Machine Learning Pipeline Automates Audience Segmentation for Global Media Agency

Publicis Media, a global media agency that provides investment, strategy, insights, analytics, data and technology services to clients, faced a significant bottleneck in audience segmentation. Manual data science processes requiring at least a month and substantial client teams prevented the company from scaling media planning capabilities. To address this, Publicis Media's data science team built the Decision Sciences Framework, a machine learning pipeline deployed on Amazon Web Services (AWS) that automates audience segmentation and provides media buyers with highly accurate segment recommendations.

The Decision Sciences Framework leverages Amazon S3 for scalable storage, Amazon EMR clusters for parallel processing, Amazon Redshift for custom attribute and segment production, and SparkML for advanced analysis. The pipeline processes petabytes of audience data by scoring profiles, audiences, keywords, ad impressions, and data provider information for propensity modeling, persona extraction, and other predictive characteristics. The solution's portable architecture allows Publicis Media to deploy the framework inside customer AWS accounts for localized data analysis without requiring data movement, enabling flexible scaling from small to large clusters depending on client needs.

By automating manual processes and leveraging AWS infrastructure, Publicis Media now services multiple clients in parallel while reducing labor-intensive work and allowing its data science team to focus on developing proprietary algorithms. The framework enables faster decision-making for media planners by identifying which audience attributes will deliver the most impact on ad performance. This shift from infrastructure management to algorithm innovation demonstrates how cloud-native solutions can transform service delivery in the media and advertising industry.