Now that’s a conversation the CFO can’t ignore!

3. Demonstrate the Power of Standardization

One of the biggest pain points for CFOs is inconsistent measurement across different marketing platforms. From Facebook to Google to Amazon, every platform has its own set of metrics, making it nearly impossible to compare apples to apples.

This is where Pēq’s standardized measurement steps in. Pēq ensures that every channel—whether it’s TV, paid social, or in-store promotions—is evaluated with the same logic, attribution assumptions, and normalization techniques. This means the CFO will no longer have to deal with fragmented data or guesswork. They’ll have the peace of mind knowing that marketing performance is being measured accurately and consistently.

4. Make It About Efficiency, Not Just Spend

CMOs are often told to do more with less. You want to show the CFO that better measurement means better efficiency. Instead of guessing where to allocate your media spend, Pēq helps you optimize your portfolio. By using real-time insights, you can confidently move your budget from low-performing channels to high-performing ones—without second-guessing.

This isn’t just about throwing more money at the marketing budget. It’s about making every dollar work smarter. With Pēq, you’ll be able to say, “We cut wasted spend by 15% last quarter by reallocating to high-ROI campaigns.”

5. The Final Pitch: Speak to the CFO’s Bottom Line

At the end of the day, the CFO is all about the bottom line. They want to know how marketing will drive growth—and they need clear, reliable data to back it up.

With Pēq, you can show your CFO that measuring marketing isn’t just a luxury—it’s a necessity. Standardized, transparent measurement not only drives better marketing decisions, but also makes your marketing team more accountable and efficient. You’re not just asking for more money; you’re asking for smarter, data-driven decisions that will ultimately contribute to the company’s growth.

So, Next Time You Walk Into That Meeting…

When you walk into that meeting with your CFO, don’t just talk about how “important” marketing measurement is. Show them how it’s going to make the company money—and make the CFO look good for approving it. With Pēq, you can transform marketing measurement from a mystery into a money-making machine.

And who knows? With the right data, you might just find that the CFO isn’t the one asking the tough questions anymore. 😉

Ready to get your CFO on board?

Start with a Free Incrementality Audit and discover where your marketing is really making an impact—and where it’s just making noise.

👉 Get your Free Incrementality Audit

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How I Learned to Stop Worrying and Love the Media Mix Model

How I Learned to Stop Worrying and Love the Media Mix Model

by | Jul 29, 2025 | Marketing, Media Mix

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How I Learned to Stop Worrying and Love the Media Mix Model

peq brian pozeskyBy Brian Pozesky

And why LOYALTY PROGRAMS and Media Mix Models aren’t enemies — they’re the ultimate allies.

It was a classic Slim Pickens moment. The marketer was riding a bomb straight into the chaos of the modern marketing war room—hat waving, influencer budgets misfiring, TV under review, and no one quite sure if paid social was delivering real incremental value or just good-looking CPMs. And loyalty programs? Sure, they clearly provided value. But in the larger fight where they really winning any battles against the enemy?

Around the table sat brand leads, media buyers, CRM strategists, and some very tired data scientists. In the center stood the CMO and CFO. The CMO, playing the part of George C. Scott, animatedly pointed at dashboards and cried, “But look at the big board!” The CFO, stone-faced, responded with the line heard in boardrooms everywhere: “You’re wasting money. You’re fired.”

At the heart of the tension was the Media Mix Model—complex, often misunderstood, and until recently, widely feared. Once considered a blunt instrument for evaluating traditional media, MMMs were thought to be incompatible with today’s digital-first, personalized marketing landscape. But as the dust settled, a realization took hold: the model wasn’t the enemy of loyalty, personalization, or performance. In fact, it was the missing link that could tie all three together.

 

Modern Media Mix Models have evolved far beyond their roots in TV, print, and radio. They now operate across a fragmented digital ecosystem, connecting data from open web activity, walled gardens like Facebook and Amazon, and even offline channels like in-store promotions and direct mail.

Powered by machine learning and real-time data ingestion, today’s MMMs can measure incremental impact across media types with a level of granularity that would have been unthinkable even a few years ago.

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And it’s precisely this granularity that reframes the relationship between MMM and loyalty.

Where marketers once had to choose between broad media strategies and highly personalized engagement, they can now align the two.

This isn’t just a theoretical synergy—it’s increasingly operational. Media Mix Model outputs now feed directly into the programmatic media supply chain. A model might identify that CTV or paid social is outperforming other channels in a specific region or demographic. Loyalty data can then provide the exact IDs and behaviors needed to define high-value audience segments.

Together, they enable marketers to not only allocate spend more effectively, but also to activate it more precisely.

In other words, Media Mix Models aren’t just compatible with loyalty—they’re becoming loyalty. The lines are blurring. MMMs now inform everything from channel mix to creative testing to individualized targeting. And loyalty data, once confined to retention programs, now helps fuel top-of-funnel performance and attribution. The result is a more integrated, closed-loop marketing system—one where macro strategy and micro execution finally speak the same language.

This evolution also means marketing teams are operating more cohesively. MMMs are no longer the exclusive domain of data scientists, buried in quarterly decks. They’ve become living tools that feed into daily marketing decisions. Brand, media, CRM, and finance teams now share a common view of what’s working, why it’s working, and how to optimize it.

Unlike the catastrophic ending of Dr. Strangelove, today’s marketers aren’t plunging into chaos. They’re steering the ship with data, insight, and increasing confidence. Media Mix Models and Loyalty Programs—once seen as separate, even conflicting forces—have become the marketing world’s odd couple: unlikely partners who, together, are more powerful than either alone.

It turns out the secret weapon wasn’t panic, or budget cuts, or viral stunts. It was understanding that strategy and personalization don’t need to be opposites. They just needed a better way to connect. And thanks to modern MMMs and loyalty data, that connection has finally arrived—with data-driven cowboy confidence leading the way.

Ready to Ditch the Dashboard Chaos?

Start with a Free Incrementality Audit and discover where your marketing is really making an impact—and where it’s just making noise.

👉 Get your Free Incrementality Audit

Join

Or Book a Demo Here

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Why Marketing Needs a Common Language for Measurement

Why Marketing Needs a Common Language for Measurement

by | Jul 15, 2025 | Marketing, Measurement

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Why Marketing Needs a Common Language for Measurement

peq rikki marlerBy Rikki Marler

No More Apples to Oranges

Let’s be real: Marketing measurement today is a mess. Everyone’s speaking a different language — and wondering why no one understands each other.

One platform says your campaign drove a 3.5x ROAS.
Another says it’s incremental.
A third just shrugs and gives you a PDF three weeks late.

Welcome to the Apples-to-Oranges Era of media measurement.

So… What’s the Problem?

You can’t optimize what you can’t compare.

Marketers are being asked to make million-dollar decisions across channels that don’t speak the same language — not in metrics, not in methodologies, not in timing, not even in logic.

One brand manager is looking at iROAS.
Another’s debating attribution windows.
The CFO wants “just one number.”
And the agency? They’re knee-deep in four dashboards and a spreadsheet that looks like it was built in 1997. It’s chaotic. It’s confusing. And it’s killing performance.

The Myth of “More Data=Better Decisions”

Here’s the truth no one wants to say out loud: More data doesn’t mean better decisions.
More comparable data does.

If one retailer defines incrementality based on store lift… And another does it using branded search uplift. Then what are you actually comparing?

Hint: nothing useful.

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This Isn’t Just an Ops Problem — It’s a Growth Problem

Inconsistent measurement doesn’t just make reporting a nightmare —
It blocks scale, wastes spend, and breaks trust across teams.

  • Media teams don’t know what to optimize.

  • Finance doesn’t trust the ROAS.

  • Leadership sees “growth” — but has no idea what’s actually driving it.

It’s not that marketers are flying blind. It’s that they’re flying with 10 compasses… all pointing in different directions.

What Marketing Really Needs: A Single Source of Measurement Truth

That’s what Pēq delivers.

We standardize how incrementality is measured — across platforms, campaigns, and retailers — so you can finally compare performance without decoding five different playbooks.

👉 Same test/control logic
👉 Same incrementality framework
👉 Same definitions for lift, iROAS, CPI — everywhere

No more apples to oranges. Just apples to apples. (And insights you can actually act on.)

Why It Works

When you use one measurement language:

  • Your media mix gets smarter — because you’re not second-guessing what worked.

  • Your reporting gets faster — no more waiting weeks for a post-campaign PDF.

  • Your team gets aligned — from marketing to finance to leadership, everyone sees the same truth.

And that truth? It drives better decisions. Period.

Final Thought

The next time someone says “we saw 400% ROAS on Platform X,” ask them:
Compared to what? Measured how? Normalized against what baseline? If they don’t have answers… it’s apples to oranges all over again.

It’s time to stop guessing.
It’s time to speak the same language.
It’s time to measure what actually matters.

Ready to See ML in Action?

Get a Free Incrementality Audit and discover how ML-powered measurement could unlock your media value—across every channel.


👉 Request Your Free Audit

Join

Or Book a Demo Here

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How Machine Learning Is Making Marketing More Measurable

How Machine Learning Is Making Marketing More Measurable

by | Jun 26, 2025 | Marketing, Machine Learning

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How Machine Learning Is Making Marketing More Measurable

peq rikki marlerBy Rikki Marler

The Problem with Traditional Marketing Measurement.

For decades, marketers have relied on fragmented metrics like last-click attribution or self-reported dashboards. They measured impressions, clicks, or linear spend—and built strategies on sand. But today’s multichannel, cookieless, fragmented landscape demands something far more robust.

That’s where machine learning comes in.

Why Machine Learning Is a Measurement Game-Changer.

1. Widespread Adoption & Competitive Edge

94% of organizations now use AI to prepare or execute marketing campaigns, and 88% of marketers rely on AI in their daily roles.

Among enterprises, 57% are actively deploying AI-based solutions. This isn’t just futuristic—it’s table stakes.

2. Real-Time Campaign Performance

ML-powered attribution shifts your view from “what happened” to “what caused it.” Beyond backward-looking MMM, AI-driven measurement offers continuous, same-day insights, enabling budget shifts mid-flight.

3. Predictive ROI and Media Mix Modeling

In retail, ML-enhanced MMM can forecast outcomes of spend changes and suggest optimal allocative shifts.

It identifies non-linear channel interactions, diminishing returns, and cross-channel lift—things simple models can’t see.

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4. Causal Lift and Incrementality at Scale

Causal machine learning (e.g., uplift models) is enabling actionable insights like: “Which coupon codes drive real incremental sales across segments?” Modern systems can isolate true lift even without real-world holdouts—essential in today’s privacy-first era.

Real-World Data That Validates the Shift.

Retailers using AI saw annual profit growth of ~8% compared to peers who didn’t. Nearly half of all businesses (48%) actively use ML/AI tools to maintain data accuracy.

61% of marketers now rate AI as the most critical component of their data strategy. These aren’t wild forecasts—they’re proof that machine learning works in marketing measurement.

What This Looks Like in Practice.

1. Unified Incrementality Measurement

Instead of trusting siloed platform ROAS, machine learning evaluates how each channel contributes lift, normalizing spend, seasonality, and cross-retailer dynamics.

2. Predictive Channel Allocation

You can simulate budget shifts: “What if we move €100K from Facebook to CTV this quarter?” ML predicts ROI based on historical patterns.

3. Continuous Trend Tracking

Forget monthly reviews—ML alerts marketing teams in real time when a campaign loses traction or a new “best channel” emerges.

4. Personalization & Behavioral Targeting

ML-based engines dynamically adapt messaging to user behaviors or segments, boosting relevance and conversion.

The Academic Layer: What the Research Shows.

Causal ML identifies which coupons truly increase demand—and for which audiences. Neural network models for conversion prediction accurately forecast who will purchase—before they even click.

These aren’t just sample cases—they show how deep learning is reshaping marketing attribution and conversion strategy.

The Bottom Line.

Machine learning transforms marketing measurement from reactive to predictive, from isolated channels to unified truth, and from guesswork to real ROI insight. No more siloed platforms, conflicting reports, or chasing incomplete data.

But ML only works when it’s layered on quality inputs and robust standardized measurement frameworks—which is why Pēq combines ML with rigorous data hygiene and unified incrementality logic.

In short: machine learning doesn’t just make marketing more measurable—it makes it measurable with integrity.

Ready to See ML in Action?

Get a Free Incrementality Audit and discover how ML-powered measurement could unlock your media value—across every channel.

👉 Request Your Free Audit

Join

Or Book a Demo Here

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Different Channels, One Truth

Different Channels, One Truth

by | Jun 24, 2025 | Marketing, Geolocation

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Different Channels, One Truth

The Case for Unified Marketing Metrics

peq brian pozeskyBy Brian Pozesky

Let’s be honest.
Marketing measurement today feels like trying to solve a puzzle with pieces from different boxes. Google tells you one thing, your retail media partner tells you another, your internal BI team gives you a third—and your CFO still thinks marketing is a cost center.

Here’s the real problem:
Every channel has its own way of measuring success, its own KPIs, and its own version of the truth. That’s not just inefficient—it’s dangerous. When every platform grades its own homework, you’re not making decisions based on data. You’re guessing. Expensively.

Welcome to the Era of Measurement Chaos:
Marketing today isn’t just multichannel—it’s fragmented. You’re probably juggling:

  • Retail media dashboards showing inflated ROAS

  • Paid social claiming last-click conversions

  • Programmatic vendors reporting on impressions and VCR

  • Internal attribution models that everyone mistrusts

And they’re all… kind of right. But not usefully right. Because if you can’t compare results across channels with confidence, you can’t optimize spend, prove performance, or scale what works.

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The Case for One Truth

At Pēq, we believe the answer isn’t more metrics. It’s better measurement.
That starts with standardization—a single framework to evaluate all media the same way, regardless of channel, platform, or format.

We call it: Different Channels. One Truth.

What Unified Marketing Metrics Look Like

Unifying your metrics doesn’t mean flattening your data. It means standardizing how you measure success, so you can compare apples to apples—even if one’s organic TikTok and the other’s a CTV ad on Roku.

At Pēq, this means:

✅ Incrementality as a common denominator
✅ Standard lift testing across channels (No holdouts or control groups required)
✅ Consistent iROAS and spend normalization
✅ Cross-retailer comparability
✅ One measurement source, not six competing dashboards

Why This Matters Now

  • Retail media is exploding—but every retailer has its own black box

  • Cookies are dying—forcing marketers to rethink attribution

  • Budgets are shrinking—so every dollar has to prove its worth

If you’re still measuring in silos, you’re not seeing the full picture. And you’re probably wasting money.

The Pēq Promise

We’re not just tracking clicks. We’re building a unified source of measurement truth—so marketers can finally say, with confidence: this campaign worked, this one didn’t, and here’s exactly why.

So whether you’re running ads on Amazon, Meta, Walmart Connect, or streaming TV—we help you compare impact with a single lens.

Ready to Ditch the Dashboard Chaos?

Start with a Free Incrementality Analysis and discover where your marketing is really making an impact—and where it’s just making noise.

👉 Get your Free Incrementality Audit

Join

Or Book a Demo Here

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When Loyalty Isn’t Enough

When Loyalty Isn’t Enough

by | Jun 19, 2025 | Marketing, Geolocation

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When Loyalty Isn’t Enough

The Case for Geo + Context

peq brian pozeskyBy Brian Pozesky

“We don’t need portfolio optimization or measurement standardization — or whatever you call it. 80% of our sales are tracked with loyalty data.”

“Get with the times, man.”

It’s a valid argument — loyalty programs have become critical infrastructure for retail media. They enable identity resolution at scale, power high-frequency personalization across digital and direct channels, and provide granular attribution tied to verified transactions. For creative performance and short-cycle ROI analysis, they are indispensable.

However, loyalty data is not without limitations. Its strength lies in precision, not coverage. Consumer graphs built from loyalty programs vary significantly across retailers and cannot be uniformly extended across the increasingly fragmented media ecosystem. They lack the interoperability and cross-channel consistency required to evaluate diverse media tactics — from linear TV to emerging programmatic platforms — in a comparable, apples-to-apples fashion. Even the largest holding companies continue to wrestle with stitching together these siloed datasets into coherent, actionable insights.

That’s where geo-based measurement offers complementary value.

Store trade area methodologies provide a standardized, spatial framework for assessing causal media impact at scale.

By anchoring measurement to physical retail outcomes — which still account for over 80% of CPG sales — geo-lift enables portfolio-level evaluation that is agnostic to ID resolution, cookie fidelity, or channel-specific tagging.

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In essence, it restores a common unit of analysis: the store

Of course, the utility of geo-lift depends entirely on the fidelity of the underlying trade areas. Naïve radius-based models fail to reflect actual consumer behavior. The most advanced approaches incorporate anonymized GPS signal data, visit frequency, dwell time, and competitive context to delineate trade areas that reflect real-world shopping patterns — not just theoretical reach.

Used in tandem, loyalty and geo provide a uniquely holistic view: loyalty delivers precision and shopper-level insight; geo provides standardization and comparability across the media mix. Together, they enable a more complete understanding of what’s working, where, and why — and allow marketers to balance personalization with portfolio optimization across an increasingly complex retail media landscape.

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