If you’ve heard of the space race, get ready for the post-cookie contest: buy-side players across the board rush to develop the ultimate advertising solution. And there are many contenders, from universal IDs and Google Topics to Meta and Mozilla’s Interoperable Private Attribution (IPA), the ad tech world has been busy. Amongst them, there is one strong competitor: data clean rooms.
But What Exactly is a Data Clean Room, and How Does it *Run*?
As we know, the available ways for anyone to gather data are progressively being restricted, and first-party data is becoming the golden currency of the internet. But what if your data only provides you with a partial understanding of customer journeys? In some cases you might want to join forces with another company – another link in the supply chain – to extend your vision.
This is where data clean rooms come in: a secure, closed platform that allows different parties to pool data together and share insights. Consumer information is hashed and encrypted, guaranteeing user privacy; a place where both parties can decide how much data they want to share. Partnerships can occur between a company and a walled garden (many will already be familiar with Google Ads Data Hub, Amazon Marketing Cloud and Meta Business Suite) or any other beneficial arrangement, perhaps between a publisher and an advertiser, for example.
When it comes to partnerships with big tech giants, it’s worth noting that they only provide insight into brand performance on their own site, with no cross-platform or omnichannel comparisons possible. Nevertheless, matching datasets can still provide a more comprehensive view of the customer journey revealing potential inefficiencies or inconsistencies in ad spend or targeting.
The Good News
If you’re looking for a secure way to grow your dataset and gain a deeper understanding of your campaign performance, this is it. The additional information can help brands determine frequency, reach, and attribution, which then feeds back into ad spend and strategies. Beyond that, pooling resources and building alliances could help level the playing field as brands, publishers, and advertisers strengthen their positions against tech giants.
Yes, scalability could be an issue but it doesn’t have to be: sure, you may not have the same breadth of information as before, but you will still be able to gain loyal customers and high-quality audience data, facilitated targeting and segmentation. These abilities can be extended when paired with customer data platforms (CDPs), further improving data sharing, as well as enhancing data strategies and asset activation.
Let’s be honest, there are few, if any, pieces of technology that don’t come with some caveats. When it comes to data clean rooms, most of them are due to the fact that they are still evolving and adapting as an ideology as much as a product. Collaboration is not yet the status quo, with many companies still reticent at the idea of sharing data freely. This is not surprising, considering widespread fears around data breaches, privacy concerns, or reputational damage. On the technical side, without universal standardisation, companies might struggle to combine data sets without the extra time needed – and perhaps a few hiccups – to properly format everything first.
What’s more, until the practice of sharing data in a privacy compliant way becomes more widespread, tech giants with deep troves of first-party data have a market advantage. And while some companies are already looking to tackle the issue of omnichannel attribution, the success of that venture depends on the cooperation of walled gardens.
All in all, while there remain some kinks to iron out, data clean rooms provide a safe way for companies to pool their resources and create a more comprehensive view of the customer journey. Especially when paired with CDPs, data clean rooms become part of a tool set that enables privacy-compliant data management and trading, which will be an integral part of success in the very near future, with the potential to be bright, and moreover, clean.