Media & Entertainment - OTT Streaming Analytics & Content Personalization

Overview

A leading OTT (Over-The-Top) streaming media platform with 12+ million subscribers across web, mobile, smart TV, and gaming consoles struggled with viewer engagement, content discovery, and subscriber retention due to generic batch-processed recommendations, losing market share to competitors with superior AI-driven personalized experiences.

Key Challenges

• Generic collaborative filtering recommendations not capturing individual viewer nuanced content preferences and contextual behavior

• High subscriber churn rate (averaging 5.2% monthly) due to poor content discovery and inability to find relevant programming

• Batch-processed viewer analytics and engagement metrics providing insights days after viewing behavior occurred—too late for real-time intervention

• Content producers, studios, and licensing teams lacking real-time performance feedback on new releases and catalog content

• Unable to detect trending content, viral moments, and binge-watching patterns to capitalize on viewer momentum in real-time

• Fragmented viewer behavioral data across streaming web platform, iOS/Android mobile apps, Roku/Fire TV apps, and smart TV applications

• Limited AI/ML model sophistication unable to understand contextual viewing preferences (time of day, device type, viewing party size)

• Inability to predict which subscribers were at high risk of canceling subscriptions for proactive retention campaigns

• Poor content library utilization—80% of viewing concentrated on 20% of content catalog

• CDN (Content Delivery Network) performance issues impacting quality of experience (QoE) and streaming quality

Our Approach

• 1

  Built comprehensive Databricks Lakehouse platform as unified foundation for all viewer behavioral data and content metadata

• 2

  Implemented real-time streaming data pipelines capturing granular viewer interactions: plays, pauses, rewinds, fast-forwards, searches, and abandonment across all platforms

• 3

  Deployed hybrid AI/ML recommendation models combining collaborative filtering, content-based filtering, and deep learning for contextual personalization

• 4

  Created transformer-based deep learning models analyzing content metadata, thumbnail effectiveness, viewer sentiment, and engagement patterns

• 5

  Established comprehensive MLOps pipeline with MLflow for continuous model training, A/B testing, multi-armed bandit optimization, and champion/challenger evaluation

• 6

  Built advanced churn prediction models using survival analysis and gradient boosting identifying at-risk subscribers 30 days before cancellation

• 7

  Developed real-time content performance dashboards for producers, content strategists, and licensing teams with engagement KPIs

• 8

  Integrated social media sentiment analysis (Twitter, Instagram, TikTok) and external entertainment trend data for trending content detection

• 9

  Implemented hyper-personalized recommendation engine with contextual awareness (time of day, device, viewing history, mood inference)

• 10

  Created comprehensive viewer 360 profiles with preference modeling, viewing patterns, genre affinities, and predicted content interests

• 11

  Built CDN performance analytics correlating streaming quality metrics (bitrate, buffering, startup time) with viewer satisfaction and churn

Key Outcomes

• Increased content engagement rates by 85% through AI-powered hyper-personalized recommendations with contextual intelligence

• Reduced subscriber churn by 43% (from 5.2% to 3.0% monthly) with predictive retention campaigns and personalized re-engagement

• Improved average watch time per subscriber by 2.3x (from 8.2 to 18.9 hours monthly) through superior content discovery

• Enabled real-time content performance monitoring for producers and studios with <5 minute latency from viewing to insights

• Launched dynamic personalized home screen layouts unique to each viewer with individualized content carousels and thumbnails

• Achieved 92% recommendation click-through accuracy validated by viewer implicit and explicit feedback

• Detected trending content within minutes of viral social media moments and dynamically adjusted recommendation prominence

• Unified viewer behavioral data across 12 million+ active subscribers generating 50+ million viewing sessions daily

• Predicted viewer churn risk 30 days in advance with 87% precision enabling targeted retention offers and content recommendations

• Increased content library utilization by 67% distributing viewing across broader catalog and reducing dependence on blockbuster titles

• Improved Net Promoter Score (NPS) by 28 points through superior content discovery and streaming quality optimization