FAQ    Marketing Measurement     What is a Data Clean Room?

What is a Data Clean Room?

A Data Clean Room is a secure, protected environment where PII (Personally Identifiable Information) data is anonymized, processed and stored to be made available for measurement, or data transformations in a privacy-compliant way. The raw PII, is made available to the brand and is only viewable by the brand.

How does it work?

All user-level first-party data loaded from CRM systems (including historical data) like Salesforce, or ecommerce platforms (such as Shopify, Magento, Epsilon). are loaded into this secure environment. Any other data sources including historical and current transaction data can also made available in the clean room environment for a variety of use cases.

The PII data sent to the clean room is hashed for transmission and once it enters the clean room it is secured and encrypted, protecting it from unauthorized access. Brands have full control over the clean room, while partners can get a feed with hashed PII data as an output. This anonymized data can then be shared in a compliant way with measurement partners like Measured or media/publisher platforms like Facebook and Google.

What’s the benefit?

It is set up by a partner like Measured, but is handed over to the brand to use as a turnkey feature giving complete control of the environment to the brand.

What are some additional privacy features?

A consent management system that captures first-party acceptance of cookies to be added to the environment which assists with adherence to CCPA/ CPRA /GDPR regulations. These consent signals are applied to data in the Clean Room, resulting in enriched data that can be used for measurement, or to pass back to media/publisher platforms.

All in all, the Data Clean Room is a trusted Turnkey Compliance solution that is easy to deploy and maintain within your secure environment for privacy compliance.

Use cases for a Data Clean Room:

  • Anonymizing user-level PII data that can be used for measurement
  • Automation for upload of offline data to publishers like Facebook for matchback processes
  • User-level analysis of customers including LTV reporting
  • Cohort level analysis.
  • Built-in privacy compliance support for regulations such as CCPA, CPRA and GDPR.

Author

Madan Bharadwaj - Cofounder & CTO

Expert in advertising measurement, attribution and analytics

 

Multi-touch attribution is more challenging today due to limited tracking options, identity and cross-device resolution hurdles, data leakage and the massive amount of time it takes to implement.

 

What is cross-platform attribution (or cross-channel attribution) and why is it difficult?

The goal of cross-platform attribution in marketing is to gain clarity on the interplay and contribution of influence that each channel/tactic/campaign has on driving conversions over and above baseline sales.

It’s a task that has proved to be very difficult for many reasons including but not limited to:

  • Walled gardens are typically inaccessible to third-party tracking of impressions
  • Identity resolution across media platforms is quite low
  • Cross-device tracking is difficult and match rates are extremely low
  • Instrumenting a tracking infrastructure by a third party measurement provider has proved to be fraught with breakage and data leakage
  • It is extremely time consuming to implement without the help of a partner

Video: Landing a source of truth cross-channel media reporting dashboard

 

 

What are some cross-channel attribution tools?

MTA – collects individual, or user-level data, for trackable addressable media and conversion events in order to determine the impact of each media event to the desired conversion at the customer level. By summing the impact of each addressable media touchpoint on each customers’ likelihood to convert, MTA quantifies the total media channel lift provided by addressable media. MTA does not account for the impact of non-addressable media, and furthermore much addressable media is either non-trackable or lost due to the innumerable challenges of tracking data at the user level.

Incrementality Measurement – Incrementality in marketing refers to the incremental benefit produced per unit of input stimulation. Incrementality is the lift in desired outcome (awareness, web visits, conversion, subscriptions, revenue, profitability) provided by marketing activity.

Incrementality in marketing is especially needed for channels where ad impressions such as display, Facebook, social, or even TV are hard to measure. To measure incrementality, the audience is broken out into test groups (exposed to the ads) and a control group (suppressed from seeing the ads).

MMM – MMM is a top down (aggregate marketing data) and very artistic statistical exercise where one or more models (e.g. econometric, multi-linear regression) are leveraged to extract key information and insights by deriving information from multiple sources of marketing, economic, weather and financial data. MMM is also a high-touch consultative approach that is very manual with little to no automated data inputs, whereas MTA and Incrementality, when deployed properly, is a very automated approach leveraging preconfigured connectors that extract the required marketing data, across many channels, on regular cadence. (It’s important to note that MTA can take 6 months or more to deploy, whereas Incrementality can be up and running with reporting in 4-6 weeks.) See this article for more on why always-on automated experimentation is the future of marketing measurement.

Author

Trevor Testwuide - CEO

Expert in business strategy and marketing measurement.

 

Multi-touch attribution is more challenging today due to limited tracking options, identity and cross-device resolution hurdles, data leakage and the massive amount of time it takes to implement.