Advanced Data Pipeline Modernization and AI-driven Churn Prediction System for Media Enterprises

A prominent media network serving millions of households faces outdated data pipelines, manual workflows, and limited capabilities in predictive analytics, hindering rapid decision-making and efficient customer retention strategies. The organization seeks to modernize its data architecture and develop AI-driven models to improve churn prediction accuracy and operational efficiency.

A large-scale media entertainment company with a significant digital subscriber base, aiming to enhance data infrastructure and leverage AI for customer retention.

• Implement a reliable and scalable data engineering solution to modernize core data pipelines and facilitate faster analytics workflows.
• Automate end-to-end data extraction, transformation, and loading processes, reducing manual intervention and increasing robustness.
• Develop and deploy AI models for predicting customer churn with high accuracy (aiming for 95% projected accuracy) to enable targeted retention strategies.
• Monitor and maintain AI model health to prevent bias, hallucinations, and model drift, ensuring sustained prediction quality.
• Achieve significant cost savings through improved subscriber retention, with a target of up to 5X potential cost reduction compared to acquisition costs.
• Enhance data visibility and reporting through integrated analytics dashboards for informed decision-making.

• Automated data extraction from external sources and internal databases.
• Standardized and version-controlled data engineering workflows.
• Migration of existing data pipelines to modern cloud-based platforms.
• Integration of machine learning models into production environments for real-time and batch predictions.
• Automated monitoring systems for AI prediction quality, bias detection, and model health tracking.
• A user-friendly dashboard for analytics visualization and operational insights.

• External data sources for comprehensive subscriber analytics
• Internal customer management and billing systems
• Analytics and reporting dashboards
• Monitoring tools for AI model health and bias detection

• System scalability to handle increasing data volume and user load.
• High performance with batch and real-time processing capabilities.
• Data security and compliance with industry standards.
• Reliability and uptime of 99.9%.
• Automation of workflows to reduce manual intervention and optimize processing times.

The implementation of modernized data pipelines coupled with AI-driven churn prediction models is anticipated to improve subscriber retention accuracy to over 95%, potentially reducing churn-related costs and increasing customer lifetime value. Overall, this project aims to unlock up to 5X cost savings through enhanced retention efforts and more agile data operations, leading to a significant competitive advantage in the media industry.