How AI/ML Helps DSPs to Improve Day-to-Day Operational Efficiency

August 6, 2018

Telecom industry is facing a huge gap between customer experience demands and fulfillment. At one end, customer expectations are increasing for high service availability, low turnaround time, and better quality of service (QoS) while on the other end, DSPs’ current infrastructure and service delivery approach is unable to meet customer expectations. Artificial Intelligence/Machine Learning (AI/ML) technology, with its recent advancements is fast becoming the choice of DSPs to bridge this gap and improve operational efficiency. AI/ML systems together with Bigdata can process huge amounts of historical and real-time data from various systems such as CRM, billing systems, NMS/EMS, and product catalogue to provide actionable insights & predictions.

AI/ML can reinforce DSPs’ infrastructure and propel them towards the following next-generation services and experiences.

  • Intelligent Software-defined approach for operations and delivery of services (virtualization, self-healing and self-learning networks)
  • Automation of customer service and customer experience improvement (Chatbots, virtual assistants)
  • Predictive maintenance and agile operations (automated problem detection,
    troubleshooting, and optimization of networks)
  • Innovation in subscriber profiling, usage analysis, and personalized offers

This insight offers a list of AI/ML use cases to enhance DSPs’ key operational areas such as customer service, service assurance and network automation. It also discusses various machine learning algorithms that are suitable for each of those use cases.


Avaiarasi S.
Velmurugan M.
Vishwa Nigam

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