Telefónica improves network performance and automates customer service operations

Telefónica, a leading telecommunications provider in Spain, faced the challenge of managing vast amounts of network data, seeking more efficient network optimization and customer service automation. Collaborating with Microsoft, Telefónica deployed a big data analytics architecture leveraging Azure, Azure Data Explorer, and Azure Databricks. This enables real-time processing and analysis of network data and anomaly detection. The integration of Power BI facilitated advanced dashboards and business insights, and more than 1,000 digital workers were deployed using Blue Prism RPA integrated with Microsoft technology. This intelligent automation supports proactive issue identification and improved customer experience, reducing operational time and maintaining high service standards in the company's B2C operations. The implementation resulted in increased network reliability, streamlined operations, and a measurable reduction in manual intervention. The project highlights the collaboration between Telefónica and Microsoft to harness AI and automation to transform large-scale network operations.

Organization
Telefónica
Industry
Tech & Comms
Location
Spain

Reported outcomes

−10%

timeTime & speed

Strategic outcomes

New product / capabilityImproved network performance and reliabilitySpeed & agilityFaster issue resolution before customer impactCustomer experience & trustEnhanced customer experience and satisfactionSpeed & agilityAutomated customer care and operations

Catalog median for time & speed deployments: −60% across 728 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

  • 1AI-driven Network Optimization
  • 2Automated Customer Service with Digital Workers
  • 3Big Data Pipeline for Proactive Anomaly Detection
  • Vast amounts of network data generated daily complicated efficient analysis and network optimization.
  • Manual oversight limited the speed and accuracy of network issue identification.
  • Customer service operations required excessive manual intervention, impacting efficiency.
  • Need to improve proactive detection and resolution of network anomalies to minimize customer impact.
  • Deployed a big data analytics architecture with Azure, Azure Data Explorer, and Azure Databricks.
  • Used Azure Data Explorer for high-speed, scalable network data exploration.
  • Implemented Azure Databricks for AI-driven data processing and machine learning workloads.
  • Developed Power BI dashboards for advanced analytics and operational insights.
  • Integrated over 1,000 digital workers using Blue Prism RPA and Microsoft technologies to automate customer care and operations.
  • Operational time reduced by approximately 10% in B2C operations.
  • Improved network performance and reliability.
  • Proactive anomaly detection enabled faster issue resolution before customer impact.
  • Enhanced customer experience and satisfaction.
Architecture

Network data is ingested and stored in Azure and Azure Data Explorer, enabling scalable exploration and analytics. Azure Databricks is used for data processing and machine learning. Power BI is used for dashboards and reporting. Blue Prism RPA integrates with Microsoft services to automate customer care tasks, creating end-to-end digital worker flows.

Implementation partners1
Sources & evidence1
Groundedness: Unavailable

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