The data clean room market is fragmenting fast, as agency acquisitions and the rise of cloud-based alternatives reshape how advertisers work with their data.
David Barker, VP Product at C-Centric, explains why publishers that want to compete for advertiser budgets must be ready to work across multiple DCR environments – without creating a new operational headache every time.
Publishers face severe headwinds; from shrinking programmatic yields and increasing privacy regulation to the operational hurdle of scaling first-party data and meeting buyer demand for verifiable advertising accountability.
Successfully competing for advertiser budgets used to depend on audience quality, inventory, and how well you demonstrate results. That’s no longer enough. Now it also depends on how easily advertisers can onboard their first-party data, measure campaign performance and put their data to work against your inventory.
Data Clean Rooms, secure environments where organisations combine and analyse data without directly sharing it, made headlines this year when Publicis announced it was buying LiveRamp.
What started as a way to securely match data between two organisations has evolved into something much bigger: full data collaboration environments where organisations combine data, generate richer insight and create more value from what they hold.
The market around them is fragmenting fast. The Publicis-LiveRamp deal and WPP’s acquisition of InfoSum have raised real questions about data neutrality, with rival holding companies pulling away from platforms owned by their competitors and publishers wary of their valuable audience data becoming part of an agency-owned “supergraph”.
At the same time, AWS Clean Rooms, Google BigQuery, Snowflake and Microsoft Azure offer their own data collaboration capabilities, giving brands a route to major cloud platforms instead of specialist DCR providers.
Advertisers are increasingly spread across different DCRs and cloud environments, and publishers must work with whichever one they’ve chosen.
But, rather than become experts in every single data clean room, publishers are better off focusing on the technical agility that supports them.
The commercial imperative is that it removes an advertiser’s biggest objection: “We’d have to redo everything to work with you.” Especially if you can prove performance back to them regardless of which DCR their data is in. They’ve already made their investment decisions: workflow, data prep, engineering effort. They’re not going to do it again for each different publisher.
We believe there are three main use cases that publishers must deliver against, regardless of DCR.
Onboarding. Advertisers want to bring their own first-party data to suppress existing customers and target known audiences. Most of the latest releases of DCR support lookalike modelling so that advertisers and agencies have the option to direct this without disclosing row-level customer data. This also fast tracks legal sign off.
Measurement. Advertisers want to match advertising exposure to business outcomes inside their own DCR, on their own terms. Every DCR applies its own identity resolution and matching rules, so a match rate that holds up in one environment doesn’t automatically hold up in another – making this an ongoing engineering commitment, not a one-off integration.
Enrichment. The new multi-party data clean rooms (DCRs) break away from the traditional, restrictive 1-to-1 framework. This lets publishers offer a far richer picture of their audiences and create targeting opportunities that weren’t previously possible. Connecting that data through DCRs is what turns it into audience segments advertisers will pay for.
The part most publishers get wrong is treating multiple DCRs as a tech integration project, when it’s an operational capability. Each DCR structures data, matches identities, governs access and measures outcomes differently, and the challenge is there’s no single interoperability standard to fall back on. IAB Tech Lab is building toward one, but it’s early, and evolving alongside the platforms, not ahead of them.
The hard part for publishers isn’t actually connecting to another DCR; it’s running multiple environments without creating fresh operational complexity every time a new advertiser wants to work with you.
One approach is to create an operating model that removes that complexity; one that lets advertisers keep working in the environment they’ve already invested in, while the publisher handles the translation, governance and interoperability behind the scenes.
We’ve seen this operating model play out first-hand. One of the largest connected TV advertising businesses needed to support multiple DCRs to work with advertisers, agencies and cross-industry measurement initiatives, without being locked into one.
What it needed was a much richer understanding of the audiences behind that viewing – audiences valuable enough for advertisers to want to target them.
The way we approached it was to create a DCR operating model around an Identity Graph connecting multiple identifiers with external datasets, so the platform could support multiple DCRs without becoming an expert in each one.
We built the capability to onboard and match advertisers’ first-party data, measure exposure against business outcomes and create richer audience segments for targeting across multiple DCRs.
As fragmentation in the DCR market continues, publishers who can work inside whichever environment the advertiser has already chosen will have a huge advantage.