Telecom & Media

TechVantage has deep expertise in applying Analytics, Machine learning and AI to the viewership data in the broadcasting, media and entertainment industry. We strongly believe we can provide you with some data-driven state-of-the-art solutions to increase your revenue, optimize your costs and provide a great end user experience for your viewers. We also have helped Telecom companies in packaging products effectively.

Case studies

  • Predicting Program viewership
  • Customer Churn model
  • Operational Efficiency
  • Target Audience Identification and Channel Identification


The client is a Data aggregator for MSO’s in North America.


Problem Statement


Channel or Program rating is not sufficient for optimal ad pricing in targeted advertising. The client wanted to create quarter hour projections of viewership at the household level per network.




Viewership data of 6 months for over 7 million households were used as historical data.


Used IBM Netezza and AWS Redshift as a high-performance database.


Used proprietary analytics framework built on Python for prediction using Machine learning




Predicted viewership on average of 86% accuracy. Used predicted values as input for Media planning platform to determine ad pricing. This is expected to increase revenues for the MSO by around 15% in the first year




The client is a leading cable distribution & broadcasting player in the US.


Problem Statement


The client wanted to develop an early attrition indicator for their customer base so that targeted campaigns can be designed with customer retention standpoint.




Viewership Behavior, History of Customer’s Relationship with the Client like subscription behavior, Interaction with Customer help.


Payment Patterns were found to be influencing the customer’s decision to attrite.


Logistic Regression Model was built for customer base snapshot as of 2 years back.


keeping the attrition flag as the Dependent and the earlier mentioned factors as Independent factors.


The accuracy of the model was assessed by checking the model results against the 30% customer base (Control Group)




~13%(~1.5M Customers) of the existing customer base was found to be likely attrition candidates.


Upon deep dive analysis, the effect of attrition was found to be prominent in the urban areas especially New York, California, San Francisco etc.


Marketing Team was given the responsibility to design targeted campaigns to retain these customers.



Problem Statement


Data received from billions of data points each day aggregating to Peta Bytes and from multiple network vendors – Nokia, Ericsson, Lucent. Needed real-time analytics to improve operational efficiency and reduce the Time to Repair.




Built a Big data analytics framework using the Apache stack.


Kafka for stream processing, Hadoop HDFS for storage, Map/Reduce jobs, Apache Spark, Python for ML and Tableau for visualization.


Used proprietary analytics framework built on Python for prediction using Machine learning. 




Identified patterns from equipment data and characterized failure symptoms. Built a failure prevention model that predicts potential failures in advance with prescribed actions based on past experience. Average of 76% accuracy. This process improvement is expected to reduce operational costs by around 18% in the first year.


Problem Statement


Client wants to identify the target audience for a new show “Born This Way” aired on “A&E”. They also want to identify the right channels through which ad agencies could reach the target audience.




Create different micro-groups of subscribers who watch a particular show using demographic, viewership and interest data.


Use this profiling to identify the target audience along with the propensity to tune to that channel.


Analyze the viewership data for the customers to identify the right channels, day and time for running the targeted campaigns.




It was identified that Afro Americans living in Long Island who had 2-3 children in their house were most likely target group.


In order to ensure that maximum impressions from these households were obtained, it was identified that the ads must be pushed through the following networks TLC , VH1 , LIFE during 7 PM – 8 PM.

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