Data analytics and machine learning in media & entertainment

Solution accelerators for media and entertainment

Based on best-practices from our work with the leading brands, we’ve developed solution accelerators for common analytics and machine learning use cases to save weeks or months of development time for your data engineers and data scientists.

Quality of service

Providing consistent quality in the streaming video experience is table stakes to keep fickle audiences with ample entertainment options to stay on your platform. This solution is a quick start for most streaming video platform environments to embed this Quality of Service real-time streaming analytics solution.

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Customer lifetime value

Understanding and identifying who your most valuable customers are will help guide better marketing investment and product development choices. This solution focuses on retention and spending components to then combine into an overall CLV model ideal for transaction-based business like TVOD or AVOD.

Subscriber churn prediction

Identify customer behaviors to predict increased risk of canceling their subscription using Kaplan-Meier curves and Cox Proportional Hazarxd models.

Behavioral segmentation

Create more advanced customer segments to drive better purchasing predictions based on behaviors. Using historical data from point of sales systems along with campaign information from promotions management systems, this solution helps teams derive a number of features that capture the behavior of various households with regards to promotions in order to build useful customer clusters.

Sales forecasting & advertising attribution

Easily merge advertising and sales data from a variety of sources to understand impact of individual marketing channels in driving sales at a geo level. Create visualizations and use ML to predict granular-level analyses of marketing tactics and messaging down to the customer segment level.


Create a personalized experience for your customers to drive engagement and monetization. Solution includes notebooks for collaborative filtering and content-based recommenders.

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