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, introducing automation and standardization to core data engineering workflows. Thanks to our AI/ML team, the company was able to rapidly develop proof-of-concept ML-driven MarTech while saving engineering costs.

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

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, eliminating 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

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, which enables the prevention of issues like bias, hallucinations, model drift, etc.