INDUSTRY: Media and entertainment
SOLUTION: Customer retention, revenue forecasting
TECHNICAL USE CASE: Data ingest and ETL, machine learning
Today’s consumers expect more from their content providers and can quickly tune out if expectations are not met. To ensure engagement and loyalty, Showtime wanted to leverage data to drive content strategy, but they struggled with scaling limitations of legacy systems and inefficient data pipelines. With Databricks unified data analytics platform, they now have an actionable view into the consumer journey to inform programming and content with the goal of increasing engagement while lowering churn.
Legacy Systems Slowed Time-to-Market of New Features
The Data Strategy team at Showtime is focused on democratizing data and analytics across the organization. They collect huge volumes of subscriber data (e.g. shows watched, time of day, devices used, subscription history, etc) and use machine learning to predict subscriber behavior and improve scheduling and programming. Unfortunately, legacy technology architectures were pulling teams away from high-value data science activities.
- Infrastructure complexity: Finding the infrastructure that allowed for flexibility but didn’t require constant maintenance.
- Inefficient Machine Learning Pipelines: The process to develop, train, and deploy machine learning models was highly manual and error-prone, leading to slower time-to-market of new models.
Smarter Content Programming with ML
The Databricks Unified Data Analytics Platform provides Showtime with a fully managed service that has greatly simplified data engineering and improved the productivity of their data science teams. Now they are able to tap into the insights within their rich pool of data to uncover opportunities to drive viewer engagement and reduce churn.
- Automated Infrastructure: Fully managed, serverless cloud infrastructure for speed, cost control and elasticity.
- Interactive Workspace: Make collaboration easy and seamless across teams and multiple programming languages to accelerate data science productivity.
- Simplified ML Lifecycle: MLflow allows them to streamline the entire ML lifecycle.
Faster Data Analytics, Data Science Innovation
Databricks a permis à Showtime de démocratiser l'accès aux données et au machine learning dans toute l'entreprise, orientant ainsi la culture sur les données.
- 6x Faster pipelines: Data pipelines that took over 24 hours are now run in less than 4 hours enabling teams to make decisions faster.
- Removing infrastructure complexity: Fully managed platform in the cloud with automated cluster management allows the data science team to focus on machine learning rather than hardware configurations, provisioning clusters, debugging, etc.
- Innovating the subscriber experience: Improved data science collaboration and productivity has reduced time-to-market for new models and features. Teams can experiment faster leading to a better, more personalized experience for subscribers.
