Big Data Analytics Tools and Solutions | HCLTech


Our data-driven strategies enable our clients to compete, innovate, and drive value.


Today, communication is essential for companies across the board in providing and feeding data for analysis. Big Data analytics tools are hugely effective in this context, creating more targeted offers, developing customizable consumption plans for end consumers, and enabling business strategies based on behavioural studies. What’s more, the explosion of devices and platforms has only meant a proliferation of the avenues utilized by customers for interactions, both with each other, and with the organization.

This implies the need to continuously replenish strategic approach, based on insights gleaned from numerous channels. Big Data analytics is also being deployed internally to evaluate performance metrics, fine-tune expansion strategies, and measure program effectiveness.

We are committed towards the effective implementation of Big Data Analytics tools across several mission-critical industries, helping our customers take informed operational decisions in the following areas: pricing, product bundling, campaigns, customer experience, churn, and customer management. Business capabilities that we have enabled includes:

  • Increased service reliability and prediction on component failure, thus reducing unscheduled maintenance
  • Cross-selling and up-selling of products and services
  • Better insights into customer behaviour and churn management
  • Improved sales analytics and visual dashboards for marketing campaigns
  • Classification of application sessions - web, multimedia and p2p for service differentiated charging


Telecom Operator Analytics

Assessing propensity to churn and spend

Customer Experience Analytics

Evaluating end user experience for telecom services

Product Bundle Analytics

Providing real-time information during purchase from multiple telecom operators

Choke Point Analytics

Identifying bottlenecks in telecom networks

Supply Chain Analytics

Assessing efficiencies across the chain for buying, distribution, and consumption patterns