Why Is the Data Clean Room Becoming a DCP? From Locked Room to Open Table

Data clean rooms provided marketers with something that seemed out of reach at one time; a method to partner up with someone using data while not giving away valuable customer information. Nevertheless, as regulations about privacy get tighter and user IDs disappear, a clean room is no longer enough. Instead, marketing teams require a tech infrastructure called DCP, which stands for Data Collaboration Platform, which would allow them to use the data for creating audiences and the technology to activate the audiences.

This change is already happening. According to the IAB and BWG Strategy 2024 report, 66% of data and advertising professionals are using data clean rooms in the US. But what comes next, and how does DCP change the way of working? At Apptrove, we help mobile marketers with their measurement every day. In this guide, you will find the definition of what a DCP is, how it is different from a data clean room, and how to choose the one you need.

What Is a DCP? Understanding the Concept of Data Collaboration Platforms

A data clean room is a safe, neutral place where two or more entities introduce their first-party data while not revealing the many details of the end user. The data clean room operates like a laboratory with glass walls: both parties perform experiments together, but none of them steals the other’s materials.

A data collaboration platform preserves the workings of a data clean room, but it adds many more steps. It enables entities to create target audiences, reach them via communication channels and analyze the outcomes of their campaigns instead of limiting the functionality of data clean rooms to matching only.

This is meaningful because the ability to match data cannot provide answers to questions related to campaign effectiveness.

How Was Data Clean Room Integrated into DCP?

As the clean room technology matured, the main feature of the clean room, secure matching, became available. Rather than protecting a data artifact, the value shifted to the need for the audiences, activation and results. As a result many providers started integrating clean rooms into different platforms.

Why Is a Standalone Data Clean Room No Longer Sufficient?

Data clean rooms have addressed a certain issue while simultaneously creating additional challenges.

Why Is the Cost of Data Clean Rooms So High?

The costs are staggering. According to the research findings of the IAB concerning the current state of data, it was revealed that in 2022, 62% of clean room users had spent over $200,000 on such a solution, while the rest (23%) invested more than $500,000. The use of clean rooms also requires the engagement of specialists in the sphere of data sciences, and not many companies have the capability of employing such employees.

How Do Insights Get Stuck in a Data Clean Room?

The very first stage of using data involves ensuring its safety. According to the survey by IAB in 2023, 52% of clean room users have difficulties with utilizing the insights obtained from the clean room and proving the return on investment, and just 27% applied this information to attribution. Partners may be using various clean rooms with different technical requirements; thus, they cannot cooperate with each other effectively.

What Are the Steps Involved in Using a Data Collaboration Platform (DCP)?

The process comprises four stages. First you upload your first-party data encompassing CRM data, app activities, and purchases. In the next step along the line, the DCP identifies it against the data of your partner in a safe environment, resulting in the aggregated data insights only.

Then the DCP enables you to convert insights into audience creation and run advertising campaigns. The last step and the step usually overlooked by many independent approaches is measurement. It is important to combine the engagement data and the conversion data in one place so mobile attribution will show you the most effective touchpoints.

What Uses and Benefits Does a DCP Offer? Four Use Cases You Should Know About

How Can a DCP Help You Create More Intelligent Audiences?

Imagine you are a media company with lots of interaction data and an ad-based firm that has access to some purchase data. A DCP allows the latter to create segments based on the overlapping information such as users that bought something or interacted recently without them having to see the actual numbers from the media company. With self-service and no-code tools, marketers no longer need to depend on engineers to create this segment.

Why is Measurement Easier with a DCP?

To measure anything, three components should be at hand: media data, conversion data, and a way to marry them. A DCP provides those three elements in one tool across mobile, internet or connected TV. As a result, the reporting is cleaner and there are fewer discussions around whose numbers are correct. In addition, automated data flows are more difficult to manipulate than the traditional spreadsheet approach, which is very important.

What Should Mobile App Marketers Know About DCPs?

Mobile app marketers grab data from multiple sources including ad networks, app stores, owned media, and partners, and each source offers a unique part of the story. As device-level signals fade away, it is essential to collaborate on aggregated, consented data in order to keep calculating. DCP collects all these bits in one place. For instance, you may connect an ad viewed in a partner’s app with a purchase in your app, without either of the parties disclosing raw user details.

How Important is Privacy for DCP? 

Privacy influences all aspects of how DCP operates. It is based on a privacy-first measurement, with data being yet hashed or aggregated, and data access being allowed by all parties. Due to legislation changes, this design enables one to participate in cooperation without relying on identifiers that may be gone in the near future.

How Do You Choose the Right Data Collaboration Platform (DCP)?

It is important to remember that not all DCPs work for every team, so before picking one, reflect upon the following questions:

• Is the system built with privacy in mind? Look for advanced encryption and an effective management system.
• Can it be used for marketing campaigns without the need to write SQL? User-friendly systems allow campaigns to be implemented effectively.
• Is it valid for measurement and activation? Simple matching will not guarantee any positive results.
• Are data flows automated? Using SDK or API connections is more effective than uploading different files manually.
• Are support and pricing clear? Understanding costs and being sure that one can receive help at any stage will be beneficial. 

A good way to start is to select one reliable partner and one use case that can be treated as a test. Make sure that the metrics are defined beforehand to judge the outcome.

Why the Logical Decision Is a DCP, not Just a Data Clean Room

The data clean room model will still remain relevant but will serve better as a component of something comprehensive. With the tightening of privacy legislation and the increasing demands for measurement, the mere fact of secure matching will not profit anymore. The DCP does not lose the original ground in terms of protection but introduces audiences and measurement steps into its work.

At Apptrove, we believe collaboration and measurement work best on the same foundation: trusted, privacy-aware data. Sharing data only pays off when it leads to a decision or an action, so begin with one use case that matters to your business and build from there. If you want to talk through where to begin, contact our team.

FAQs

Q1. Which types of information can be used in DCP?

Most often first partner information is used, such as CRM files, app activity information, data about purchases and marketing campaigns, as well as files provided by external friends. During the matching process, identifiers are kept in secret so that person-protecting data is not leaked.

Q2. Why is interoperability important while choosing a DCP?

When there’s no interoperability, teams need to transfer information between the clouds. This leads to extra expenses and delays, as well as more risks. Interoperable platforms allow friends to operate in the environment where they already have information, so there’s no need for data duplication.

Q3.  How long does it take to complete DCP setup?

The duration of your DCP setup will completely depend on the tidiness of your data and how many partners are involved in that process. For example, clean and organized data along with already agreed objectives help in DCP set-up, while cluttered datasets and confusing ownership patterns slow the process down.

Q4. When is it illogical to use DCP?

When there is no significant amount of first-party data available, only one partner present, and the only question being investigated is quite narrow in scope, you can consider using simpler data methodologies, like clean room or basic analytics and gather the required data first, before going for DCP. 

Q5.  What does it mean to use second-party data in DCP?

Second-party data is the term that refers to the first-party information of another entity shared through partnership that is required for DCP analysis with your info without making the raw dataset available.

from Apptrove https://apptrove.com/what-is-dcp-data-collaboration-platform/
via Apptrove