skip to main content
Close Icon We use cookies to improve your website experience.  To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy.  By continuing to use the website, you consent to our use of cookies.

Introduction

CSPs are keen to understand how their peers are successfully mining their volumes of data using artificial intelligence (AI) and big data analytics.

Highlights

  • Jio deployed Guavus’ AI-enabled analytics solutions to take advantage of real-time customer experience and predictive analytics to automate troubleshooting of the network and generate subscriber insights for use in marketing.
  • Key lessons learned to emphasize the role that leadership, partnership between vendor and client, and strong data infrastructure and pipeline can play in accelerating AI and big data analytics projects.

Features and Benefits

  • Provides in-depth view of Jio's implementation of Guavus' big data analytics solution.
  • Provides key lessons and recommendations for vendors and CSPs looking to deploy big data analytics to improve CX and address network management issues.

Key questions answered

  • How can an AI-based solution address CSPs' business challenges?
  • What lessons can CSPs learn from Jio's implementation of big data analytics solutions?

Table of contents

Summary

  • Catalyst
  • Omdia view
  • Key messages

Recommendations for the telecoms industry

  • Recommendations for CSPs
  • Recommendations for vendors

Using AI-based analytics to solve CSPs’ customer experience, network management and cost issues

  • Setting the business context
  • The role of AI and analytics in solving key telecoms operations challenges

Lessons learned

  • Vision, commitment and support are all critical
  • Acting quickly to meet project requirements
  • Be knowledgeable but also willing to learn
  • Providing business users with tools to transform analytics ideas into applications on their own
  • Having a dedicated team to provide data
  • Taking stepwise approach to developing analytics products
  • Openness and visibility between vendor and client

Appendix

  • Methodology
  • Further reading
  • Author