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Media-Machine Learning and AI
Media

Media

Use Cases

Data Ecosystem

Evolution of content consumption

Evolution of content consumption

Focal areas for Analytics

Focal areas for Analytics

Use Cases – Understanding The Audience

Understanding viewer preferences in terms of genre, time-of-viewing, loyalty to channel v/s loyalty to content, flipping behavior, paths to content discovery.

Understanding The Audience

Use Cases – Content Creation

Analytics of scripts, fan blogs to mine sentiments and emotions, model topics, character popularity.

Content Creation

Use Cases – Promotion Strategies

Analytics around same-channel, inter-channel promos, marketing efficiency, and effectiveness, market mix modeling.

Promotion Strategies

Use Cases – Impact on Social Media

Analysis of social network activity - likes, shares, tweets, posts - can lead to better understanding of opinion formation and influence.

Impact on Social Media

Deep Dive - Increasing viewership on the Movies channel

Objective

  • Reduce the # of negative surprises in the performance of movie – slot TVTs
  • Improve the likelihood of good performance by tier 2 movies
  • Recommendation Engine
  • Automate current reporting and tiering process
Objective

Data Considered

  • BARC TVT performance Ratings
  • Library Information
  • Break Placements
  • TV Promotions
  • Open Source – IMDB, Twitter etc.
Data Considered

Deep Dive – Viewership on Movies channel – Analytical Approach

Exploratory Analysis

  • Current State Assessments: Initial exploration of the data
  • Feature Engineering: Creation of Potential variables related to content, programing events, break placement, promos etc.
  • Detailed Exploratory analysis to check for patterns, trends, relations etc.
Exploratory Analysis
Driver Analysis and Creation of Business Rules

Driver Analysis and Creation of Business Rules

Using statistical techniques to

  • Identify important drivers/variables affecting movie performance
  • Create new tiering rules to classify movies into different tiers (may result in dynamic tiering)
  • Create new ranking rules to rate movies for a given timeslot on likelihood of performance

Recommendation Engine

Provide top 2 movie picks for every slot considering the following:

  • New Business rules
  • Constraints based on contracts
  • Other business constraints
Recommendation Engine

Automation of MIS Reporting

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