Case study

Performance Measurement Across Linear TV Networks of a Media Conglomerate

media conglomerate


A global media conglomerate.

Business Situation

The client wanted to automate the data extraction and analysis of its TV viewership data by building scheduled data pipelines. Also, the client was interested in an intuitive self-service analytics user interface that governed the data pipeline scheduling process and provided the client’s customer marketing team with drill-down filters to navigate the data.

Benefits & Outcomes of Our Engagement

This big data analytics solution has enabled the client to take quick actions such as:

  • Time-zone change for relevant shows and their comparative revenue from ad-sales.
  • Cannibalization impact of OTT (over-the-top) media such as Amazon and Hulu on certain linear TV programs.
  • Increasing content for certain show types on OTTs.

SGA Approach

Step 1: Data Tagging

  • SGA mapped and standardized the program names using fuzzy logic (machine learning algorithm) across the client’s linear and digital platforms.
  • This data-mart served as a one-stop database that could be used across deeper program-related analytics, forecasting, and insight generation. This included integrating data across Nielsen TV ratings, the client’s program schedule, and program meta data.

Step 2 : Data Extraction and Integration

  • SGA created ETL processes and API connections to extract and aggregate data-points from each of the data sources.
  • SGA defined schedulers for data refresh as well as fresh data pull across platforms by building data pipelines using PySpark.

Step 3: UI (Web Application Using d3.js) Development

  • SGA built a web interface/self-service analytics portal that provided client-defined data analysis filters on Top 10 shows per day, audience behavior tracking, advertisement frequency by shows, etc.

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