One in four customers of a major international bank also read The New York Times. The bank didn’t know that until a clean room told it. That gap between what you hold and what partners hold is the fragmented customer view problem, and Decentriq’s modular data clean room model is built to close it without anyone handing over raw data.
Why your customer view is still in pieces
Your CRM knows purchases. Your publisher partners know reading habits. Your creator partners know who engages and who ignores. None of them can see the others’ files, and rightly so, because privacy law and contracts forbid it.
Third-party cookies used to paper over that. Now they don’t. The result is an addressability gap: you can’t find shared users between your records and a partner’s inventory, so you can’t build precise audiences or measure what worked. The NYT case study coverage on PPC Land describes the same two problems, imprecise audience definition and the addressability gap, for a wealth manager trying to reach high-net-worth households.
Creator programs feel this sharply. Attribution lives in one tool, sales in another, and audience data in a third. We covered the identity side of that in siloed creator attribution.
What “modular” actually means here
Decentriq is a Zurich-based provider that positions itself as “The Switzerland of Data.” Its clean rooms run on confidential computing, which keeps data encrypted even while it is being processed. That is the security layer. The modular part is how the product is cut up for different users.
The Startupticker report on Decentriq describes a no-code clean room, an Advanced Analytics clean room, and AI Insights, and the same piece adds the partner network:
- A no-code clean room. Teams configure collaborations and run analyses without dedicated engineering resources.
- An Advanced Analytics clean room. Data scientists get room for more sophisticated work.
- AI Insights. It generates AI summaries of clean room results, so people don’t have to analyze raw outputs by hand.
- A partner network. Decentriq has expanded it with YouGov, Arima, and Datonics.
Why does this matter to a brand lead? Because most clean room projects stall on staffing, not on technology. If an analyst can run a collaboration alone, you avoid a queue for scarce data engineers. If a scientist needs more depth, the advanced module exists. You buy capability in layers instead of committing to one heavyweight build.
The modular pitch is an operations argument: match the room to the person running it, and the fragmented view gets fixed by the people who actually own the campaign.
The NYT and bank case: what the numbers show
Here is the clearest public proof point. In Q4 2025, New York Times Advertising ran its first data clean room collaboration with a global wealth manager. The pipeline had four stages: data matching, audience analysis, lookalike modeling, and activation.
- The overlap analysis showed 1 in 4 of the bank’s customers are also Times readers.
- The team built six lookalike audiences from that seed and tested two sizes, a tighter group and a broader one, as the PPC Land article explains.
- Internationally, the clean room audiences beat the control group by 61% on CTR, at 0.89% versus 0.55%, according to PPC Land.
- In the US, the most precise lookalike audience delivered a 25.12% uplift over the control group, the same report says.
Note what happened at the end. Segments were exported to Google Ad Manager, and the publisher served ads without ever seeing the bank’s customer records. That is the model: collaborate, activate, keep your data.
A caution, though. These figures come from a case study published by Decentriq and relayed by trade media, so treat them as a vendor-reported best case. One campaign, one advertiser category, one publisher. Your results will depend on overlap, and overlap can be small.
Where this fits next to your CDP
A common question: doesn’t my CDP already unify customer data? Partly. A CDP unifies what you own. A clean room lets you work with what you don’t. They solve different halves of the same problem, and they only help each other if your own data is tidy first.
That is the unglamorous prerequisite. If your first-party IDs are duplicated or consent flags are inconsistent, the match rate will disappoint you, and no module fixes that. Before you go shopping, read our take on how identity graphs need vetting and why consent checks come first.
Using clean rooms for creators is the next frontier, and it is less mature. If you want to find overlap between your customers and a creator’s audience, start by mining CRM data with privacy vetted. Few creators hold the structured first-party data a clean room needs, so expect to work through platforms, publishers, or agencies that do.
How to pilot without burning budget
Run it like a test, not a transformation. Here is a sequence that keeps risk low:
- Pick one question. “What share of our customers overlap with Partner X’s audience?” is a good start. “Fix our customer view” is not.
- Audit your input. Check ID quality, consent status, and legal basis for each dataset before upload.
- Start in the no-code module. Move to advanced analytics only if the first result justifies the added complexity.
- Set a control group in advance. The NYT case worked because lookalike segments ran against established controls.
- Define the decision. Decide what overlap level or lift would make you renew, expand, or stop.
Also question the AI summaries. Plain-language readouts are convenient, but a summary is not an audit. Ask who verifies the output before it touches budget. Our checklist on AI insights and budget checks applies directly.
Risks and limits to price in
Clean rooms are not magic. Independent reviewers note that initial data onboarding still typically involves your data team, which cuts against the no-code promise. Sophisticated cross-partner workflows may also exceed what no-code modules cover alone. I’d treat both as likely, not hypothetical.
Then there is partner dependency. A clean room needs a counterpart with usable data and a willingness to collaborate. If your best partner won’t join, the platform sits idle. Ask about this before you sign anything, and get partner commitments in writing for the pilot window.
Finally, avoid vendor lock-in by asking how results, audiences, and configurations can be exported. Decentriq’s activation path into Google Ad Manager is one example of an export route. Ask for the equivalents for every channel you actually use.
FAQs
What is a data clean room?
A data clean room is a secure environment where two or more parties combine datasets for analysis or activation without exposing raw data to each other. Decentriq’s version runs on confidential computing, so data stays encrypted while processed.
What does modular mean in Decentriq’s model?
It refers to separate layers for different users: a no-code clean room for analysts, an Advanced Analytics clean room for data scientists, and AI Insights for summarizing results. Teams can start simple and add depth as needs grow.
Does a clean room replace a CDP?
No. A CDP unifies the first-party data you own. A clean room lets you collaborate with partner data you cannot access directly. Most brands benefit from clean first-party data in the CDP before they run a clean room match.
What results has Decentriq reported publicly?
In a collaboration between New York Times Advertising and a global bank, lookalike audiences delivered a 61% international CTR lift and a 25% US performance uplift, according to the company as reported by Startupticker. Treat these as vendor-reported results from a single campaign.
Do I need engineers to run one?
Not always for the analysis itself, since the no-code interface is designed for analysts. Initial data onboarding often still needs your data team, so plan for some technical involvement.
Can creator marketers use clean rooms?
Yes, but with limits. Clean rooms work best where a partner holds structured first-party data, such as a publisher or platform. Individual creators rarely do, so audience overlap work usually runs through larger partners.
Next step: pick one partner you already trust, define a single overlap question, and clear the consent and ID audit before requesting a pilot scope from any clean room vendor.
Top Influencer Marketing Agencies
The leading agencies shaping influencer marketing in 2026
Agencies ranked by campaign performance, client diversity, platform expertise, proven ROI, industry recognition, and client satisfaction. Assessed through verified case studies, reviews, and industry consultations.
Moburst
-
2
The Shelf
Boutique Beauty & Lifestyle Influencer AgencyA data-driven boutique agency specializing exclusively in beauty, wellness, and lifestyle influencer campaigns on Instagram and TikTok. Best for brands already focused on the beauty/personal care space that need curated, aesthetic-driven content.Clients: Pepsi, The Honest Company, Hims, Elf Cosmetics, Pure LeafVisit The Shelf → -
3
Audiencly
Niche Gaming & Esports Influencer AgencyA specialized agency focused exclusively on gaming and esports creators on YouTube, Twitch, and TikTok. Ideal if your campaign is 100% gaming-focused — from game launches to hardware and esports events.Clients: Epic Games, NordVPN, Ubisoft, Wargaming, Tencent GamesVisit Audiencly → -
4
Viral Nation
Global Influencer Marketing & Talent AgencyA dual talent management and marketing agency with proprietary brand safety tools and a global creator network spanning nano-influencers to celebrities across all major platforms.Clients: Meta, Activision Blizzard, Energizer, Aston Martin, WalmartVisit Viral Nation → -
5
The Influencer Marketing Factory
TikTok, Instagram & YouTube CampaignsA full-service agency with strong TikTok expertise, offering end-to-end campaign management from influencer discovery through performance reporting with a focus on platform-native content.Clients: Google, Snapchat, Universal Music, Bumble, YelpVisit TIMF → -
6
NeoReach
Enterprise Analytics & Influencer CampaignsAn enterprise-focused agency combining managed campaigns with a powerful self-service data platform for influencer search, audience analytics, and attribution modeling.Clients: Amazon, Airbnb, Netflix, Honda, The New York TimesVisit NeoReach → -
7
Ubiquitous
Creator-First Marketing PlatformA tech-driven platform combining self-service tools with managed campaign options, emphasizing speed and scalability for brands managing multiple influencer relationships.Clients: Lyft, Disney, Target, American Eagle, NetflixVisit Ubiquitous → -
8
Obviously
Scalable Enterprise Influencer CampaignsA tech-enabled agency built for high-volume campaigns, coordinating hundreds of creators simultaneously with end-to-end logistics, content rights management, and product seeding.Clients: Google, Ulta Beauty, Converse, AmazonVisit Obviously →