Every report I read says that spending with retail media networks is increasing year over year, yet my conversations with consumer packaged goods stakeholders (or ‘suppliers’ as the retail category managers often refer to them) don’t necessarily reflect what the numbers are saying.

If we look at the two biggest RMNs – Amazon Advertising and Walmart Connect – here are the numbers:

So what’s really happening?

1. All budgets are getting funneled towards RMNs: CPG budgets that were traditionally defined as ‘trade,’ ‘shopper marketing’ and ‘branding’ are all being tapped by the RMNs who claim that they can reach the consumer throughout the entire funnel of the purchasing journey. That means that different people within a CPG organization are spending with RMNs – salespeople, shopper marketing teams, brand teams, digital commerce teams and more. 

2. Fragmented support system: Large CPGs who sell well-known products often work with multiple agencies and brokers who are tasked with managing a specific part of their budget. A retail media network can potentially work with multiple agencies/brokers who are all tasked with supporting the same CPG.

3. Retailers are re-orging: As the retail media networks take over responsibility for different monetization opportunities within the retailer’s organization, the revenue is now being counted towards the “RMN,” but this is merely a change in accounting. What used to hit a different P&L within the retailer organization is now being captured under the retail media network division.

What are the consequences?

1. CPGs need to make every dollar count: Marketing dollars need to work harder than ever before because they have multiple KPIs. I can’t tell you how many times I’ve heard, “The KPI is awareness but we really need sales.” Everyone knows that awareness is a much different beast than conversion, yet there’s an unreasonable expectation that all marketing activations will be successful when you look at a variety of metrics.

Marketing today is more complex than ever.

Campaigns are running across dozens of platforms. Data is siloed. Attribution models fight for credit. And marketers are constantly under pressure to prove ROI — faster, with fewer resources. It’s no wonder decision-making can feel more like educated guesswork than confident strategy.

At Pēq, we built our platform to solve this exact problem. And if we had to boil down our most powerful value proposition into one word, it would be this: clarity.
Clarity on what’s actually working. Clarity on where to invest. Clarity on how to grow.

Why Pēq Exists
The idea behind Pēq came from a simple but frustrating reality: marketers were drowning in dashboards but starving for answers. Reporting was slow. Results were murky. Incrementality was nearly impossible to isolate, especially across newer channels like retail media or influencer content.

So we reimagined the entire approach to marketing measurement — combining advanced AI with real-world sales and media data to give teams not just more information, but better insight.

And because our platform was built to integrate easily with your existing data (without relying on PII or clean rooms), you can get up and running quickly and see results fast. Whether you’re reallocating budget, testing new creative, or making the case for more investment — Pēq gives you the evidence to back it up.

Why It Matters
When marketing is moving fast and budgets are under pressure, the cost of inaction is high. Delayed insights mean delayed decisions. And in a competitive environment, speed and accuracy are everything.

With Pēq, teams don’t just move faster — they move smarter. They make decisions based on actual outcomes, not instincts. They cut what’s not working and double down on what is. And they do it all with confidence. That’s the clarity we offer.

If you’re tired of guessing, and ready to prove what’s really driving growth — we’d love to show you how Pēq can help.

John Wanamaker, Sam Walton and Jeff Bezos walk into a bar.

Wanamaker wondered where his other half had gone, which he had been worrying about long before his overused quote, “Half of the money I spend on advertising is wasted. The trouble is I don’t know which half,” became legend. Bezos didn’t really care—he already knew everything about everyone anyway. And Walton? He was politely explaining to the bartender that he had a store within 10 miles of every U.S. consumer, complete with smiling greeters and low prices—so really, what was there to worry about?

They pulled up stools and ordered drinks. Wanamaker kept going on about his missing half.
Bezos gave a knowing smile. “That’s cute. These days, we test everything. I know which campaign made someone blink twice before they scrolled past.”
Walton, never one to mince words, chimed in: “Guys, just keep the prices low.”

But Walton had to admit—he did want to know what actually drove people into the store. Bezos had data coming out of his ears, but even he was drowning in dashboards. And Wanamaker? He was still in the corner, flipping coins and arguing with himself about which half to blame.

It’s a crucial concept for understanding marketing effectiveness, but over the past 25+ years of digital marketing, it’s been buried under an overwhelming amount of noise.

Digital marketing was supposed to take the guesswork out of advertising—everything could be tracked, every consumer’s behavior could be analyzed. But as the medium exploded, so did the volume of data. Thousands of ad-tech vendors entered the scene, each offering their own tracking and attribution schemes, none of which could be easily compared, validated, or trusted. 

Over time, the market began to consolidate. Many vendors were either absorbed by larger players or run out of business, leaving the power in the hands of a few dominant, fragmented entities.

But it’s not just media providers who control the data today—retail giants like Amazon and Walmart now wield enormous influence. These retailers, along with tech giants including Meta and Google, hold vast pools of first-party data, and their influence over consumer behavior is unparalleled.

In this chaotic environment, incremental measurement emerges as the solution to cut through the noise. Instead of trying to track every interaction or relying on attribution models that often tell you everything and nothing at the same time, incremental measurement focuses on one crucial question: What would have happened without your campaign? By isolating the true impact of your marketing efforts, it helps you identify valuable insights amid the overwhelming noise.

In a world where speed, precision, and adaptability define success, incremental measurement has evolved from a static evaluation tool into a real-time engine for smarter decision-making. Thanks to AI and machine learning, we’re no longer guessing about the impact—we’re seeing it unfold, understanding it instantly, and acting on it with confidence. The future of measurement isn’t just about knowing what worked; it’s about knowing what’s working right now—and being ready to evolve with it.