The Hidden Cost of Retail Media Networks: Why First-Party Data Alone Isn’t Enough in 2025

Let’s get something straight: first-party data is powerful. It’s clean. It’s consented. It’s real. But inside retail media networks? It’s also a trap. Here’s why.

Every RMN runs on their first-party data—not yours. That means you’re building campaigns on data you don’t own, can’t see outside of a single platform, and definitely can’t take with you across retailers or channels. Sure, it works inside Amazon. Or Walmart. Or Kroger. But outside that ecosystem? You’re stuck. You can’t retarget. You can’t coordinate. You can’t measure beyond that specific dashboard. And if you think the retailer is going to hand over the keys to that customer relationship… think again.

This is the hidden cost of relying on RMN-provided first-party data.

Let’s break it down.

The Problem: You Don’t Actually Own the Data

Retailers know their shoppers. That’s why RMNs work. But when you advertise through an RMN, you’re renting access to their audience, not building your own.

Here’s what that means in practice:

• You can’t see what those shoppers do outside the retailer’s walls
• You can’t retarget them through other channels
• You can’t build longitudinal performance views
• You can’t tie it back to your own CRM or loyalty program

Let’s say you’re a premium baby formula brand advertising on Walmart Connect. You run a high-performing RMN campaign that drives strong ROAS. Great. But those buyers? You don’t know who they are. You can’t retarget them on Meta. You can’t follow up with an offer on Instacart. You can’t even suppress them in your next paid campaign.

So now what? You either run the same campaign again. Or start from scratch with a different RMN, targeting a different segment. That’s the cycle. And it’s expensive.

Why First-Party Data Isn’t the Full Picture

Your own first-party data is valuable, bby itself, it’s not enough to build a scalable retail media strategy. There are two main reasons why.

It only shows one side of the customer

Your loyalty program or CRM might show:

• What people bought
• When they bought it
• How often they come back

But it doesn’t show:

• What else they shop for at other retailers
• Where they spend their time outside your brand
• What lifestyle or behavioral signals predict future purchases

That’s a huge gap.

Let’s say you’re a frozen meals brand launching a plant-based line. Your CRM shows repeat buyers of high-protein meals. Great. But what if some of those buyers are also:

• Shopping at Sprouts and Trader Joe’s
• Visiting gyms 3x a week
• Using wellness apps like MyFitnessPal
• Engaging with vegan recipe content on mobile

That’s insight you’d never get from first-party data alone.

It doesn’t scale across RMNs

While Retail Media Networks are growing fast, each RMN operates as a silo. Amazon data stays in Amazon. Walmart data stays in Walmart. You can’t connect the dots across platforms without a portable audience layer that works across ecosystems. If your CTV ads, Meta campaigns, and in-store activations are using different targeting strategies from your RMN campaigns… your audience isn’t unified.

It’s fractured. And fractured audiences = fractured performance.

The Fix: Predictive Enrichment

This is where smarter marketers are pulling ahead. They’re not just using first-party data from one retailer. They’re building predictive audiences that combine:

• CRM and loyalty behavior
• Mobile app usage
• Retail visitation patterns
• Lifestyle traits
• Content consumption
• Household demographics
• Cross-retailer buying behavior
  

This is where Skydeo predictive audiences and insights come in. We enrich your first-party seed audience—then give you predictive segments that can be activated everywhere.

Say you’re a sports nutrition brand. Your best customers are:

• Men 25–40
• High-frequency protein bar buyers
• Engaged with your email promos

That’s great—but limited.

Now enrich that seed audience with predictive data, and you uncover:

• Most of them go to Planet Fitness or LA Fitness 3x a week
• They use health tracking apps
• They also buy high-protein frozen meals
• They respond to lifestyle content, not clinical benefit ads

With that, you can now:

• Target them on Walmart Connect, Instacart, and Amazon
• Build lookalikes in CTV and TikTok
• Run a campaign synced to New Year fitness resolutions
• Retarget with snack bundles across Meta and email

That’s not a siloed RMN campaign. That’s a cross-channel, data-driven strategy built on real predictive behavior signals—not assumptions.

You Can’t Just Play by the Retailer’s Rules

If you want to win in retail media, you can’t just play by the retailer’s rules.

You need your own playbook—and it starts with audience strategy. Because first-party data is just the foundation. To scale, perform, and truly grow across RMNs, you need depth. You need reach. You need predictive enrichment that lets you go beyond a single retailer’s view of your customer—and build your own.

The brands that succeed in this new retail media era won’t be the ones spending the most. They’ll be the ones that understand their customers better than anyone else—and use that insight everywhere.

Want to Enrich Your First-Party Data with Predictive Signals?

Skydeo gives you:

• Enrichment for CRM and loyalty data
• Lookalike modeling across 30,000+ predictive traits
• Portable audiences you can activate across RMNs, CTV, programmatic, and social
• Identity resolution tools that keep you privacy-compliant and omnichannel-ready

– Try Skydeo SAM for free and unlock smarter targeting for your next retail media campaign.

Learn More About Real Media Networks:

Retail Media Networks Are Blowing Up—But Most Brands Are Only Getting Half the Value
How to Build a Predictive Audience That Performs Across Retail Media Networks
Retail Media Networks Without Measurement Are Just Expensive Guessing