AWS Clean Rooms proof of concept scoping part 1: media measurement
Companies are increasingly seeking ways to complement their data with external business partners’ data to build, maintain, and enrich their holistic view of their business at the consumer level. AWS Clean Rooms helps companies more easily and securely analyze and collaborate on their collective datasets—without sharing or copying each other’s underlying data. With AWS Clean Rooms, you can create a secure data clean room in minutes and collaborate with any other company on Amazon Web Services (AWS) to generate unique insights.
One way to quickly get started with AWS Clean Rooms is with a proof of concept (POC) between you and a priority partner. AWS Clean Rooms supports multiple industries and use cases, and this blog is the first of a series on types of proof of concepts that can be conducted with AWS Clean Rooms.
In this post, we outline planning a POC to measure media effectiveness in a paid advertising campaign. The collaborators are a media owner (“CTV.Co,” a connected TV provider) and brand advertiser (“Coffee.Co,” a quick service restaurant company), that are analyzing their collective data to understand the impact on sales as a result of an advertising campaign. We chose to start this series with media measurement because “Results & Measurement” was the top ranked use case for data collaboration by customers in a recent survey the AWS Clean Rooms team conducted.
Important to keep in mind
- AWS Clean Rooms is generally available so any AWS customer can sign in to the AWS Management Console and start using the service today without additional paperwork.
- With AWS Clean Rooms, you can perform two types of analyses: SQL queries and machine learning. For the purpose of this blog, we will be focusing only on SQL queries. You can learn more about both types of analyses and their cost structures on the AWS Clean Rooms Features and Pricing webpages. The AWS Clean Rooms team can help you estimate the cost of a POC and can be reached at firstname.lastname@example.org.
- While AWS Clean Rooms supports multiparty collaboration, we assume two members in the AWS Clean Rooms POC collaboration in this blog post.
Setting up a POC helps define an existing problem of a specific use case for using AWS Clean Rooms with your partners. After you’ve determined who you want to collaborate with, we recommend three steps to set up your POC:
- Defining the business context and success criteria – Determine which partner, which use case should be tested, and what the success criteria are for the AWS Clean Rooms collaboration.
- Aligning on the technical choices for this test – Make the technical decisions of who sets up the clean room, who is analyzing the data, which data sets are being used, join keys and what analysis is being run.
- Outlining the workflow and timing – Create a workback plan, decide on synthetic data testing, and align on production data testing.
In this post, we walk through an example of how a quick service restaurant (QSR) coffee company (Coffee.Co) would set up a POC with a connected TV provider (CTV.Co) to determine the success of an advertising campaign.
Business context and success criteria for the POC
Define the use case to be tested
The first step in setting up the POC is defining the use case being tested with your partner in AWS Clean Rooms. For example, Coffee.Co wants to run a measurement analysis to determine the media exposure on CTV.Co that led to sign up for Coffee.Co’s loyalty program. AWS Clean Rooms allows for Coffee.Co and CTV.Co to collaborate and analyze their collective datasets without copying each other’s underlying data.
It’s important to determine metrics of success and acceptance criteria to move the POC to production upfront. For example, Coffee.Co’s goal is to achieve a sufficient match rate between their data set and CTV.Co’s data set to ensure the efficacy of the measurement analysis. Additionally, Coffee.Co wants ease-of-use for existing Coffee.Co team members to set up the collaboration and action on the insights driven from the collaboration to optimize future media spend to tactics on CTV.Co that will drive more loyalty members.
Technical choices for the POC
Determine the collaboration creator, AWS account IDs, query runner, payor and results receiver
Each AWS Clean Rooms collaboration is created by a single AWS account inviting other AWS accounts. The collaboration creator specifies which accounts are invited to the collaboration, who can run queries, who pays for the compute, who can receive the results, and the optional query logging and cryptographic computing settings. The creator is also able to remove members from a collaboration. In this POC, Coffee.Co initiates the collaboration by inviting CTV.Co. Additionally, Coffee.Co runs the queries and receives the results, but CTV.Co pays for the compute.
Query logging setting
If logging is enabled in the collaboration, AWS Clean Rooms allows each collaboration member to receive query logs. The collaborator running the queries, Coffee.Co, gets logs for all data tables while the other collaborator, CTV.Co, only sees the logs if their data tables are referenced in the query.
Decide the AWS region
The underlying Amazon Simple Storage Service (Amazon S3) and AWS Glue resources for the data tables used in the collaboration must be in the same AWS Region as the AWS Clean Rooms collaboration. For example, Coffee.Co and CTV.Co agree on the US East (Ohio) Region for their collaboration.
To join data sets in an AWS Clean Rooms query, each side of the join must share a common key. Key join comparison with the equal to operator (=) must evaluate to True. AND or OR logical operators can be used in the inner join for matching on multiple join columns. Keys such as email address, phone number, or UID2 are often considered. Third party identifiers from LiveRamp, Experian, or Neustar can be used in the join through AWS Clean Rooms specific work flows with each partner.
If sensitive data is being used as join keys, it’s recommended to use an obfuscation technique to mitigate the risk of exposing sensitive data if the data is mishandled. Both parties must use a technique that produces the same obfuscated join key values such as hashing. Cryptographic Computing for Clean Rooms can be used for this propose.
In this POC, Coffee.Co and CTV.Co are joining on hashed email or hashed mobile. Both collaborators are using the SHA256 hash on their plaintext email and phone number when preparing their data sets for the collaboration.
The exact data schema must be determined by collaborators to support the agreed upon analysis. In this POC, Coffee.Co is running a conversion analysis to measure media exposures on CTV.Co that led to sign-up for Coffee.Co’s loyalty program. Coffee.Co’s schema includes hashed email, hashed mobile, loyalty sign up date, loyalty membership type, and birthday of member. CTV.Co’s schema includes hashed email, hashed mobile, impressions, clicks, timestamp, ad placement, and ad placement type.
Analysis rule applied to each configured table associated to the collaboration
An AWS Clean Rooms configured table is a reference to an existing table in the AWS Glue Data Catalog that’s used in the collaboration. It contains an analysis rule that determines how the data can be queried in AWS Clean Rooms. Configured tables can be associated to one or more collaborations.
AWS Clean Rooms offers three types of analysis rules: aggregation, list, and custom.
- Aggregation allows you to run queries that generate an aggregate statistic within the privacy guardrails set by each data owner. For example, how large the intersection of two datasets is.
- List allows you to run queries that extract the row level list of the intersection of multiple data sets. For example, the overlapped records on two datasets.
- Custom allows you to create custom queries and reusable templates using most industry standard SQL, as well as review and approve queries prior to your collaborator running them. For example, authoring an incremental lift query that’s the only query permitted to run on your data tables. You can also use AWS Clean Rooms Differential Privacy by selecting a custom analysis rule and then configuring your differential privacy parameters.
In this POC, CTV.Co uses the custom analysis rule and authors the conversion query. Coffee.Co adds this custom analysis rule to their data table, configuring the table for association to the collaboration. Coffee.Co is running the query, and can only run queries that CTV.Co authors on the collective datasets in this collaboration.
Collaborators should define the query that will be run by the collaborator determined to run the queries. In this POC, Coffe.Co runs the custom analysis rule query CTV.Co authored to understand who signed up for their loyalty program after being exposed to an ad on CTV.Co. Coffee.Co can specify their desired time window parameter to analyze when the membership sign-up took place within a specific date range, because that parameter has been enabled in the custom analysis rule query.
Workflow and timeline
To determine the workflow and timeline for setting up the POC, the collaborators should set dates for the following activities.
- Coffee.Co and CTV.Co align on business context, success criteria, technical details, and prepare their data tables.
- Example deadline: January 10.
- [Optional] Collaborators work to generate representative synthetic datasets for non-production testing prior to production data testing.
- Example deadline: January 15
- [Optional] Each collaborator uses synthetic datasets to create an AWS Clean Rooms collaboration between two of their owned AWS non-production accounts and finalizes analysis rules and queries they want to run in production.
- Example deadline: January 30
- [Optional] Coffee.Co and CTV.Co create an AWS Clean Rooms collaboration between non-production accounts and tests the analysis rules and queries with the synthetic datasets.
- Example deadline: February 15
- Coffee.Co and CTV.Co create a production AWS Clean Rooms collaboration and run the POC queries on production data.
- Example deadline: Feb 28
- Evaluate POC results against success criteria to determine when to move to production.
- Example deadline March 15
After you’ve defined the business context and success criteria for the POC, aligned on the technical details, and outlined the workflow and timing, the goal of the POC is to run a successful collaboration using AWS Clean Rooms to validate moving to production. After you’ve validated that the collaboration is ready to move to production, AWS can help you identify and implement automation mechanisms to programmatically run AWS Clean Rooms for your production use cases. Watch this video to learn more about privacy-enhanced collaboration and contact an AWS Representative to learn more about AWS Clean Rooms.
About AWS Clean Rooms
AWS Clean Rooms helps companies and their partners more easily and securely analyze and collaborate on their collective datasets—without sharing or copying one another’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.
- Getting Started with AWS Clean Rooms Demo Video
- Five key takeaways on data clean room collaboration from Cannes Lions
- AWS Clean Rooms Now Generally Available — Collaborate with Your Partners without Sharing Raw Data
- AWS On Air episode, featuring AWS Clean Rooms – Now Generally Available
- Introducing AWS for Advertising & Marketing: Helping customers reinvent the industry with purpose-built services, solutions, and partners
- Introducing four new solutions that help customers integrate AWS Clean Rooms into their advertising workflows
- AWS Clean Rooms User Guide
About the authors
Shaila Mathias is a Business Development lead for AWS Clean Rooms at Amazon Web Services.
Allison Milone is a Product Marketer for the Advertising & Marketing Industry at Amazon Web Services.
Ryan Malecky is a Senior Solutions Architect at Amazon Web Services. He is focused on helping customers build gain insights from their data, especially with AWS Clean Rooms.