Starz: ML and data solutions for a premium TV network

A premium US television network reaching 28 million American households works with AgileEngine on business-critical data systems and experimental machine learning capabilities. Our Data Studio experts modernized the company’s data pipelines by introducing automation and standardization across core data engineering workflows. Thanks to our AI/ML team, the company was able to rapidly develop proof-of-concept ML-driven MarTech solutions while saving engineering costs.

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Industries

Digital media, Entertainment, Subscription

Services

Data engineering, AI engineering, Backend development, Modernization

Solutions

Machine learning models, Predictive analytics, Churn prediction, AI, Data pipeline, AI integration

Technologies

Python, SQL, AWS Sagemaker, AWS Batch, AWS ECS, AWS EMR, XGBoost, Bash, Airflow, Snowflake, Tableau

Outcomes
and highlights

  • 95% projected accuracy demonstrating that AI-driven churn prediction can significantly outperform traditional methods
  • Up to 5X potential cost savings unlocked through improved retention compared to the average cost of acquiring new subscribers

Solutions overview

Data Studio
AI/ML solutions overview

Data pipeline modernization

Our Data Studio experts’ work had a profound impact on the engineering workflows related to our client’s migration to a new technology stack. The version control and standardized process introduced by our team streamlined the client’s data engineering pipelines, automating manual tasks and speeding up issue detection and resolution. With smarter automation in place, the company has seen significant improvements in data engineering speed and reliability.

Key deliverables

  • Development of a robust and reliable data engineering solution for the client’s analytics teams
  • Migration of critical data pipelines to a new technology stack and architecture
  • Introduction of version control for the analytics projects, migrating and optimizing the data pipelines
  • Automation of end-to-end processes that include extraction from external data sources and execution of ad hoc Python scripts as batch jobs

Technologies

Python, SQL, Bash, Airflow, AWS Batch, AWS ECS, AWS EMR, Snowflake, Tableau

ML-driven churn detection

AgileEngine’s experts led the development of a PoC enabling ML-driven churn detection using gradient boosting. This solution enables product teams to define user cohorts with greater accuracy by predicting the likelihood that subscribers will leave the platform. The PoC demonstrated how ML could enhance retention strategies and move beyond heuristic-based decision-making.

Key deliverables

  • Churn forecasting PoC using predictive analytics (gradient boosting and logistic regression) to process historical data and estimate churn probability
  • Batch processing for AI/ML model training and analysis
  • Automated monitoring of ML predictions that enables the prevention of issues like bias, hallucinations, model drift, etc.

Technologies

AWS Sagemaker, AWS Batch, XGBoost

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