This Case Shows Specific Expertise. Find the Companies with the Skills Your Project Demands!

You're viewing one of tens of thousands of real cases compiled on Many.dev. Each case demonstrates specific, tangible expertise.

But how do you find the company that possesses the exact skills and experience needed for your project? Forget generic filters!

Our unique AI system allows you to describe your project in your own words and instantly get a list of companies that have already successfully applied that precise expertise in similar projects.

Create a free account to unlock powerful AI-powered search and connect with companies whose expertise directly matches your project's requirements.

Modernization of Data Pipelines and AI-Driven Churn Prediction System for Premium TV Network

agileengine.com

Media

Digital Media

Entertainment

Subscription Services

Challenges in Data Management and Subscriber Retention

The client faces inefficiencies in manual data engineering workflows, lack of standardized processes, and high subscriber churn rates. Traditional methods for churn prediction and retention strategies are insufficient, leading to increased costs and reduced profitability.

About the Client

A premium television network reaching 28 million US households, requiring advanced data systems and AI solutions to optimize subscriber retention and operational efficiency.

Key Goals for System Modernization and AI Integration

Modernize data pipelines with automation and standardization to improve engineering efficiency
Develop AI-driven churn prediction models to enhance retention strategies
Reduce operational costs through optimized data workflows and improved subscriber retention
Enable scalable, reliable, and secure data and AI systems for future growth

Core System Capabilities

Automated end-to-end data pipelines for ingestion, transformation, and analysis
Version-controlled analytics workflows with standardized processes
AI-driven churn prediction using gradient boosting and logistic regression models
Batch processing for model training and ad hoc Python script execution
Real-time monitoring of AI predictions for bias, drift, and accuracy

Technologies and Tools

Python

SQL

Bash

Apache Airflow

AWS Batch

AWS ECS

AWS EMR

AWS SageMaker

XGBoost

Snowflake

Tableau

System Integrations

External data sources (subscriber behavior, viewing patterns)
Existing analytics and reporting platforms (Tableau)
Cloud infrastructure (AWS) for scalable processing

Non-Functional Requirements

High scalability to handle growing data volumes
99.9% system reliability for critical pipelines
Data security and compliance with industry standards
Low-latency processing for real-time analytics
User-friendly interfaces for data engineers and analysts

Expected Business Outcomes

The project will deliver 95% accurate churn predictions, enabling proactive retention strategies that could reduce subscriber attrition by up to 5x compared to acquisition costs. Automated pipelines will cut engineering overhead by 40-60%, while standardized workflows will accelerate issue resolution and improve system reliability.