4 Takeaways on Data Collaboration for Advertising and Marketing

Companies across multiple industries increasingly look to build a complete view of their customers or business by complementing their data with external business partners’ data. For advertising and marketing use cases, brands, media publishers, agencies and their partners want to collaborate using datasets that are stored across many different channels and applications to improve the relevance of their campaigns and better engage with consumers throughout their journey.

To achieve this, companies are investing in data clean rooms to allow multiple parties to analyze their collective datasets to facilitate more data-driven, strategic analyses with more comprehensive insights. Amazon Web Services (AWS) built AWS Clean Rooms to help companies and their partners more easily and securely analyze and collaborate on their collective datasets—without sharing or copying each other’s underlying data. With AWS Clean Rooms, customers can create a secure data clean room in minutes and collaborate with any other company on AWS to generate unique insights about advertising campaigns, investment decisions, and research and development.

Recently, the AWS Clean Rooms team met with a group of 50 customers and partners, including brands, publishers, and agencies, to discuss priorities for data collaboration. During this discussion, AWS asked the group to complete an anonymous survey about data collaboration. Following are the four key takeaways from our findings.

1: The top motivation for data collaboration tool adoption is customer data protection

Figure 1 – Collaboration motivation resultsFigure 1 – Collaboration motivation results

How we evaluated: We asked the group to select as many motivations as applicable when seeking collaboration tools from the five options listed in Figure 1. Responders could select as few or as many options, and numbers reflect the count of votes per option.

When prompted for why they seek out data collaboration tools in the first place, the top selected motivation from the survey group was customer data protection, closely followed by evolving targeting technologies. This confirmed that advertisers and marketers are seeking out and adopting new tools and solutions as the privacy landscape evolves.

Data clean rooms are increasingly popular tools to help companies and their business partners collaborate, such as brands or agencies advertising with media publishers, while protecting the underlying data about their customers.

AWS Clean Rooms was built to make it easier for customers and their partners to analyze and collaborate on collective datasets to gain insights, without sharing or copying one another’s underlying data or having to move it outside of AWS. AWS customers can collaborate with hundreds of thousands of companies already using AWS, eliminating the need to maintain a copy of their data outside of AWS or load it into another platform.

When queries are run in a collaboration, AWS Clean Rooms reads data from where it lives and automatically applies restrictions set by collaborators that protect each participant’s underlying data. With AWS Clean Rooms, media publishers, agencies and their partners are able to collaborate on targeting use cases such as audience segmentation or media measurement—all while preserving the underlying data of their customers.

2: Results & Measurement is the top use case for data collaboration

Figure 2 – Use cases resultsFigure 2 – Use cases results

How we evaluated: We asked the group of customers and partners to stack rank which of the seven collaboration use cases listed in Figure 2 were most important for their business. Ranking each use case was not required.

We asked customers to rank their use cases for data collaboration, and Results & Measurement was the top use case for respondents to collaborate with their partners on their collective data, followed by a tie between Customer Insights and Audience Activation. These top use cases relate closely to different stages of campaign collaboration, especially with brands and agencies advertising with their publisher partners and wanting to determine the lift or return on investment (ROI) of running ads on media platforms.

In the advertising and marketing industry, the top use cases the AWS Clean Rooms team hears for data collaboration relate to advertisers, media publishers, and their partners expanding their access to data as they create, deliver, and measure personalized advertising experiences for customers. Measuring campaign effectiveness between advertisers and publishers is a common use case for using AWS Clean Rooms customers, who want to collaborate to measure and optimize their campaigns.

For example, AWS Clean Rooms helps match ad impression data from media publishers with conversion events (such as purchases from advertisers or measurement partners) to measure and attribute desired actions in campaigns without exposing raw user-level data. From there, advertisers and publishers can apply their learnings to optimize downstream campaign performance. Other popular measurement use cases include reach and frequency (R/F) analysis and lift analysis.

3: When it comes to data collaboration tool selection, enhancing privacy is top-of-mind

Figure 3 – Collaboration tool selection resultsFigure 3 – Collaboration tool selection results

How we evaluated: We asked the group of brands, publishers, and agencies to evaluate on a sliding scale from strongly disagree to strongly agree, how important the five criteria in Figure 3 were in relation to data collaboration. Evaluating each criterion was not required.

When it comes to data collaboration, advertisers and marketers are most interested in enhancing privacy, followed by flexibility and configurability. However, all the criteria options received fairly high scores in terms of importance for data collaboration. This response is consistent with customer feedback related to data collaboration, and the changing privacy landscape is often a starting point for seeking out data clean rooms technology.

With AWS Clean Rooms, customers can use a broad set of privacy-enhancing controls for clean rooms—including data access controls, query controls, query output restrictions, and query logging. This allows them to customize restrictions on the queries run by each clean room participant. AWS Clean Rooms also includes advanced cryptographic computing tools that keep data encrypted—even as queries are processed to comply with stringent data-handling policies.

4: Data clean rooms collaboration often starts with a proof of concept

Figure 4 – Collaboration proof of concept scopingFigure 4 – Collaboration proof of concept scoping

During discussions with AWS many customers shared their personal experiences using AWS Clean Rooms and provided advice for others seeking privacy-enhanced data collaboration. A common theme was to get started with one priority use case with a few priority partners, to quickly evaluate the value of collaborating with them. This way, customers could collaborate before deciding to invest in adopting, automating, and scaling their data clean rooms collaborations.

AWS created a framework for setting up a proof of concept to help customers define an existing problem of a specific use case for data collaboration with partners. Once customers have determined who they would like to collaborate with, AWS recommends three steps to set up the proof of concept:

  1. Defining the business context and success criteria for the proof of concept – Determine which partner to collaborate with, which use case should be tested, and what the success criteria is for the AWS Clean Rooms collaboration.
  2. Aligning on the technical choices for this test – Predefine who will set up the AWS Clean Rooms, who is analyzing the data, which data sets are being used, and what analysis should be run.
  3. Outlining the workflow and timing – Create a workback plan, deciding on synthetic data testing and aligning on production data testing.

The goal of a proof of concept is to run a successful collaboration and evaluate results against your acceptance criteria. If you would like to learn more about setting up a proof of concept for an AWS Clean Rooms collaboration, read our AWS Clean Rooms proof of concept scoping part 1: media measurement blog. This is the first in a series of scoping walkthroughs for common data collaboration use cases using AWS Clean Rooms.

Conclusion

Privacy-enhanced data collaboration is a top-of-mind topic for advertising and marketing leadership. If you’d like to learn more about use cases and customer stories related to the topics from our survey, check out the AWS Clean Rooms website. Watch the AWS Clean Rooms video to learn more about privacy-enhanced collaboration with AWS Clean Rooms and contact an AWS Clean Rooms expert if you would like to learn more.

Additional Resources

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