Media and Entertainment



To successfully compete for the fleeting attention of their customer base, content producers and publishers today need to personalize content. To tailor your offering based on the vast number of data sources ranging from event data (e.g. viewing behavior, search history) to social media and third party sources, you need a data platform that can help you analyze data holistically and develop advanced predictive models that increase engagement and customer lifetime value (LTV).

Common Use Cases

Product Recommendations

Use customer behavior data and profile information to predict next best offers and provide a more personalized experience to drive customer engagement and LTV.

Sentiment Analysis

Analyze how social, advertising, competitor moves, product launches or news stories 
affect the brand.

Pricing Optimization

Analyze demand and inventory data such as segmentation, attribution, and cost to determine prices and set discounts.

Making Innovative Use Cases a Reality

Data Sources

  • Image Data
  • Video Stream
  • Pixel Data
  • Adobe Data
  • Viewing Data
  • Survey Data
  • Social Media
  • CRM Data
  • Transactional Data


Content Personalization

  • Cohort Analysis
  • Web Analytics
  • Click-Path Optimization
  • Next Best Product Analysis
  • Customer Segmentation

Sentiment Analysis

  • Natural Language Processing
  • Social Media (Drive Marketing Strategy)
  • Reputation Management


  • Real-Time Content Streaming Recommendations
  • Offers and Coupons
  • Market Basket Analysis
  • Smart Search and Playlists

Predictive Analytics

  • Audience Targeting
  • Predicting Pricing
  • Performance Metrics For Conversion
  • Box-Office Revenue Projections


  • Analyze consumer behavior and feedback to drive more engaging content and revenue.
  • Improve demographic targeting to increase conversion rates and customer lifetime value.

Some of our Customers

Databricks takes the pain out of cluster management and puts the real power of these systems in the hands of those who need it most: developers, analyst, and data scientists are now freed up to think about business and technical problems.

Shaun Elliott, Technical Lead of Service Engineering,