Data Clean Rooms: 3 must-know use cases to unlock new possibilities for businesses

Companies are constantly looking for innovative solutions to harness the power of their data. With the ability to guarantee confidentiality and compliance, data clean rooms are emerging as a promising way of accelerating decision-making.

Quick reminder: what is a Data Clean Room?‍

Data clean rooms are secure environments that allow organizations to collaborate and derive insights from combined datasets while maintaining data privacy.

Companies are constantly looking for innovative solutions to harness the power of their data. With the ability to guarantee confidentiality and compliance, data clean rooms are emerging as a promising way of accelerating decision-making.

In a previous article, we discussed best practices for successfully setting up a data clean room in your organization. Today, we’re highlighting a few key use cases that offer new possibilities for better business and organizational management.

1. Reinventing marketing performance analysis in the post-cookie era

With the announced end of third-party cookies, digital marketing teams are continuing to look for opportunities to better measure the effectiveness of their media actions. Data clean rooms could become one way of measuring media effectiveness. After an ad has been shown, the publisher transmits its information to a data clean room. This data can then be cross-checked by the advertiser with sales data of the product or service. There are challenges, however, such as: assessing the publisher’s ROI and the relevance of the customer journey for each publisher (or even combining data from several publishers for this type of analysis), and understanding purchasing paths, etc.

It should be noted that IAB France – the Interactive Advertising Bureau, an association representing the digital advertising industry and promoting the digital marketing ecosystem – is currently developing standards for data clean rooms^[1]. A common topic of discussion is measurement opportunities: advertising pressure, marketing lever contributions, data-driven attribution, incrementality analysis and Marketing Mix Modeling, and creative performance. To be continued…

2. Customer retention: early detection of at-risk customers

Isn’t predicting and managing customer attrition a myth? Not anymore! Thanks to data clean rooms, a company can now anticipate the possible termination of a service. How so? While a company previously had no particular interest in sharing its customer data with another company in another sector – or simply couldn’t do so – it can now pool it in a secure and compliant environment. In a data clean room, algorithmic models are trained not only with the historical database of the company in question, but also with more relevant or complementary variables from other companies. For example, to anticipate contract terminations, an insurance company can decide to share its data with a telephone operator in a data clean room. The insurer can train an artificial intelligence model with its own variables as well as telephone operator indicators that a customer may move. The insurer thus has a more effective model for anticipating future moves and terminations, which will help it to implement targeted customer retention actions well in advance of a potential termination.

By taking the new opportunities offered by data clean rooms a step further, it is possible to extend customer knowledge and audience enrichment to other issues – and by no means insignificant ones – such as Customer Lifetime Value, customer segmentation and next best experience.

3. Fraud: prevention is the best cure

525 million euros! This is what bank fraud in France amounted to in 2020^[2]. It’s no surprise then that banking institutions are on the lookout for solutions to combat this threat. If banks decide to pool their data in a data clean room, they will have a much more effective fraud detection tool. Why? Because the detection algorithm, trained with a considerable volume of data, will sharpen its ability to spot illegal transactions. Also, the data clean room guarantees a confidential and exclusive modeling environment. The result:

• A significant improvement in social impact and customer relations thanks to:
  • earlier, more accurate fraud detection; and
  • a drastic reduction in “false positives” (i.e. fraud detection errors). This is an important point as false positives can have disastrous consequences for an organization: imagine being contacted by your bank about suspicious activity, when all you’ve done is receive inheritance from your great-aunt!
• Notable productivity gains thanks to reduced human controls on illegal transactions.

These three use cases illustrate the tremendous potential of data clean rooms in corporate data management. Using innovative technology, they simplify and speed up the resolution of previously complex challenges. They also encourage convergence between sectors that previously seemed to have little in common, in turn creating new business opportunities.

[1] The IAB Tech Lab’s standards on data clean rooms are currently being discussed by the Rearc Addressability Working Group. See: https://iabtechlab.com/datacleanrooms.

[2] Statista Research Department, July 2023. Read here (in French): https://fr.statista.com/themes/3222/les-fraudes-bancaires-en-france/#topicOverview