On-Demand Webinar

Telecom Data Analytics

Understanding Customer behaviour and driving targeted marketing campaigns using SAS Analytics


About the webinar

Why does the Telecom industry need Data Analytics? Telecoms are losing money due to a highly saturated customer base and competition from hundreds of players in the market.

In the face of such a battle, only those who have a competitive edge over others would thrive. To stop losing money, Telecom operators need to develop a strong customer proposition and marketing strategies by leveraging analytics. It is only logical as large quantities of data are a part of daily telecom operations and this data is going up every single day.

Using SAS Viya, we can implement complex machine learning and predictive techniques on the vast amount of data at a lower cost and efforts to:

  • Reduce customer attrition.
  • Understand customer behaviour.
  • Design targeted marketing campaigns.
  • Improve the quality of service
  • Increase overall revenue

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About the Experts

Dwiti Pandya

Dwiti is a student at UCD Michael Smurfit Business School pursuing MSc. in Business Analytics. She has done her bachelor’s in Computer Engineering.

She has worked with Persistent Systems Limited as a Software Developer for 2.5 years where she was involved in designing database schemas and developing applications to manage enterprise data.

By implementing various analytical projects, she has gained hands-on experience in a wide range of software and data management systems ranging from traditional options like Excel and MySQL to newer tools including SAS and Tableau as well as programming languages like Python which has enabled her to analyse data by creating models and reports to gain insights

Jonita Fernandes

Jonita is a student at UCD Michael Smurfit Business School pursuing MSc. in Business Analytics.

After completing her bachelor’s in computer engineering, she worked as a Senior Consultant at Mindcraft Software Pvt Ltd which included designing the databases for multiple applications, analysing huge datasets to develop insights and KPI’s for underwriters in the insurance industry. She has a penchant for understanding data and has worked on a wide range of data analysis tools such as SAS, Tableau and programming languages like R and Python.

She is currently focussed on using her industry experience and knowledge of analytics to aid organizations to understand their data better