WPP Just Made a Big Move—Here Are 3 Terms Every Marketer Needs to Know

Why WPP’s InfoSum Acquisition Matters

TL;DR: WPP is betting big on AI and clean room tech—not identity graphs. Here’s what you actually need to understand from their earnings call.

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During WPP's recent earnings call—alongside news of their InfoSum acquisition—a few terms kept popping up that left me doing some extra homework.

Quick refresher: InfoSum is a data collaboration platform that lets companies connect and analyze data without moving or exposing it. Think: clean room tech, but built for scale. It now powers the secure infrastructure behind WPP’s AI-driven strategy.

That’s why WPP’s acquisition made so much sense—it wasn’t splashy, but it was smart.

It signals that they’re doubling down on future-proofed data strategies: identity-free targeting, federated learning, and full-stack media offerings.

After digging into what these concepts actually mean in practice, I thought I'd share my findings in case you're also trying to decode the same jargon.

Here's what I learned:

1. Identity-Free Targeting

The first term I kept hearing was about targeting without personal identifiers. As third-party cookies disappear, there's a push toward alternative methods that don't rely on tracking specific individuals.

What this means in practice:

• Content-based targeting that focuses on what people are consuming, not who they are

• Predictive models that forecast buying windows based on behavior patterns

• Using engagement signals (like scroll depth and time on page) to determine interest

This approach isn't just about regulatory compliance—it's about finding more effective targeting mechanisms as traditional identity solutions become less reliable.

Example: Instead of targeting "30-year-old males who visited Nike.com," advertisers might target "users currently reading in-depth content about marathon training" regardless of who they are demographically.

2. Federated Learning

The second term that kept coming up was "federated learning." After some research, I learned this is a technology that allows different companies to collaborate on data insights without actually sharing their customer information.

Practical applications I discovered:

• Joint purchase prediction with retail partners while keeping customer data separate

• Creating audience insights across platforms without centralizing sensitive information

• Enabling compliant targeting in heavily regulated categories like healthcare and finance

The key innovation seems to be that raw data never leaves company servers, which solves many privacy and competitive concerns at once.

Example: A beauty brand could work with a retailer to understand what customers buy before purchasing their products, without ever seeing the retailer's actual customer data. This helps them target people in the right part of their journey.

3. Principal Media

The third term that caught my attention was "principal media." Agencies are increasingly becoming media owners, not just buyers.

What this looks like in practice:

• Agencies bundling together CTV inventory with performance guarantees

• Creating retail media packages enhanced with proprietary data and attribution

• Developing AI-curated private marketplaces they manage directly

From what I can tell, this represents a significant shift in how agencies operate—they're moving from just buying media to actually packaging and selling it as products.

Simple example: Instead of just helping a client buy CTV ads across different streaming platforms, an agency might create its own bundle of premium inventory with guaranteed viewability and attention metrics, selling it as a complete package with performance guarantees.

How Clean Rooms Tie Into All This

Remember that time I wrote about clean rooms for Warner Bros? Well, clean rooms are actually the connective tissue between all three concepts above.

Clean rooms are secure environments where different companies can analyze combined data without actually exposing their raw information to each other. They're essentially the infrastructure that makes these new approaches possible.

How clean rooms enable each trend:

• For identity-free targeting: They allow contextual signals to be combined with conversion data without identity exposure

• For federated learning: They provide the secure environment where algorithms can learn across datasets

• For principal media: They enable the performance guarantees by facilitating closed-loop measurement

In the Warner Bros case, they used clean rooms to connect streaming viewership with campaign performance data. Now, we're seeing this technology expand into virtually every corner of digital advertising.

Why I Think This Matters

I'm not claiming to be an industry prophet, but connecting these dots helped me understand why WPP made this acquisition.

By bringing InfoSum in-house, WPP is future-proofing its data strategy—enabling clients to activate campaigns, train AI models, and measure results without relying on outdated identity systems.

It gives them full control of a privacy-safe data infrastructure that can scale across creative, media, and performance, while unlocking new, AI-driven products that competitors can’t easily replicate.

If you're still planning campaigns the traditional way—heavily reliant on cookies or device IDs—it might be worth experimenting with some of these approaches.

The terminology can be intimidating, but the underlying concepts aren't as complicated as they sound. At the end of the day, it seems the industry is just finding new ways to be effective while working within increasing privacy constraints.

Hope this helps demystify some of what you might be hearing in your own meetings and calls.

What are you hearing about these terms in your meetings? Drop a comment or DM—I’m curious how others are navigating this shift.