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Data Clean Rooms is one of the most talked-about topics in the marketing, analytics, and advertising industries.
As Google has given up support for third-party cookies from Chrome and joined Apple(Safari) and Mozilla(Firefox) in their odyssey of a cookieless world, the advertising attribution challenge has elevated. Marketers are facing inaccuracies in measuring their return on advertising spend.
This is where Data clean rooms come to the rescue, as they are the key to measuring advertising effectiveness.
In this blog, we’ll go over what data clean rooms are, the types of data clean rooms, why you need them, and how they will affect our ability to measure campaigns.
Data Clean room is an encrypted, secure location where huge tech companies (such as Google or Facebook) store aggregated advertising data. They are used to understand advertising data better by major brands and businesses.
In a Data Clean Room, publishers and advertisers can securely store and share combined datasets for marketers to analyze and leverage their advertising efforts.
As the user data enters the Data clean room, aggregated data and insights are delivered in a cohort.
Data clean rooms have strict privacy controls where customer-level data is not used, allowing it to be a safe space for first-party user data while upholding privacy compliance.
Personally-Identifying Information (PII) is Attribution Restricted user data, that is unexposed to any collaborators in a data clean room. Hence, they cannot single out users with unique identifiers.
PII data is processed for various measurement purposes by producing anonymized data that can be crossed and combined with different sources.
In this way, brands get access to insightful data, without violating consumers’ privacy.
A Data clean room process can be classified into five parts:
First-party data(CRMs, site/app, attribution) is merged with a trusted second-party(brands, partners, ad networks, publishers) data partner is funneled into the data clean room.
Various privacy techniques ensure that personally identifiable information linked with your customers is secure and not shared with second parties.
Data Clean Rooms offer privacy features like restricted access, differential privacy, pseudonymization, and noise injection.
Datasets of both parties are then matched in the clean room environment using a one-way hash of identifiers and made to complement each other using Data Enrichment tools.
Data is analyzed for Overlaps, Intersections, Attribution, and measurement.
Although only aggregated data can be queried, without exposing individual data.
Data Clean Room has enabled marketers to many Marketing applications using this aggregated data outputs such as:
Data clean rooms can be diversified into four different types, which are suitable for most businesses according to their unique needs:
Walled gardens are data clean rooms provided by big tech platforms like Facebook, Google, or Amazon, which have created a closed ecosystem with significant control over the hardware, applications, or content.
They were introduced by these tech giants to safely commercialize first-party data and measure ad spending.
The upside is the support for first-party data set enrichment with event-level data.
The downside is that they have a rigid architecture, lack the support for multi-touch attribution(no cross-platform activation data), and have strict query functionality.
Diversified providers are businesses operating in the same or related industries like cloud data storage or marketing applications.
They offer organizations multiple data collaboration mechanisms to gather data signals in a privacy-compliant way.
On the upside, they offer great architectural flexibility and governance controls over data types and levels of analysis. The downside is that they have limited access to walled garden data.
These are young and small-scale data clean room providers that develop software solutions for media companies and brands.
The advantages of choosing a pure player are that they are architecturally flexible and offer a better-unified view of the data insights.
The downsides are that they are limited in first-party data granularity, often rely on 3rd-party infrastructure for data ingestion, and can be difficult to integrate with limited downstream integration options.
An MMP is a platform that offers user-level and cross-channel data aggregated and grouped for actionable insights.
It also provides
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In a study by Gartner, around 80% of marketers with media budgets over $1B will adopt Data clean rooms by 2023.
There can be multiple reasons for marketers to start using Data clean rooms. Here are three reasons for you to understand why you need a Data clean room?
Privacy: The alarming rise of questions over data privacy has made many marketers rethink their advertising approach.
Data Clean Rooms offer a framework that ensures that involved parties can’t access each other’s data while ensuring that PII or aggregated user-level data is not shared between different companies without consent.
Handing valuable First-party data outside a data cleanroom can be a risky affair. Hence, most marketers choose Data Clean Rooms due to a lack of commercial trust between parties.
Measuring Tool: Data clean rooms can be utilized as a tool to measure your campaign performance.
Ease of Use and implementation: Data Clean Rooms offer an increase in efficiency for synthesizing data, and allow data correlation across separate platforms, without the requirement of data scientists.
Most data clean rooms cannot be combined with other data clean rooms and they only function for a single platform. It limits companies from analyzing advertising data across multiple platforms.
Additionally, data clean rooms set a lower limit on the number of users needed before sharing the aggregated results with the businesses. This limits businesses from getting insights for one or a small group of users.
For eg: Google’s Ads Data Hub has set a minimum limit of 50 users in order to share the aggregated results.
Data clean rooms are just one way of overcoming the challenges we face with the loss of third-party cookies, but there are other solutions.
Two other notable alternatives being discussed right now are:
Browser-based tracking anonymizes user-level data and clusters audiences based on shared attributes.
They are an in-built or native feature of your browsers such as Google Chrome’s Federated Learning of Cohorts (FLoC) which is as effective as third-party cookies for ad targeting and measurement.
Universal user IDs are an alternative to both Data Clean Rooms and Browser-based tracking.
These are used across all major ad platforms and are anonymized, so advertisers wouldn’t see a user’s personal data.
Theoretically, it would be easier for advertisers to perform cross-network attribution as the universal ID tag replicates the functionality of third-party cookies.
The growing interest in regulated privacy data collaborations of first-party data has resulted in an increasing number of data clean room providers.
As more companies collaborate using Data clean rooms, it has become easier for marketers to monitor, attribute, and optimize their campaigns.
Data Clean Rooms are undoubtedly a highly credible platform, which provides access and usage of data, agreed by all data clean room parties, and data governance, enforced by a trusted Data clean room provider.
Many creative and young brands are seizing such an opportunity to plan, explore and test new solutions to evolve their advertising efforts.