Innovate faster for the post-cookie era with the broadest analytics and machine learning capabilities in the cloud
Spend less time on data engineering, and more time on data science with the broadest and deepest cloud capabilities for analytics and machine learning. With AWS, you can reduce heavy lifting and invent faster in areas such as federated ad measurement, audience segmentation, propensity scoring, identity resolution, identity enrichment, tokenization, contextual segmentation, inventory forecasting, dynamic creative optimization, and predictive attribution.
Leading advertising intelligence platforms on AWS
AppsFlyer used Amazon SageMaker and AWS Serverless capabilities to build a predictive analytics product in 6 months that predicts the lifetime value of a mobile app user with machine learning—improving accuracy on its attribution modeling and protecting user privacy in compliance with Apple’s iOS 14.
FreeWheel uses Amazon SageMaker to predict advertising inventory for digital video and linear TV months in advance for billions of ad-serving records per day—reducing overall efforts by 60 percent, costs by 50 percent, and improving accuracy of its predictions.
Programmatic advertising technology company TripleLift pioneered a solution for dynamically inserting product placement ads into streaming TV shows by using a combination of custom-built models and machine learning (ML) on Amazon Web Services (AWS).
Publicis Media built a machine learning pipeline that provides media buyers with highly-accurate recommendations on audience segments using Amazon EMR and Amazon Redshift.
Nielsen Marketing Cloud processes 250 billion ad measurement events per day using serverless computing with AWS Lambda and containers with Amazon EKS to achieve 20 percent cost efficiency.
Annalect, a subsidiary of Omnicom Media Group, reduced its cost per usable terabyte from $70 per usable terabyte to less than $5 — a 92% cost savings —as it analyzes trillions of events and petabytes of data per month.
Reduce heavy lifting
Reduce heavy lifting for data engineers and data scientists with more analytics, artificial intelligence, and machine learning services that than any cloud provider. With AWS, data science teams spend less time preparing, pre-processing and setting up analytics infrastructure; and more time inventing in areas like data privacy, federated ad measurement, audience segmentation, consumer identity, contextual analysis, brand safety, fraud prevention, and connected TV ad serving. AWS customers process vast amounts of data with Amazon EMR, which supports 21 different open source processing projects, and use Amazon SageMaker to easily prepare, build, train, and deploy high-quality machine learning (ML) models quickly.
Analyze distributed data in privacy-safe workspaces with the broadest capabilities of any cloud provider for secure analytics and storage, including tools for data sharing, governance, confidentiality, storage, preparation, security, and analytics. With AWS Lake Formation, you can build a secure data lake in days, defining data sources and what data access and security policies you want to apply. Join, match, and enrich data across accounts with Amazon Redshift Data Sharing, which enables instant, granular, and high-performance data access across Amazon Redshift clusters without the need to copy or move it.
Enrich audience data with your preferred data providers via AWS Data Exchange, which makes it easy to find, subscribe to, and use third-party data in the cloud. Qualified data providers include Adstra, Acxiom, Epsilon Data Management, Foursquare, Kantar, and others. Once subscribed to a data product, you can use the AWS Data Exchange API to load data directly into Amazon S3 and then match it with other identifiers using Amazon EMR and AWS Glue Catalog.