Data clean rooms (DCR) is a technology solution that keeps first-party data private. The concept of a data clean room has evolved over time. Today, these tools are used to aggregate and anonymize user information to help protect the privacy of customer data when shared with advertisers.
In advertising privacy and security, DCRs are a relatively new technology that has only been used for the last two to three years. But how did DCRs get started, how are they being used, and how can brands get the most value out of them?
How Data Clean Rooms Started
Data clean rooms have been used for several decades, with the earliest known example dating back to the 1970s. The concept was first developed in the context of legal disputes involving copyright infringement and trade secret misappropriation.
In the 1970s, two separate legal cases involved the unauthorized use of proprietary software by competitors. In response, the companies involved created “clean rooms” to ensure their employees did not have access to any proprietary information while developing competing software. These organizations used the clean room approach to clearly separate those with access to the proprietary information and those who did not.
Over time, data clean rooms have expanded beyond software development. They are now used in other areas such as data analysis, data integration, and data sharing. Today, data clean rooms are used by various organizations to ensure the privacy and security of sensitive information. This strategy has become especially important with new regulations like the General Data Protection Regulation (GDPR) and other restrictions on data owners.
Various organizations use data clean rooms to ensure the privacy and security of sensitive information, particularly in industries such as healthcare, finance, and technology. Here are some examples:
- Healthcare organizations: Data clean rooms are used in healthcare to protect patient privacy and comply with regulations such as HIPAA. For example, healthcare companies may use data clean rooms to develop new treatments or analyze patient data without exposing identifiable information.
- Financial institutions: Banks, investment firms, and other financial organizations use data clean rooms to protect confidential financial information, such as customer transaction data and account balances.
- Technology companies: Companies that deal with sensitive user data, such as social media platforms or online retailers, may use data clean rooms to analyze user behavior and preferences without exposing individual user identities.
- Government agencies: Government agencies, particularly those involved in national security, may use data clean rooms to analyze intelligence data without risking leaks or breaches of classified information.
- Academic institutions: Researchers in fields such as social sciences and economics may use data clean rooms to analyze large datasets without exposing individual identities to protect the privacy of research participants.
Advertising platforms are another type of organization that uses data clean rooms. Ad platforms that handle large amounts of user data can also use data clean rooms to ensure privacy and security. Data clean rooms offer advertisers a way to analyze customer journey data without directly accessing personally identifiable information (PII) or sensitive user-level data.
For example, a social media platform may use a data clean room to allow advertisers to analyze user data, such as their interests, behaviors, and demographics, without exposing individual user identities. The data clean room would be set up so that the advertiser can only access aggregate data that is de-identified. That means the user identities are removed and the data is presented in a way that cannot be traced back to any individual user.
Using data clean rooms for advertising can help protect user privacy and security. That’s especially important given the increasing concerns around privacy. By using data clean rooms, advertisers can gain insights into their customers while respecting their privacy. This approach also reduces the risk of data breaches or leaks of sensitive information.
How Are Companies Using DCRs?
Companies use data clean rooms in various ways, depending on their industry and business needs. However, according to the latest IAB report, the mostly widely used use cases for DCRs in advertising are data anonymization, privacy and compliance, data normalization/cleansing, and transformation/enrichment.
According to the IAB and AdWeek, nearly 80% of advertisers who spend more than $1 billion annually on media will use DCRs. With data clean rooms, brands are starting to tackle the cookie challenge. They’re identifying proactive marketing uses for data clean rooms that also will help them navigate the coming deprecation of third-party cookies. Combining cross-platform data sets with a clean room solution also supports marketing and advertising efforts.
How to Get the Most Value From Your DCR With Predictive Analytics
Predictive analytics and data clean rooms can complement each other in several ways. Predictive analytics involves using statistical algorithms and machine learning techniques to analyze data and predict future events or behaviors.
More than half of DCR users say that providing ROI is challenging with a DCR, so it’s a must to find ways to maximize the value of the data stored there. Three major opportunities are available when predictive analytics is used in conjunction with a data clean room:
Marketing mix modeling: Marketing mix modeling is a statistical approach to measuring and forecasting the impact of marketing campaigns on sales. Companies can determine the most effective marketing mix for their products or services by analyzing historical data on marketing campaigns and sales. Predictive analytics can help in this process by identifying patterns in data that marketers can use to make accurate predictions about future marketing campaigns. Companies can use a data clean room to ensure that sensitive data used in marketing mix modeling is kept secure and private.
Attribution: Attribution is the process of assigning credit to different marketing channels for driving sales or conversions. By using predictive analytics, companies can analyze data from multiple channels, including digital and offline media. This analysis can determine which channels are most effective at driving sales or conversions. A data clean room can provide a secure environment for analyzing this sensitive data, but also keeping it private and confidential.
Driving return on ad spend (ROAS): ROAS is a metric used to measure the effectiveness of advertising campaigns by comparing the cost of the campaign to the revenue generated. Marketers can use predictive analytics to develop models that predict the ROI of advertising campaigns. A data clean room can help ensure that the sensitive data used in these models, such as customer purchase history, personal data, and advertising spend, is kept secure and confidential.
It’s a powerful combination: Predictive analytics used with a data clean room can help companies make more accurate predictions about marketing activities and optimize marketing spend. By ensuring the privacy and security of sensitive data, companies can use predictive analytics to drive more effective marketing campaigns and improve ROI.
Ready to take the next step and add ML-based predictive analytics to your DCR strategy? Get in touch to learn more about how Pecan’s low-code predictive analytics platform offers accessible machine learning for real business impact.