Data privacy continues to evolve, and brands ne to proactively explore privacy-compliant solutions to targeting and attribution challenges. Marketers also ne to look at how these approaches can intersect and integrate to get ahead of the curve.
Clean rooms are gaining momentum as a privacy-first analysis solution because they let marketers safely match and intermingle their first-party data with platform data. But they come with many limitations and even a certain degree of risk, especially if you’re depending on ad platform-own clean rooms.
If your business is using a data clean room right, chances are high that you’re using one offer by a platform like Google, Meta, or Amazon. Basically, these work because you provide a direct pipeline of data to an advertising platform (via a pixel, Offline Conversions API, etc.) where personally identifiable information (PII) is match to the platform’s identity graph, after which the onboard record is purg.
The platforms get some tangible
Benefits from this setup because advertisers can inadvertently “overshare” by giving the platforms access to more data about individuals they can then use to further augment profiles or even shadow profile individuals they have no data about.
Brands that are taking user privacy preservation seriously ne to consider utilizing a clean-room type solution where data is never explicitly transferr into the advertising platform. We call this new bre of clean room environment a server-side clean room.
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What are the functions of a server-side clean room and how does it help solve for identity resolution?
In a world where 3P cookies are a distant memory and no passive data transmission can occur on a website, the future could look very bleak for marketers. Inde, if you want to scare a marketer in two words or less, “No Cookies” ranks right up there with “No Budget.
You ne to get ahead of this potential future state
That starts with understanding what will still work. You will still be able to gather 1P data from users when it is explicitly shar with your business, and advertising platforms will be able to do the same—with the critical caveat that neither party will be able to share that information with the other.
It seems like a classic paradox: how can both parties recognize a common individual they have mutual consent from and use that information for reporting and targeting optimization?
That’s where the concept of server-side clean rooms comes into play.
Server-side clean rooms will allow for granular control over data pipelines, ensuring that no protect information is ever utiliz (let alone shar) within an advertising clean room. This new version of a clean room serves as a privacy-first solution for co-mingling data with advertising platform identity graphs because the data never actually enters their systems.
That means that
No additional data is transferr to the platform to augment a user’s targeting profile beyond the fact they convert on this brand
If a user is not match (i.e. isn’t not as expos to an ad campaign on the platform), then that data will NOT be shar with the platform.
Server-side clean rooms preserve privacy because they never actually share any data with a third party. Think of it this way: Person #1 and Person #2 each have a secret they can’t tell the other. But they can both whisper their secret to person #3, who is a neutral third party. Person #3 can let each person know if their secrets are actually the same, which validates the information.
The Server-Side FutureIn a server-side buy phone number list clean room, PII that is actively shar by users can be augment and immiately process by a server-side intermiary to determine if it matches any PII stor within the ad platform’s identity graph.
Instead of sharing your data with a third party (like an ad platform), the server-side intermiary simply matches and confirms the signal (a conversion event). The data transmitt to the clean room could also be augment prior to ingestion with additional identity variables or user attributes that could increase the value of the conversion. That’s not possible in our current world, because ad platforms are f directly from browser signals, which aren’t all creat equal.
With every new advance comes a new defense, of course. Apple’s Hide My Email, for example, would still disrupt a server-side clean room’s ability to match emails across systems because the feature generates unique, one-time-use emails for users, so even actively shar information is difficult or even impossible to match.
Customer data platforms and server-side portability
are commoditiz off-the-shelf server-side data portability solutions available, but the quality and utility of those solutions vary. You ne to be able to refine the data and include interactions that take place beyond the browser.
Server-side solutions controll by customer data platforms (CDPs) make sense as the logical evolution of this space, but the increas power of these outputs comes with additional risk and responsibility.
When you add more singapore number refinements, you’re essentially introducing more pipes for data to travel through. They can break at times, experience latency, and be hard to control if you don’t have the right processes and safeguards in place.
Shopify is a prime example. They have fac some criticism of their server-side data-capture solution because it doesn’t always generate the optimal data payload for advertising platforms. That’s because it prioritizes all of the attributes downstream. When everything is prioritiz, nothing is prioritiz, which had a negative impact on identity reconciliation.
Minimizing data feback loop latency is critical
Meta, for example, has not that a delay of more than four hours following an event will result in diminish impact with regard to algorithmic optimization.
Some of the big selling points for CDPs are audience management and automation (which is really just the second coming of marketing automation platforms), but a critical value proposition people might not be focus on is tag management functions. Platforms like Tealium, mParticle, and Segment have made this a key aspect of their solutions. They don’t simply port and manage audiences, they look at what those audiences do and utilize those insights as part of the conversion feback mechanism.
The construct isn’t new. Google Ads has been supporting Offline Conversion Tracking (OCT) for over a decade through the use of gClids, but this isn’t the OCT you are us to. The new version is built for both spe (by focusing on real-time signal processing rather than batch uploads), and accuracy (by including a broader array of identity elements for matching).
Protect identity resolution capabilities
It’s possible that a future awaits where websites have no PII-bas tracking functions whatsoever. Facebook and Google pixels are replac with technology that would feel more at home on a GeoCities website circa 1998.
It wouldn’t be just PII use that’s curtail; all passive identification solutions are illegal and you won’t be able to track or port identity between platforms due to browser restrictions or privacy laws.
Essentially, everyone would be anonymous until they provide an explicit identifier like an email and even then it will be ti to a moment in time and not persistent across return sessions.
We call this state data zero
It might sound like science fiction now, but platforms are already preparing for the possibility of data zero. A server-side solution featuring a clean room-esque environment where no data is ever shar would be essential if you want to utilize PII feback loops for algorithmic optimization. Clean rooms would likely be us in a much broader capacity, not just for measurement but for audience onboarding and unifying cryptographically stor PII.
