AWS Clean Rooms ML custom modeling is now available | Generate predictive insights using your own machine-learning (ML) models and data in a clean rooms collaboration »

AWS Clean Rooms ML

Apply ML with your partners without sharing underlying data
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AWS Clean Rooms ML helps you and your partners apply privacy-enhancing controls to safeguard your proprietary data and ML models while generating predictive insights—all without sharing or copying one another’s raw data or models. With AWS Clean Rooms ML custom modeling, you and your partners can bring a custom ML model for training and inference using first-party data and algorithms to apply ML predictions at scale without having to share sensitive intellectual property. You can also use an AWS-authored lookalike model and invite your partners to bring a small sample of their records to a collaboration to generate an expanded set of similar records while protecting your and your partners' underlying data.

AWS Clean Rooms ML

Benefits of AWS Clean Rooms ML

With AWS Clean Rooms ML, your data is only used to train your custom or lookalike model, and your data is not shared among collaborators or used to train AWS models. You can remove your data from Clean Rooms ML or delete a custom model whenever you want, and you can apply privacy-enhancing controls to safeguard sensitive data that you bring to a collaboration.
With AWS Clean Rooms ML custom modeling, you can run ML training and inference using your models, algorithms, and data to generate predictive insights with your partners, without having to share your proprietary models or algorithms that you bring to a collaboration.
With AWS Clean Rooms ML lookalike modeling, you can train a custom, AWS-owned ML model for you and your partners. The AWS-authored model was built and tested across a wide variety of datasets such as news, e-commerce, and streaming video channels. Your data is only used to train your model, data is not shared with either party, and you can remove your data or delete a custom model whenever you want.

Use cases

Advertisers can bring their proprietary model and data into a Clean Rooms collaboration, and invite publishers to join their data to train and deploy a custom ML model that helps them increase campaign effectiveness.

Financial institutions can use historical transaction records to train a custom ML model, and invite partners into a Clean Rooms collaboration to detect fraudulent transactions.

Research institutions and hospital networks can find candidates who are similar to existing clinical trial participants to accelerate clinical studies.

Brands and publishers can model lookalike segments of in-market customers and deliver highly relevant advertising experiences.

Customers and partners

Flywheel

Xmars is an advanced AI-powered ads management platform that provides brands, sellers and agencies with unparalleled advantages in maximizing returns of their Amazon advertising spend.

“Driving incremental reach at high precision and scale efficiently is a top priority for our clients. With AWS Clean Rooms ML solution, enriched with Amazon Marketing Cloud (AMC) data, we are able to create highly custom-built modeled audiences, which is designed to predict users' likelihood of engaging with an ad or completing a purchase. By directly reaching the modeled audience through Amazon DSP, this Bring-Your-Own-Model (BYoM) approach resulted in 34% increase in detail page view rate and 24% higher shopper transaction value on Amazon. AWS Clean Rooms truly unlocks more possibility and flexibility for us to discover high-value prospects for our advertisers.”

Tony Wang, Co-Founder of Xmars

Flywheel is an ecommerce retail agency. Its best-in-class service combines tailored expertise with cutting-edge software solutions to achieve a singular goal: drive incremental sales, share, and profitability for our clients on Amazon.

"AWS Clean Rooms ML is enhancing our ability to measure user purchase propensity. Privacy-safe deep learning allows us to capture complex relationships between users and their shopping journeys, enabling more precise targeting for our brands. For the first time, we can customize the shopper journey at the individual level in a privacy-safe way, driving better outcomes for both them and our customers."

Dan Nealon, Senior Manager Data Science, Flywheel

Xmars

Amazon Marketing Cloud (AMC) is a secure, privacy-safe clean room application from Amazon Ads that supports thousands of marketers with custom analytics and cross-channel analysis. Builders can use AMC APIs to create their own offerings, while analysts can interact with a user interface available through the Amazon Ad console.

“AMC Audiences now offers new custom lookalike audiences, powered by AWS Clean Rooms ML, which can be activated on campaigns in the Amazon DSP and help advertisers unlock incremental audience reach in line with their goals. Since its launch in October 2023, this capability enabled a leading CPG brand to reach new prospects and increase campaign performance.”

Paula Despins, Vice President of Ads Measurement, Amazon Ads

Slalom is a global business and technology consulting company.

"We are always looking to partner with our publisher clients to update their technology stack so they can more easily unlock the full potential of their high-quality ad inventory. AWS Clean Rooms ML's highly accurate ML modeling is very compelling as publishers look for ways to improve advertising effectiveness. AWS Clean Rooms ML provides an easy-to-use interface that publishers and brands can use to identify the right users for an ad campaign, while protecting sensitive data of both parties."

Mukesh Kumar, General Manager of the Global Technology Team, Slalom

Experian gathers, analyzes, and processes credit data at massive scale to help businesses make smarter decisions, individuals gain access to financial services, and lenders minimize risk.

"As marketers and publishers seek to maximize the value of their first-party data across a growing number of consumer touch points, our customers want solutions that enable them to effectively and securely interact with their partners. AWS Clean Rooms ML enables our marketer customers to use their first-party data in combination with our unique consumer data, such as vehicle purchase information, to find prospective users on publisher sites that resemble the marketer's current best customers without revealing sensitive data to partners."

Chris Feo, SVP of Sales, Experian

Twilio Segment is a leading customer data platform (CDP) that accelerates client business growth through advertising effectiveness.

“Never has it been more important to focus on quality, real-time first-party data as businesses launch more AI-driven campaigns. Our recent report shows that 85% of businesses are prioritizing capturing and leveraging first-party data better in the coming year. Leveraging AWS Clean Rooms ML modeling helps protect our customers’ valuable first-party data while empowering them to reach high-value prospecting audiences through collaboration with their preferred media publishers.”

Kathryn Murphy, SVP of Product, Twilio Segment

Affinity Solutions, a leader in consumer purchase insights, uses data from over 140 million cards to provide an unparalleled view of US consumer spending, transforming data into actionable insights that drive market share and revenue growth.

“Affinity Solutions is at the forefront of balancing privacy with providing comprehensive consumer insights. With AWS Clean Rooms ML, our marketer clients will be able to leverage our deterministic dataset as seed data for creating advanced lookalike models in combination with their own data. This empowers companies to identify potential purchasers across platforms, while adhering to privacy standards and providing potent, actionable insights for today's privacy-conscious market.”

Atul Chadha, Chief Technology Officer, Affinity Solutions

The Weather Company provides weather data and insights to consumers, brands, and businesses across the globe.

“The Weather Company is testing AWS Clean Rooms as a practical way to enable advertisers to analyze their first-party data together with weather data and use predictive machine learning to identify engaged audiences, at scale, based on weather’s impact to people’s daily lives. AWS Clean Rooms offers a streamlined  capability that accelerates time to value, enabling lookalike segment creation in a few clicks, while helping us protect the data of the hundreds of millions of consumers who visit our digital properties each month.”

Dave Olesnevich, Head of Advertising Products, The Weather Company

StellarAlgo is a leading customer cloud platform for the sports and live audience industry, partnered with more than 110 properties across North America, including league-wide relationships with the NFL, NHL, and NBA.

“As a leader in helping the world’s leading sports and live entertainment brands understand, grow, and monetize their audiences, we are thrilled that AWS Clean Rooms continues to innovate rapidly to empower our clients to succeed. AWS Clean Rooms ML modeling helps our clients identify and engage high-value prospects, allowing them to execute more effective, resonant partnerships—all while enabling us to help protect their sensitive first-party data. We are thrilled that AWS Clean Rooms continues to innovate rapidly to empower our clients’ success.”

Greg Sargent, SVP Sports Partnerships, StellarAlgo

BRIDGE is a people-based omni-channel marketing platform that helps customers market to their true buying audience.

 

“At BRIDGE, we are excited to use AWS Clean Rooms ML to support our lookalike audience builder—enabling our clients to securely leverage our real people datasets to better understand their CRM files and find their next customer. AWS Clean Rooms ML supports BRIDGE’s goal to provide privacy-first collaboration tools that improve consumer intelligence and drive marketing outcomes more effectively.”

Rob Rose, CEO, BRIDGE